PAW 2019 Podcast Series

PAW 2019 Conference Series – Tony Ayaz – Gemini Data Inc.

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Tony Ayaz, CEO and Co-founder of Gemini Data Inc.

Find Tony Online:



Gemini Data Inc.


Tony, when did you start Gemini?


We started the company in 2015.


2015.  We’re in the Happy Market Research Podcast right now.  We’re at Predictive Analytics World and Marketing Analytics World and there’s lots of worlds in this particular conference. I think there’s like twelve.  Have you guys been to this conference before? 


This is actually our first time at this conference.  And for us, I think it’s a win because what we’re looking for is real users with real pain a little bit beyond the typical IT folks that we’re looking for.


Got it.  So, you’re based out of San Francisco.  You started the business in 2015; so, you’ve had some success obviously.  Tell me a little bit about what you guys do.


Sure.  At Gemini Data, we help our customers with digital transformation initiatives.  What I mean by that is we help customers achieve data availability. And data availability is a necessary requirement today if you’re really looking to do something significant or on digital transformation, AI, or ML initiatives.  And what we mean by that is that you have to access to data and you have to make that data available. And there’s a lot of talk about out-of-the-box machine learning solutions and things that are out there in the market. But the reality is that if you’re doing complex things and trying to run your business, you need data diversity, and you only get that through data availability.  And so, what we do is we leverage the customer’s existing investments in various, different data platforms: it could be in a CSV; it could be in a data lake. It doesn’t really matter to us. We have a method that we apply called Zero Copy Data Virtualization that actually takes your data that’s sourced without you to move or copy that data or do the complex ETL processes that we’ve all been used to for the past two to three decades, which just simply doesn’t scale with AI.    


Data diversity is a term I’ve never heard before, but it is my favorite one in this conference.  Diversity is something that we’ve… we’re becoming more and more aware, especially in the Bay Area, like Silicon Valley…  I’d say globally you’re seeing… The math is that if you have more diversity in your senior leadership team, then you have a better world view, which gives you an improved advantage in the marketplace, right?       




And what’s interesting is how you’re connecting that in with data.  It isn’t about a single… right? It’s about different types of data.  You mentioned CSV versus data lake, which are vastly different, like profoundly different.  Your system is able to ingest both of those? 


I wouldn’t say ingest, access those systems.


Got it


We don’t want you to move or copy the data, but we allow you to access it in a unified way.


OK, cool.  So that bypasses some PIII?


Yes, it bypasses it in the sense that you’re giving access to people that should have access to it.  So we follow the same protocols of data access they have as their role or authority would provide them.  But we take it a step further of looking into five years from now, Zero Trust Networks are going to be deployed, which is a new, let’s call it, security protocol or methodology, which basically changes things versus where we’re at today:  It’s the perimeter of defense, which I’m going to put firewalls around things; I’m going to give you access to things you should have; and then when you’re not an employee, for example, I take you off. Think of this as more of a real-time basis of how you should have access, when you should have access, right by you as a user using the system.  Nobody has to manually set things up for you. The machine kind of knows what you should have access to, what you shouldn’t. It protects you. And this is something that’s far more deeper and can evolve, but you can only do that by applying modern architectures that have been around less than five years, I would say, to go to this next level for security.      


That’s really interesting.  You’ve been in the industry a long time.  What do you see as some big trends both from things that have evolved relatively recently in the last two to three years and then where we’re going in the next two to three years?


If I may go even a little past two to three years…  So, in 2005 is where I like to start is when the evolution of Big Data started, right?  It was the dotcom crash, but then things were coming up. Big Data, grab all the data that’s going to solve world hunger.  It’s going be awesome.


I actually think I saw that tweet.


Yeah, probably.  Right. At the time, there was nothing wrong with that.  That’s what you had to do. There’s a whole bunch of data coming in.  Nobody knew how to collect it. So the idea was centralize all this data.  Just grab it. And then there was a lot of successful companies that came through. which one of I had the pleasure of being at.  It was called Splunk in the early days. We grabbed the data, brought it in, and centralized it, made it easy for people. Well, that was 2005, and at the same time, data lakes came out and the whole Dupe and Open Source.  Fast forward to 2013 or into current time, you’re dealing with data chaos. And what’s happening is now that everybody has actually collected everything you could imagine. I call it a messy filing cabinet. Imagine if you went to your filing cabinet and didn’t have proper files and you just shoved papers in there, every time you need to go look through the papers, you have to sift through one by one.  Now, think about the petabytes of data that’s out there.      




That strategy does not work.  If you’re just collecting it, you’re making it very had to access.  And so where we’re going tomorrow, meaning the industry, from an AI perspective is back to that point about AI needs data diversity, right?  You need to make sure that you’re looking at all different data. So, if somebody tells you to move your data somewhere else and port it here or put it in the cloud, they’re doing a dissatisfaction because we’re playing the same game again.  You’re moving that data again, waiting to get access to it, and what I think customers would need today, and if they’re thinking about AI, is I’ve made my investments but I need to make it easier to access. And the way we access that is we make it easy for you to apply standard [unclear] that’s been around for three decades, and you can use it across all these complex systems and bring the data together.  Whether it’s CSV, whether it’s in a database or a data lake, it doesn’t really matter; we’re giving a uniform way to access it.     


And then, it’s accessed and then is there also display and interact with on the other side of it?  


Absolutely.  So, we have our interface that you can look at the data; we integrate it with a graph database:  much like you can use LinkedIn to see your first- or second-degree contacts. Image if you could do that with your data.  So we bring the data together; we allow you to see the relationships, which that by itself provides a significant value to customers because 51% of data scientists dilemma is getting access to the right data set to apply machine learning.  And, if you’re an analytics person, you’re relying on IT way too much to get that access. So we provide that as an option. We have other analytics capabilities on top of that. But the other thing that we do that’s interesting is, if you’ve invested in a Tableau or a Looker or a Business Intelligence of your choice, we don’t want to disrupt the business user.  So, they want data diversity. So we actually can send that data into their BI tool of choice as well. 


So you’re really fitting like an API basically or this middle ware (I don’t know what the right framework is), but that allows a Rosetta Stone of sorts, right, where it’s able to then interpret that messy data structured and then…   


Yeah, think of it as a…  If you want to classify data management and data integration technologies that have been around for two, three decades, we’re now at a point that they’re trying to apply that towards AI, which basically means there’s a lot of consulting and ETL and time and preparation and people needed to do that.  With the amount of data that is being spit out and what you need to do with AI, that doesn’t scale. So, we’re bringing a modern approach from a cloud prospective how you can access data to source, not move it, and accelerate the analytics process. 


Oh, that’s huge, that’s huge.  That speed-to-insight is what’s king right now.  


Exactly.  And to your question about the industry, there’s been recent acquisitions with Tableau and everything that’s happened.  In our opinion, that’s kind of validated the need for the market. Now look, if I’m Sales Force and I have the large, diverse data sets and I need to integrate them together and bring Tableau together into that, that’s a fantastic purchase.  But what if you’re not ready to make that migration to the cloud? What if your data is on premise? What if you don’t want to move it around? And customers need to leverage those systems and bring that power to them. But, in reality, what also people have to think about is how am I going to make it easier for my business users, who are not technical, to get access to that data.  And that’s why we really rely on sequel or we make it a graph-interaction with the data so everybody can understand it. We all know the challenges of hiring technical talent.     


War on talent for like the last three years and getting bloodier.  It’s just mind-blowing what’s happening right now on that front.  




So, who’s your ideal customer?


Starting from the top, I would probably say a Chief Data Officer or anybody in that function or reporting within that group.  Below that, I would say Head of Analytics or business intelligence leaders. And then I would say a layer below that, it would be data engineers that are sometimes tasked with getting this and we can provide tremendous automation for those folks as well.


Got it.  Favorite customer story?


Favorite customer story I would say is in the health care industry.  I’m not at liberty to mention the customer but… 


We never are if it’s pharma or health care.  It’s always top secret.  


It’s a health care but the best quotes I heard from the Chief Technology Officer was that “Hey, you guys bring the best of both worlds to me.”  He goes, “I have my data governance people here that are always telling me how to protect the data and make sure that we don’t violate any compliance issues or things like that.  Then I have my Chief Research side that’s always looking at cutting-edge, innovative things. And they’re supposed to look at AI how to improve things. And you guys are bringing the best of both worlds.”  And what he meant by that was we’re moving in those data-sharing economy that let’s say you’re a researcher for cancer and you have a cure for some ailment of cancer; and I’m a numbers-cruncher, but I have all this data based on medical device data that when we applied this medication to that we could solve that problem; and a third party may be a health care provider trying to see how many patients are accessing that or could improve the lives of people or reduce insurance rates or whatever may be.  This is all stored in different areas. If we could actually share our data in the context that we had together, those are tremendous things that we can solve together. And that’s why I really like the health care side where we’re giving them access to so many different data sources that can have profound effects on the better good and health and other beings’ aspects of life that we can hopefully provide for our customers.      


That is super powerful.  I love that story. That just crystallizes the importance of the work that you guys are doing.  


Exactly.  Thank you.


If someone wants to get in contact with you or Gemini data, how would they do that?


You go to our website or contact  I’m always accessible, so  Yeah, it’s very easy to talk to us.


Yeah, perfect.  Tony, thanks so much for being on the Happy Market Research Podcast.


Thank you for having us.


Everybody else that’s listening to this show, please take the time to screenshot it.  This is my – sorry to the other guests – favorite episode so far. The data-sharing part I thought was really interesting that Tony brought up.  The data-sharing economy: that is such a powerful framework for us to start understanding how we’re going to make better decisions as we incorporate more data diversity in those.  So definitely take the time, screenshot this. Hope you tag us on Twitter, LinkedIn, whatever your social media platform of choice is. Thanks so much for all the support. Have a great rest of your day.

PAW 2019 Podcast Series

PAW 2019 Conference Series – Satish Pala – Indium Software

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Satish Pala, Senior Vice President, Digital at Indium Software.

Find Satish Online:



Indium Software


You’re listening to the Happy Market Research Podcast.  I’ve already screwed up this intro a couple of times. I apologize for that to my wonderful guests.  Right now, I’ve got Satish Pala, Indium Software. He is a market veteran, been in the industry for 20 years.  Welcome to the podcast.


Thanks.  Thank you so much.


We are live today at Predictive Analytics World and Marketing Insights World and all kinds of worlds.  I think there’s 12 shows. It’s a lot of shows. That’s right, yeah. From health care… Everybody’s using insights.  What do you see as one of the big trends in the industry?


So, one of the things that I see is people having a lot of data, people thinking a data analytics solution will help them but have no direction or a strategy towards these concerns of theirs.  So they are trying to do what I call a prototype-based analytics. So, they would want to spend some money, figure out what insight could come up with that amount of data they have in a shorter cycle, maybe a month or two.  And then they figure out how it is going to impact their business. And based on the business impact, they’re trying to spend more, invest more in the scheme of data analytics where they can get impact, a positive impact on their business.   So, for example, if a company who’s having products related to customer, consumer, for example, electronics and they see the trend in electronics buying has reduced, they would want to analyze this challenge. They would want to analyze why the trend is so in terms of how the consumer behavior is.  So they would want to take the data that they have and analyze the patterns of consumer behavior and identify how they could fix it or, I would say, how they could work around these challenges that they’re facing. Having analysis on data insights on data can give them a picture of what they need to do in the near future.     


So, Indium Software, you guys have been around awhile?


Yeah, we’ve been around for almost 20 years.  And, as you said, we are veterans in the IT services.  We deal with quality solutions, services. Primary, I would say the portfolio that we solve, is at once analytics; then we have Big Data analytics, where in we help customers do data engineering, where we bring in all the data into a warehouse or data lake.  We give them business intelligence solutions; we give them visualization solutions; we also offer descriptive analytics solutions; we also offer advanced analytics, predictive-modeling-type solutions. We also help them integrate these solutions into their web portals.  So, one of the key challenges we have noticed is that, while they have done their analytics model on a dashboard or a visualization layer, they are trying to figure out how do I input this into my day-to-day life or day-to-day operations. So that is something we really focus on and help the customers with.       


You know it used to be the case that data was far away from the person that needed it, and now it’s moving really close, even like integrating insights into the decisions that are the workflows of the practitioners that are inside of the brands.  


I don’t want to interfere in your questioning, but the thought you have is right.  People have identified analytical models; people have insights. But how do they operationalize these insights?  How do they put in the business…?


I love that term “operationalize.”


So, we normally have an analytical model life cycle that starts with data preparation, modeling, model management, and operationalization of analytics.  So, this is something that we’re very good at. We take the insights and put it into the business process so that the business can see the value out of this model.  So that is one of the challenges where each business is trying to see how my model helps me. OK, I invested so much in analytics, but how does it help me in regular day-to-day operations.  Is there an automatic, seamless way of identifying the solutions for these challenges I have? Can these models be running a business like a regular day-to-day work instead of some data scientist coming to me with a report?  So that is the key. And the seamless integration of modeling into business processes is called operationalization, which we are very good at.


That’s fantastic.  Do you have a favorite customer story?    


Oh, yeah.  So, I have a couple of stories, but let me focus on the one. So, let me focus on the one where we have helped them predict, I would say, anomalies on their production line or on production output.  We also helped them identify all the parts or the assembly line that they have, which can fail in a few weeks. For example, let me elaborate. This is a manufacturing company, and it manufactures a very niche product, which has advanced machines that are fabricating this product.  They have a challenge where they want to identify the anomalies in this product because the product is so niche, the quality needs to be optimum quality, right? So they wanted to analyze the data that is available with them to identify all the products or all the output of this assembly line that can be outside the specifications that they have.  So, we had helped them build an analytic model to detect the outliers, I would say. These are the anomalies. That is one, which helped them make the productivity higher, maybe make it, I would say, the assembly-production process was much more efficient than before.    


So they used that data in order to identify where the anomalies were taking place.  They could focus on improving it, whether it was like a lean approach or maybe a machine or…


There were various things that they could do.  They could identify problematic machines that were fabricating these devices.  Or they could identify the process, by which they are fabricating. They could identify maybe a change to the product itself.  Let’s say you’re getting ten out of a million products. Is it the right number? Should I make it zero defect, zero outliers. So, that is something that is very important for them to have zero outliers because it’s a very costly product and niche product.    


Yeah, for sure.  


The second thing that we have helped them with is these assembly lines or these machines that fabricate this product, they have various sensors on top of them.  So, these sensors generate events. And these events, for example… I’ll just give an example, right? So, one of the sensors is a temperature sensor, the other one probably related to luminosity.  So, the outcome is that they want to identify how safe is my assembly line, how safe is my assembly station. And they have data available from these sensors. So, there are thresholds set for this sensor data that   if the temperature goes above so much Fahrenheit or the luminosity is above so much levels, which is safe zone, they would want to flag it, saying that this particular assembly station could fail in two weeks or three weeks.  Would you like to do preventative maintenance so that it is safe and you don’t have an accident later? So it’s pretty advanced because we had to bring in the IoT data. So this sensor data generates IoT data.


Are you partnering with somebody to do the IoT?


No, we do it ourselves.  


It’s all yours, yep, right.  You have some kind of like sensor that you’ve installed on the…?  


OK, in this particular scenario, the sensors were already installed by them. 


OK, got it.


We helped them ingest the data from these sensors into a data platform, which we built.  And then, we developed an analytical model to identify outliers, the anomalies. We also built a predictive model to identify when a part can fail or when a station can fail.


So it’s predictive maintenance is what’s happening.


Predictive maintenance is the key.  Two things: one is identify the outliers; second is predictive maintenance, which is very important because it is better you perform preventative maintenance rather than reactive where you have to spend a lot of money.    


Yep, makes sense.


And this particular company built it for themselves.  Now that it’s working for them, they are productizing it, monetizing it, positioning it for their vendors.  So, that’s the nice story and we’re very proud of this because it really helped them improve their efficiency. 


Indium Software, if somebody wants to get in contact with you, how would they do that?


So, you could go right into the web portal and then you will see key-members profiles available there.  Or you could go to LinkedIn or you could go to social media, Twitter.  We’re available everywhere, and we will contact you right away.


Satish, thanks for being on the Happy Market Research Podcast.


No problem.  Thanks, it’s my pleasure.


If you enjoyed this episode, please take the time to screen capture, share it on social media.  Special thanks to Predictive Analytics World. Really appreciate you guys hosting us. Have a wonderful rest of your day.

PAW 2019 Podcast Series

PAW 2019 Conference Series – Ryohei Fujimaki – dotData

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Ryohei Fujimaki, Founder and CEO of dotData.

Find Ryohei Online:





My guest today is Ryohei with dotData.  Actually, you guys have the premier booth location on site at this year’s Predictive Analytics Conference.


That’s true.


Yeah, what do you guys think about the show so far?


Yeah, this show…  This is the first time to sponsor this show.  And the show is great: like a lot of relevant audiences.  And we have the keynote presentation for 20 minutes. It’s very well received.  A lot of data scientists are looking for new trend in this industry. So we really like it. 


Yeah, it’s pretty well attended.  I really like the layout of it. I feel like there’s a nice cross-pollination between the speaking events that are happening and then the exhibit floor.  It’s really well laid out. And the attendee list is pretty good.  


Yeah, that’s true, that’s true.  It’s a very good mixture of the technical audience and the business audience.  So, we really have the good conversation with a lot about 10 days in our booth.  


Yeah, that’s great.  And your booth, by the way, I think is spectacular.  It’s the perfect booth kind of as the entry point ‘cause it’s very welcoming and you feel like you just go sit down and have a nice conversation.      


And also, we have the Lunch and Learn Session to talk more about data science automation, particularly for Python users.  There are a lot of people standing, taking a lot of memos, and they are taking maybe thousands of pictures. So that’s a very, very good session we had.   


Oh, that’s great.  I’m sorry… I’ve been doing podcasts straight through like every 20 minutes or so.  So I haven’t been able to attend any of the content, unfortunately, which is very disappointing ‘cause it seems like it’d be interesting.  So, dotData, what do you guys do?


Yeah, so, dotData we are offering end-to-end data science automation.  And, basically, we are the first and only company who can automate end-to-end data science process from raw data through data on the feature engineering and machine learning in production.  In particular, dotData we have the very strong artificial intelligence technology that automates the feature engineering process. The feature engineering process was told it’s not possible to automate because that’s a black art of domain expert.  But we invented the really strong technology, our world is going to explore a lot of business hypotheses with automated expertise.     


So congratulations.  That is a very hard problem to solve.  Do you have a favorite customer story?


Yeah.  So, the most favorite story I have is our first customer, of course.  That was a very, very exciting moment. Actually, that is the project we kind of decided to launch the company.  And that was one of the top 15 banks in the world, a very, very huge bank. And they have the data science team. And their problem is, first of all, they have no sufficient data scientists.  It takes a very long time to complete a data science project: each project takes three to four months by a couple of data scientists. What we have achieved in that project was literally we just took out tons of huge table, huge data in the bank, and they applied dotData technology.  Just within a day, we delivered the outcomes to the customer. And the outcomes are even better than the result of the data science team. The customer was so impressed and so excited because that is really accelerating their process. It used to take months, but now they can complete a project in a couple of days.  It’s a huge acceleration 


Even with a better outcome.


Yeah, even a better outcome.


When you’re interacting with a customer, are you interacting with the data science team pretty heavily?  Do they see you as a partner? Or do they see you a little bit as a competitor?  


Uh, no, we are actually quite good partner because we are always telling them automation is not something to replace a data scientist, but it helps data science team in a couple ways.  First thing is acceleration. Just imagine they can run a data science project within a couple of days. Eventually, data science project is turnaround. They have to try a lot of different ideas and learning what works and what doesn’t work.  That turnaround agility is a key for succeeding in data science project. So that is one way we are going to help. Another way is what we call democratize data science. Experienced data scientists should very focus on high-impact project or technically challenging project.  But there are a lot of templated projects, standard, common projects that even non-data scientists can execute with our data science automation. So data scientists are not our competitors at all; rather, we are helping them be more efficient, more effective.   


So that’s your user.  I like your framework of this democratization.  It feels to me like more and more people are leveraging AI-based technology in order to make informed business decisions.  Your tool, obviously, is a great example of this. What other trends have you seen, having been in the industry for over a decade, what have you seen that’s evolved and where do you think the industry is going in the next three to five years?   


So, actually, for these couple of years to maybe two to three years, first of all:  automation of machine learning and data science is going to be a very, very big momentum because there’s a lack of data scientists, a lot of data science projects that eventually fail maybe because of data scientists, because domain expertise, because communication between business and data science.  There’s a lot of reasons. One the other hand, automation can address a lot of these issues. It’s not replacing data scientists, again, while it is going to address a lot of industry challenges. This is the first step. The second step we are seeing just imagine automation enables us to build a lot of machine-learning models very, very easily.  Today, we are talking about 10 models, 50 models but two to three years later, we are talking about 100 or even 1000 machine-learning models. What happens is operationalize this model, maintenance of this model: those are going to be a very big problem in the next three to five years. So this is another area we are working very hard.   


So the issue there is it’s hard to maintain all these disparate, niche models.


Yeah, because the value to build a model is getting lower and lower.


That’s very interesting.  Haven’t actually heard anybody articulate that point before but that’s fascinating.




Yeah, for sure.  If somebody wants to get in contact with you or sales at dotData, how would they do that?


Yeah, please visit us at or LinkedIn and please download our White Paper or look at the webinar and let us discuss in detail.


It’s been an honor having you on the Happy Market Research Podcast today.


Yeah, thank you very much.


And for all of you who are listening, please take the time to screen shot and share this content.  There’s a ton of value wrapped up into these types of conversations as you get just a microview of a major player inside of the analytics space.  I hope you enjoyed it, found value. Have a wonderful rest of your day.

PAW 2019 Podcast Series

PAW 2019 Conference Series – Mike Galvin – Metis

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Mike Galvin, Executive Director of Data Science Corporate Training at Metis.

Find Mike Online:





Hi, I’m Jamin, and you’re listening to the Happy Market Research Podcast.  We are live today at Predictive Analytics World. My last guest at the show is Mike at Metis.  Tell me a little bit about the company.   


Sure, so, as you know, that in analytics, data science talent…  There’s a huge gap and there’s a large demand for it. So, that’s where we come into play.  We’re a data science and analytics training company. We’re part of Kaplan. So, if you’ve heard of Kaplan, the global education company…  We’re about six years old, launched organically, and we work with companies to upskill their staff, both technically and non-technically in kind of all things data science and analytics (data literacy, tools, machine learning).    


That’s awesome.  I actually think data science is the No. 1 job right now, nationally; I’m not sure if it’s global but certainly in the U.S.  And there’s a big gap in terms of the need, the desire to hire from big companies and small companies, for that matter, and the available workforce.  Sounds like you guys are playing a big part in, after people are hired, that subsequent improvements and ongoing skills training.  


That’s part of what we do.  There’s a little bit more. So, we have an accredited boot- camp that’s twelve weeks long.  We have it in New York, San Francisco, Seattle, and Chicago. That is a retail consumer product for people who want to shift into or pivot their careers into data science roles.


How is that helping?


So, that helps with the data acquisition, intel acquisition pipeline at the entry level.  Then, there’s the corporate training business, which is where I work. Within the corporate training business, we work with companies who not only upskill their existing tech talent in data sciences and in new areas and new tools and things like that but also their broader workforce; sometimes, even not technical in C-suite all the way down to individual contributors to build that literacy and fluency so that they can interact and collaborate with the data science teams more.    


Oh, that’s very cool, very cool.  Do you uh… On the engagement side of things, do you guys also have placement, help companies with placement or job candidate as you’re doing…?  It seems like there’s that middle piece between people want to pivot their careers, right; so, you’re training at the data camps, etc. And then, all of a sudden, there’s like the need, which you’re training people internally, right; so, the space in the middle is, “I want to hire.”     


So, not directly but indirectly.  So on the bootcamp side, part of that is getting people jobs.  So we have an entire career support team. 


Oh, you do then.




OK, got it.


To get people into actual data science jobs.  And over the past five-and-a-half, six years, we developed a huge network of hiring partners that we work with, and this ranges from Apple and Facebook to IBM and Ooze and all the way down to smaller companies as well, depending on who it is.  We started with the bootcamp, but that hiring network is really how we kind of started getting to the data science corporate training space ‘cause we started talking to them and realized, “Hey, there’s not only this entry-level hiring partner…”  


See, I think that’s really important because you’re offering really the whole product for the corporation, solving three distinct problems for them in that framework.  And that’s a really powerful, awesome place to be able to sit, which again because of the sheer value and the network effect that you have because, obviously, you have the pivot people or whatever (the trainees, if you will) and then ongoing training inside of their corporate experience.  So that sounds like a very compelling product.   


It’s an entire end-to-end journey really.


Exactly, end-to-end.  Right, totally, or end-to open end…


End-to open end.  That’s right. I like that.


So, tell me a little bit.  Do you have a favorite customer story? 


Oh, wow, there’s so many.


Every customer story is a favorite, but you have to pick one.


Sure, so, I’ll just give you a recent example.  So, we were working with a consulting company that works mostly in government and telecom.  And one of their key issues was they’re primarily Excel users and some of the problems they were encountering required a little more advanced analytics but also, they had really large data sets that Excel couldn’t handle anymore.  And so, that’s where we came in. And we put together a curriculum for them to train their consultants and principals and everyone kind of within the organization in Python to help add to their Excel workflow; and delivered the training a few months ago; and got some results out recently.  And they have a 22.5-times increase in speed of developing… doing their analyses.  


That’s unbelievable.


Yeah, that’s pretty cool because I always love to hear the impact that the training actually has because sometimes it’s hard to connect the dots to ROI.  And in a case like that, it’s really apparent.


So, now, are you actually creating customized curriculum per customer or is it more black box?


It’s a little bit of both.  So, we do have more what I would call off-the-shelf courses that we deliver, and it’s constantly evolving based on demands we’re seeing in the market.  But we also work with companies to develop more custom, bespoke products depending on their needs.  


Got it.


A lot of times we re-purpose what we have but contextualize it to their particular use cases.  So to give you one example: Working with a client, who is a large Fortune 500 and financial institution, and they have a talent-acquisition pipeline problem; they want to create data scientists.  So, what they’re doing is hiring STEM graduates right out of college and putting them through a twelve-week-long, on-boarding program. Now, we can re-purpose kind of our off-the-shelf, bootcamp curriculum for that, but what’s really important to a company like the one we’re working with is can we integrate in their use cases, their data sets.  Some of their tools have some of their data scientists and machine learning engineers come in and contextualize it so they get a better flavor of the type of work that actually may be doing once they roll into their full-time positions.    


That’s awesome.  That is a great story.  What do you think about the show?


It’s great.  First time at Predictive Analytics World.  So, so far, so good. Talking with a lot of great people, had a lot of great conversations.  Pretty diverse crowd, which I love. I just gave a Lunch-and-Learn talk: (It went well) “Building Organizational Competencies for Data Science.”


I’m sorry I missed that.  It sounds very interesting.  They got me tethered to the booth here. So…


I’ll let it slide this time.   


Thank you, thank you.  


Had a great turnout.  Lots of engagement. So it was an overall good session, and I’ve had a great time at the conference so far.  


Good.  Hopefully, it’s a lot of good leads.


Well, fingers crossed.


Yeah, yeah.


Only time will tell.


That’s the truth of it.  That’s the truth of it. If somebody wants to get in contact with you, how would they do that?  


Sure, so, one is our website:  Thisismetis M – E – T- I –  You can kind of explore the website and go from there.  If they want to reach out to me about corporate training, you can reach me at, and then my email is  


Michael, thank you for being on the Happy Market Research Podcast.


Great, thank you.


Everybody else, I hope you found a ton of value.  I certainly did in this episode. If you please take time, screen share, distribute it on social media.  I would really appreciate it. Thank you, all. Have a wonderful rest of your day. 

PAW 2019 Podcast Series

PAW 2019 Conference Series – Matt Cowell – QuantHub

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Matt Cowell, CEO of QuantHub.

Find Matt Online:





Hi, I’m Jamin.  You’re listening to the Happy Market Research Podcast.  My guest today is Matt Cowell, CEO of QuantHub. Matt, I’m really excited about this conversation we’re going to have.


Yeah, yeah.  Thanks for having me on.  I appreciate it.


So, you guys have a super interesting product concept or business that I am thrilled to dive into.  It looks like (now, keep in mind I haven’t spoken to anybody but just kind of walking to the booth and looking at your video) it looks like you are a… almost fit inside of the HR layer for data scientists.  Is that kind of…? 


Yeah, that’s correct.  Really data scientists, data engineers, data analysts, all advanced analytics professionals. 


Got it, got it.  Well, I should be quiet.  Why don’t you tell us a little bit about your business.


We actually help companies that are hiring in that area, whether it’s data science or data engineering.  These roles are really popular right now; they’re really hot. Everyone wants to be in this field. And so, what’s happening in the industry is when people post a position, they’re getting a ton of candidates.  And so, we found that people are spending just an inordinate amount of time doing the technical screening for those candidates. So they’re taking their most valuable resources and they’re having them do interviews all day long.  


Which is insane.


Yeah, which is crazy, right?  So, there’s a shortage of these people.  And the ones that they do have are actually spending all day interviewing.  And so, we have this problem ourselves. We’re actually a spin-out of a data science consulting company.  And so we built a platform to do…  


I don’t know what’s happening behind you but…  It’s like Lord of the Rings


It’s like I’m dropping the mic right now.  I’ve got sound effects on the podcast. It’s incredible.  And so…  


Wait, wait for it, wait for it.  My God, what is happening in there?


Incredible timing, incredible timing.  


Literally, the whole time this has never happened.


This is amazing.




OK.  And so, we actually built a skill testing and data challenge platform for these roles.  In the interview process, what would happen is you would actually get candidates that would probably come in by the hundreds because the job’s very hot.  And then, you would send them some sort of skill test in machine learning or data wrangling, data exploration or R, Python, you know, all the kind of relevant skills, depending on the job; and use that to sort of screen out people.  And then you might also send data challenges that are more hands-on.     


So, those exercises, are they done remote or are they done on-site? 


Yeah, they’re web-based.  They’re all web-based exercises.  They’re all graded automatically. And so, it takes about two minutes for a HR person or a tech lead to send out these challenges.  And then, they just start getting scores in from candidates.


I think it’s fascinating.  And it looked like there’s a score card of your overall quality of talent or talent quality, sorry.  That I thought was really interesting because it identifies potential weak spots that you need to create a bridge for from a training perspective.


Right.  So, you’ll see companies out there that do assessments like we do.  And so, I think what’s different about us is we don’t just do that for hiring.  We want to apply that to the entire really talent process. So, bringing on people, of course, that’s a normal use case.  People use assessments, but also to your existing team. We want to help benchmark the skills of your team, help people figure out sort of where they want to go in their careers and actually that sort of skill gap that they have to get there, and then align them with training.  So, if you’re a leader of a team, you could see that sort of skill graph and see where you’re deficient. You can work with your individual team members and figure out where they want to go and then what skills they need to get there and then align them with training. The problem we’re trying to solve there is there’s so much training; there’s so many companies now focused on training in this field that it’s a little overwhelming.  People don’t know where to start. And so, what we’re trying to do is help them actually make it much more targeted. And so, I don’t want to go to an intro to data science course. Maybe I just need to learn NLP. And so, let’s actually determine that through assessments and benchmarking.    


What’s interesting is at scale, all of a sudden, you can be like…  Part of the meta here is you could become the greater of the data science camps that are proliferating everywhere, right?  You could say these guys are generating… You know this is their score. 


It’s a normal evolution of content.  So, if you think about a company like Trip Advisor, why do they exist?  They exist because there’s so much content out there that someone needed to come in and aggregate and point people in the right direction.  And I feel like that’s exactly where we’re going to fit. There’s so much training content someone needs to come in as an aggregator and point people in the right direction.  And that’s kind of where we’ll fit.    


That’s a fun thing too from an SEO perspective.  You really have an opportunity to differentiate yourself and then jump materially in the rankings, which isn’t bad either.  


That’s the hope.


That’s right.  That’s interesting.  So, you’ve been part of the industry for a while.


Yeah, I’ve been in product and tech for a long time and a little bit in analytics.  So, in the last couple of years now, I’ve been exclusively focused on the analytics space, which has been fun ‘cause it’s pretty hot.    


Yeah, I love it.  When you look forward in the next three to five years, what do you see as a trend?


Yeah, I think people get worried about jobs being automated and gotten rid of and displaced, I should say, by automation.  What I think is interesting about this field is the role of data scientist is not something that is easily automated. You know there’s all these companies, even here at this conference, that are doing automated-type model-building and whatnot, but the actual data science part of it is pretty tough.  That’s pretty tough to automate. So I think that will continue to evolve and become more and more something that companies just have to invest in. So I think we’re at a great point here where all companies are doing something in this area. And so, for us, I feel like we’re at a good place in the industry as it continues to evolve and people invest more and more in this.  So I feel like that’s an interesting… It’s an interesting time.


Observationally, it feels a lot like kind of the web as the web started to scale up:  kind of mid-Yahoo. I’m so old I have that point of reference, right?


Me too.


Which is exciting for me because you can really see how this thing is going to J-curve.


Yeah, yeah, definitely.


Everybody knows they need to do this:  employ ML or AI, etc. into their overall business processes and decision-making.  But they’re not exactly sure how or where, but at the same time they’re willing to sequester big pieces of corporate budgets in order to solve that problem.  So it’s a nice land-grab opportunity. To your earlier point, the ones that are adding value like the data camps or whatever and creating great outcomes for the businesses, I think there’s mega opportunities over the next five years.   


Yeah, another thing, I think, is happening or will happen is my background is more on the product and tech side within software development.  I gave a talk yesterday that there are things that we do in that field: embracing product management, agile and lean concepts that, for some reason, we’re not yet really embracing as much in the analytics space.  So I think that will continue to evolve. We’ll learn from our sister fields and I think that’s going to help us deliver better outcomes. I think right now we’re all excited about the cool tech but not always delivering on the outcome.  So I think we need to align better with the business strategy, which is not straightforward to do, but there are roles out there that – product management being one of them – that, if we embrace that, I think we’ll have a better shot.     


Yeah, totally.  One of the CEO’s that I’ve interviewed had this term “data diversity.”  And I thought that was creating better outcomes for businesses. That’s not like single source; it’s looking across the organization in the market in order to find out whether it’s consumer data or employee data or what have you.  So the more you that you can create like benchmarks, third-party validations and then internal… You know start triangulating this point of truth. 


Yeah, yeah, that makes sense.


Uh, what do you think about the show?


I think it’s good.  This is our first time out here at this show in Vegas, and it’s been good.  What’s interesting, as an exhibitor, is that there’s no traffic when sessions are going on, which actually must mean that it’s really good content.  Normally, people don’t go to all the sessions like this. So it’s like a ghost town here during sessions.   


Yeah, it really is.


Because of all the special effects behind me that you just heard.


I think they just did that for you.  That’s hilarious.  


But it’s been good.


It’s been good.  I like how they’ve laid it out.  I think there’s lots of good cross-pollination in between the speaking sessions.  And the food’s been pretty good. 


The food is exceptional.  I go to a lot of these shows, and the food here is exceptional.   


Me too.


Yeah, I’m going to need to work out when I get home.


I know.  


So, if somebody wants to get in contact with you or sales at QuantHub, how would they do that?


Well, you can go to and we’re offering a free trial.  You can email, and we’ll be right back in touch.  And so, easy to get in touch with us.  And it’s super easy to get started. We work with companies that are testing and assessing 5,000 people a month and startup companies that are doing 5 a month.  


So you got a nice spectrum there.


It’s been a little interesting how it’s played out that way, but it’s really easy to get started.  If you’re not sure how many candidates coming in a month, then you could still get started really inexpensively.  


Do you guys also, you know…  Thinking about it… I imagine you’re having some visibility where the prospects, candidates are getting sourced from.  Do you see that as something that as a service or auxiliary partnership?   


Yeah, so for us, actually that is an area that we’re just now starting to get into.  We’re doing public challenges, “public” meaning in the data science and the data engineering community.  And so, what we want to build up is actually a community of people that go through these challenges and actually vet their skills and help them benchmark where they are but then also align them with people, companies that are looking to hire people like them.  And so, I think that’ll be an interesting opportunity. Obviously, we’re not doing that with the database of people coming from a company but, if we’re doing that with public challenges and whatnot, then we can help on the sourcing side, which is a pain point that companies have as well.  They’re not able to find all the best people. And so, we want to help with sourcing and vetting. Right now, we’re more on the vetting end and development; we also want to help on the sourcing side.  


Yeah, makes a lot of sense actually.  I think more and more I’m seeing single source being a direction that businesses are going to right now.  So the more you can offer the complete package, the simpler their lives are.


Absolutely, yeah, without a doubt.  We’re very niche-focused; we exclusively focused in advanced analytics.  We think this field has neat characteristics and there’s plenty of room to run here.  And so, we’re not planning on getting out into other spaces. We want to get in this space and know it really well and be excellent at this.  We came out of a data science consulting company; so, this is sort of in our DNA.




And we think this role is different:  This is not a programming role. A lot of companies we’ve seen treat it like a programming role and really miss the statistics side and the modeling side.  And so, we really come at it from that perspective more than anything else.  


It’s got to be refreshing from the customer point of view.


Yeah, yeah, we’ve had customer actually displace others that were more programmer-based assessments.  We test on programming, of course, but displace those companies because we were more statistically oriented.


Right, exactly.  I think historically that’s been overlooked, but I do think that’s getting rectified right now from a priority perspective.  


Yeah, yeah, definitely, definitely.


So,  My guest today has been Matt, the CEO.  Matt, thanks for joining me on the Happy Market Research Podcast.  


Yeah, it’s been a pleasure.  I appreciate it.


Everybody else, if you found value in this episode, please take the time to screen shot it, share it on social media.  I really appreciate it, and I hope you have a great rest of your day.

PAW 2019 Podcast Series

PAW 2019 Conference Series – Mark Do Couto – Altair

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Mark Do Couto, VP of Worldwide Sales, Data Intelligence at Altair.

Find Mark Online:





My guest today is Mark Do Couto, Altair.  Mark, what do you think about the show so far?  


It’s a great show.  It seems like there’s a good turnout.  Lot of great, interesting customers here.  It’s always good to see a lot of traffic and fellow vendors that are out here supporting.


I really like how they laid everything out.  This is my first time at this particular event, and I feel like they’ve really nailed the cross-pollination between the attendees and the exhibit hall.


Yeah, it’s really cool that some of the speaking engagements and stages are kind of opened into the exhibit hall; so, it makes a more natural flow.  


Yeah, totally.  So, Altair – got it, right – is a recent acquisition or acquirer.  Tell me a little bit about the core business. 


Yeah, so, Altair, as a company, is traditionally a manufacturing and engineering company.  Focus on design, lot of time spent in the automotive, aerospace. But they really want to look into data and really get into the data space.  So Altair acquired Datawatch in December of last year. And Datawatch has a full suite of products, which goes straight from data preparation to predictive analytics to our own visualization software.  And most of our customers are based in financial services, which is completely outside of what Altair focuses on, which is a reason they were really excited. They were excited for us to stay in the market that we’ve been playing in, but also see how we can bring this into kind of the traditional manufacturing and engineering space.         


Oh, that’s interesting.  So it sounds it’s 0like a value-add to their existing, their core business but then maybe moving into another market?  


Exactly.  So they, basically, have now acquired a company that already has customer and share in another market, but we’re having really interesting conversations with their traditional customer base:  a lot of helping predict machine failure, doing predictive maintenance on the manufacturing floor. So some really interesting use cases are coming out of their traditional customer base.   


That is actually really interesting.  Yeah, the downtime on, you know, if a cog goes out on a machine is catastrophic from an ROI perspective, right?




So being able to predict when machines are going to need maintenance and that kind of thing is actually a big industry that a lot of people don’t know about.


Even some of the major automotive manufacturers that Altair traditionally works with, they have like trucking fleets.  So, if a truck is out on the road and it goes down, that delay can cost a lot too; so, we’re looking at ways that we can help predict when maintenance and support’s going to be needed on those trucks and get it done before it goes out on a cross-country haul.  


So, tell me about who’s your ideal customer?  Not, not..I mean Altair, obviously, is kind of this big but more in line with your core. 


Yeah, absolutely.  


Datawatch or before.


So, the core of the business is really around how you’re leveraging the data that you have, but how you’re doing it in a way that you don’t need to be traditional data scientist.  So, really, it’s what Gartner would call your “citizen data scientist.” So, it’s a visual space to leverage data, build predictive models, and then automatically export that into a BI tool or even automatically generate code from that so that you can plug it into a deployment engine.  So our customer base is really people that are looking to leverage data science without having to get a Ph.D. in it. So it’s more of the “citizen data scientist,” but, in the same breath, if people who do have experience win R and Python, you can leverage that within our tool as well. So you can do some of that coding stuff if there are people on-team that want to do it.  But it’s all about keeping everything transparent. We could automate a lot of things if we wanted to, but we like to show all the different steps because that allows for anyone in auditing and governance to be able to see everything that was done, full transparency and then take it from there.   


That’s actually really interesting about the need to not build automation, right?  The not building automation actually adds a layer of value because you have an audit function that can pay attention to those steps. 


Yeah, our traditional core business was always around financial services; so, we work with all of the major banks, the user software to some extent.  And for them, that was key. That whole black box solution or magic button just didn’t work for them. They really need to see the steps that are taken right from where the data source was taken in from to where it was deployed and gone live.  So we’ve tried to keep everything transparent as possible so all those steps can be seen. There are parts that we optimize. So we do a lot in terms of optimization to make the performance a lot quicker, but our customers seem to really like that.  


Oh, that’s really interesting.  Since you’re in financial services, BII got to be a big part of the whole value prop.


Yep, absolutely.  It’s one of the reasons why we ensure that our software can be run on premise within their environment.  We do have a cloud-based solution that we can provide but, at the end of the day, we let our customers choose how they want they want to deploy the software, and we meet all the security protocols to ensure that their data is safe.  


How long have you been with the company?


Been with the company just over seven years now.  


So quite a while.


Yeah, absolutely.


Yeah, no kidding.  So you’ve seen a lot of…  I mean this whole democratization of access and data science is, from my vantage point…  It used to be really hard to run statistical models. I did it back in the 90s when I first started my career.  And it was a pain in the ass. I mean there was a lot to it. 


Well, I think the advent of data growth, like the exponential growth of data, is leading us down a path of…  There’s this concept of sampling data and taking a small portion of it and building models off that and then applying that on the larger portion.  The problem with that is, if you have so much data, so many customers, how do you know that you’re properly sampling. So it’s one of the reasons why we’ve developed a piece of our software that runs native in Spark.  So, if a customer has a Hadoop setup, you can run this within Spark, leveraging all of Spark’s technology and actually build predictive models against billions and billions of records and not have to wait one or two days to process all that.    


That is crazy, right?  The volume of data that we’re dealing with now versus even five years ago is just so…  And you kind of look forward… I was talking to another guest before and he was talking about how intelligence is starting to get built into the product spec at the very beginning of the inception of the new product, right?  So, opposed to being this like more, “Oh, Gosh, now we need to pay attention,” it’s like built into… So now you think about the explosion that that’s going to have because it’s going to open up a whole bunch of streams of data that we otherwise, haven’t been able to access.     


And to that point, it’s how are we looking at that and figuring out the best way to leverage that?  If we continue to leverage data the way we’ve been doing it for the past 10, 15 years, we’re going to left behind.  If the advent of collecting data, streaming data, bringing that data together, we need to jump on that train and get on that path and make sure that we can now analyze our data in the same breath as where the data’s being collected.  And I think that’s what we’re trying to do with this Spark kind of innovation that we’ve put together.


Are you more multi-sourced data inputs, meaning not.. maybe it’s market; maybe it’s internal customer.  I guess in a broad wrapper it’s BI, but customer voice, that kind of stuff. Are you seeing that?  


Yeah, everyone wants to create the ideal data lake or kind of the one point for data, but I can’t think of anyone of our customers that has data just residing in one data source.  Multiple data sources is the common thread. The question becomes how do you manage those multiple data sources. One of the components of our software is a browser-based data preparation tool that allows you to access all those data sources and be able to create your own data sets within this browser and actually share that with your team.  It gives you that collaborative format where you can actually see a team member that’s put a data set together and be able to leverage that data set throughout, maybe, a predictive model, for example. 


Got it.  So, being a veteran in the industry, looking forward what do you think is going to be different in the next three years?


That’s a great question.  The advent of machine learning and AI, depending on who you talk to, those terminologies could be marketing spin or whatever the case may be.  I think the biggest change that we’ll see is, as data continues to exponentially grow, there’s going to be new technologies that are going to be put in place to help leverage that data as much as possible.  And it’s how we’re leveraging access to that data and getting those responses as quickly as possible. At the end of the day, everyone wants to do something real time but, if data is becoming exponentially bigger, that real-time response is going to get slower.  So, there’s going to be a technology, I believe, that’s going to come out to be able to address this, and it’ll be kind of the next wave of what Hadoop was a couple years ago.   


That’s super interesting.  That’ll be fun to watch.


Yeah, this is a great space; it has been a great space for years.  


So, if somebody wants to get in contact with you ‘cause they feel like they have a project or question about your business, how would they do that?


Yeah, they can just go to  I mean they could email me directly if they’d like.  My email is and I’m happy to answer any questions that come up or get any of my team members to support.


Mark, thanks so much for being on the Happy Market Research Podcast.


Not a problem.  Thank you very much.


Hey, everybody else, if you please take the time to screen capture this, share in on your social media.  If you found any value at all, I’d really appreciate you taking that effort. I hope you have a fantastic rest of your day.  And special thanks to Predictive Analytics World, Marketing Analytics World, all the worlds wrapped up into one. You guys are awesome.  Thanks for hosting.  

PAW 2019 Podcast Series

PAW 2019 Conference Series – Lawrence Cowan – Cicero Group

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Lawrence Cowan, Senior Partner and COO of Cicero Group.

Find Lawrence Online:



Cicero Group


Hi, I’m Jamin.  You’re listening to the Happy Market Research Podcast.  My guest today is Lawrence. He is a partner at Cicero Group.  We are live at Predictive Analytics World, Marketing Analytics World.  There’s eight worlds. I forget all the worlds, but we’re covering a gamut of health care, etc. etc.  Welcome to the show, Lawrence.


Thanks, appreciate it.  Thanks for having me.


Yeah, of course.  You guys are exhibiting here.


We are.


What do you think about the show?


It’s been a great experience so far.  I’ve been to three or four of these, and they get larger every year.  I think the organizers do a great job putting on the event. And the topics continue to expand as well.  So far, our interactions with the audience have been very engaging: great questions, hard questions. But we’ve enjoyed it so far.  


That’s great.  So, tell me a little bit about Cicero group.  What do you guys do?


You bet.  So, Cicero Group is a full-service management consulting company.  We emphasize in data analytics and data strategy. Our roots were actually in market research.  And so, we identified the value of data early on in the strategic projects we were working on. And so, we leveraged primary market research in a lot of our work and have continued to expand that into more strategic transformation strategy, advanced analytics work as well.


That’s great.  One of the terms I’ve been hearing recently at this conference (I’ve never heard it before) is data diversity.  And I think that you’re hitting on that exact point, right? It’s about the primary data that’s collected and then really trying to triangulate truth even though sometimes it’s a lot more than three points.


It really is.  Yeah, so, bringing new data to the table is critical in all the projects we work on.  Primary research happens to be a big one but, again, a lot of the topics people are talking about at the conference today are other sources of data:  secondary data, partner data. There’s so many opportunities with leveraging data these days.    


Got it.  Talk to me about who an ideal customer is.


That’s a great question.  So, we serve a broad spectrum of industries and business functions.  I think our sweet spot, I would say, is more in the sales and marketing and insights and analytics functions within organizations.  And if you look at the services we’re offering, I think there’s a lot of value being provided to organizations that have RN-subscription-type businesses whether that’s SAS or even in B-to-C subscription-type businesses because our expertise is really in understanding the customer journey and finding those opportunities across the customer journey to improve that experience.


Do you have a favorite customer story?


That’s a great question.  There’s a lot, but one that comes to mind…  This one is actually from a few years back. One of our large clients a few years back was Groupon.  This was pre-IPO days.


I actually worked with them pre-IPO days.   


Oh, you did.  Maybe we crossed paths.  You never know, yeah. So, at that time, Groupon didn’t have an insights function.  And so, we were brought in through a partnership to help them set up their customer and merchant feedback system globally.  I’m going to forget the number of countries and languages, but it was in the 20s and 30s of countries and languages that we designed their customer and merchant feedback system that has been critical to their growth and evolution as a company over time.  So, that was a really exciting one. And towards the tail end of our engagement, we helped to facilitate them bringing in an insights manager that took over the program that we helped to build and ran with it from there.  


You don’t remember the person’s name, do you?


Uhm, Eric Rasmussen.


I totally know Eric.


You do.


I know him well.  It’s so funny.  


That’s great.


Yeah, that is…  Small world…


So, I was on site in Chicago for six months prior to Eric being there.  And then, I… 


Yeah, ‘cause he was in the Bay Area.


Yeah, he was at uh…  Shoot, I forget where he was at in the Bay Area.


I think he was working out of Palo Alto.


Yeah, yeah, anyway so we crossed paths for about two months in the transition.


Gosh, yeah, I remember when he moved over to Chicago.


And I think he’s still there, right?


Yeah, to my knowledge he is.  I haven’t connected with him in over a year.  That’s great. That is hilarious. So, it sounds like you’re operating in this really important sweet spot where you think about companies setting up trip wires if they’re in a premium model, opportunities to be able to upsell or add value to their user base.  As enterprise sales are looking a lot more like B2C historically has, which I’m seeing, it feels like you guys are operating in this great kind of growth space. Is that what your…? 


Yeah, I think so.  I agree with your point.  The way companies and customers, whether it’s B2B or B2C, are buying is evolving.  They’re much more empowered; their voices are louder; they have more control, more just overall experience.  And that’s critical because it changes how a company goes to market.  




It changes how they price; it changes the whole customer journey from the experience to how you support them, how you service them, how you try to upsell them.  It’s evolving just because of the power that the end-customer has now. And so, for us, that’s a huge opportunity because it’s an opportunity to remind companies that, “You need to listen to your customer – whether it’s a B2B or it’s a partner, whomever it may be – you need to listen because they have a lot of power and you need to evolve around that to ensure that you can retain them as a customer.”  


Yeah, exactly.  It used to be the case companies were who they said they were, and now it’s they are who their customer says they are. 


That’s right.


And the customer has a freakin’ mega microphone that they can project that voice across their constituents.


And they have options.  It’s not just the voice.  There are getting fewer and fewer companies that can truly say they are uniquely different.  Not that that doesn’t exist today but there are so many opportunities for clients to go somewhere else. 


And, you know customer journey mapping is still really hard.  It’s one of those things where it’s easy to kind of conceptualize how it’s going to go on a map, but it’s getting more and more complex.  Like we’re not just email marketing anymore. In fact, email is probably a diminished sort of like focus.


Absolutely.  One area of expertise that we have is in that customer journey mapping.  And the critical piece of that is, again, thinking about it as a journey and not the touch points, right, and understanding the origination and the ultimate synthesis of where that journey is going to take customers and ensuring that you can measure the important parts of that journey because not every single touch point is critical in terms of measuring and making changes to the business.  You need to ensure that you’re monitoring the right touch points.     


Yeah, got it.  That’s fascinating.  If somebody wants to get in contact Lawrence either with yourself or someone else at Cicero Group, how would they do that? 


You bet.  You can find us on the web at  They’re welcome to reach out to me directly, which is LCowan, C-O-W-A-N, as well. 


It is an honor having you on the podcast.  Thanks so much for joining me.


You bet.  Happy to be here.


Everybody else, if you found value in this episode, please take the time (15 seconds) screen capture, share it on social media (Twitter, LinkedIn, don’t care).  Love if you take the time and effort to do that. Hope you found it valuable; I certainly did. Have a great rest of your day.

PAW 2019 Podcast Series

PAW 2019 Conference Series – Krishna Kallakuri – diwo

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Krishna Kallakuri, Founder and CEO of diwo.

Find Krishna Online:





You’re listening to the Happy Market Research Podcast.  I’m Jamin Brazil, your host. We are live at Predictive Analytics World, Marketing Analytics World.  There’s lots of worlds. I think there’s nine. Anyway, there’s health care.  


Quite a few.


Yeah, quite a few.  My guest today is Krishna.  He is the CEO of diwo. Tell me a little bit about what diwo does.


Diwo is a cognitive decision-making platform.  So, the word “cognitive” means the whole idea of how can you take the knowledge aspect of your data, teach it to a machine, and can the machine help the humans with their decision-making process, can it augment.  When the human is confused, can the machine help him or “This is a better choice for you today,” “This is how you should react in your business,” and “This is the possible outcome.” So, that is the whole motion around diwo.  That’s exactly why we call it “data in, wisdom out.” We want to help the business community with the decision-making process where we are able to reduce their cognitive load of their human brain, reduce cognitive confusion and, most importantly, augment the decision-making process.    


That is really interesting.  I like the way that you’re framing that out:  basically, making decisions a little bit easier while still using insights as the premise.  So, when did you guys start the business?  


So, it’s about four-and-a-half years ago.


Fairly recent.  You have a favorite customer story?


Yes, I do.  We actually started our first engagement with a fast fashion retailer.  When you look at the process that they had on how they were managing quite a few of their stores across the country, one of their biggest challenges was which product needs to be on the shelf at what location and at what time.  When you look at these simple questions, you may think the retailers have figured it out, they have so much experience in business. But the real problem that this customer was facing is they can’t put the right product at the right time at the right location because the customers are not reacting to it or either the product is not on the shelf at the right time, right?  So, when you look at the whole business model, it was directly impacting on the revenues because, if a product leaves a warehouse, it’s never coming back. Either you sell it or you promote it or you throw it. So the challenge that we were asked to solve for them is how do we allocate the right product at right location at the right time for the upcoming season. So, diwo, as a platform, it was able to really consolidate the customer behavior, the product behavior, the location behavior and, most importantly, it was able to embed the complete business process around it, which means now all the merchandisers, they know, “OK, 20 weeks ahead, this is where I have demand”  “This is how I need to create a buy plan, which means now OK, I need 1,000 units for these 10 locations.” Now, they have enough time to procure the material; they have enough time to manufacture the product; and then, when they manufacture the product, diwo, as a platform, is able to tell them the merchandisers that “OK, this is how you need to deploy,” which means, “Now, OK, you put 100 units in store A or put 100 units in store B or what are the cases.” So, we are really closing that whole loop. This what we mean by taking insights to a whole new level, right? How can you help that business community start consuming analytics instead of producing analytics? It’s not about creating data science models or producing insights.  That’s where most people stop, but we want to address the last mile in business. Is diwo able to contribute towards one of your milestones in your business process. So, that’s where we are really focused. So, that story that I just described is now creating a significant impact on their business, which is really saving them millions of dollars.     


That’s fantastic.  I love the framework of just-in-time retail inventory management and then building predictive models that illustrate consumer behavior, which just empowers the retailer in a tremendous way.  It’s a massive consumer advantage. Give me another example of where you guys are rolling this out. Is it predominantly in retail management or are there other sectors?


Diwo is very agnostic of any retail.  So, our second use case started with an insurance company in Australia.  


Oh, wow!  Totally different.


So, one of their biggest struggles was they wanted to identify what could be your pricing philosophy for the upcoming renewal season.  If they react to a certain customer segment where the policy is increased by 5%, they don’t understand the impact of that, which means, “Is the customer going to stay with me or am I going to churn?”  It’s very challenging situation for them in Australia. The reason is because of all the data privacy policies they have in place. If you as a driver, if you have five accidents in a given year, and if you want to just go switch to another insurance risk provider, you could just say, “I have no accidents.”  So you, basically, have no access to his driver’s history or nothing. So, in such difficult situations, now you’re always about retaining your existing customer-base rather than going after new customer-base. So, diwo, as a platform, was able to tell them, “OK, for the upcoming season, these are the segments that I see that are highly risky, but, if you react to a policy change, there is a 90%-likely chance that you will lose these segments and these are the reasons why.”  So helping them really stay ahead of the pricing optimization for the upcoming renewal season was very critical because even 1%, 2% churn in their business is a significant impact for them in billions of dollars.        




So this is a great use case that we solved for them.  And, as we speak, we’re also working with another major financial firm in New York, helping them with how they balance the aspect of delinquent customer segments.  So, if you look at most financial organizations, they pretty much know that if a customer segment is going to be delinquent. It’s inevitable; they can’t control that.  They, at least, have to figure out, “OK, if my delinquency rate is increasing, how do I add new revenue streams into the business?” Diwo, as a platform, was able to address that key problem on how do you manage your risk around these portfolios and these segments are likely to be delinquent.  So you’ll have to approve more accounts in this segment. Even though they are highly risky in nature, there is a good chance that you will make more revenue for a shorter time for this high-risk customer too. So very interesting use case that we are solving for them too.  


Yeah, that is very interesting.  You’ve been in the industry a long time.  What do you see as a major trend and what do you think is going to change in the next three to five years?  How are we going to different?


I have a positive impression about this and a negative impression about the changes that we are looking at in the industry.  At least from what we see, most organizations are adopting technology just for the sake of it, not knowing what the outcome of that technology should look like, which means technology is never aligned to the business application.  That’s one major challenge that we see in the space. And the second aspect, especially when you talk about AI in general, again, most communities are so passionate about the experience part of the customer: “Did I detect your face?”  “Did I read your emotion?” That’s all great, you know. It’s very technologically savvy kind of a process that you can bring in your business. But people are forgetting the fundamental aspect around the operational aspect: “Did I first increase the efficiency in business before I put experience part?” and “When I combine experience and efficiency, can I make that more effective?”  That’s what people are missing out. So I think probably we’ll see some adoption from the business community, but, if you are just stuck with experience, I think that is a problem that we all have to solve or put our heads together to help people educate. You have to climb this ladder one step at a time. You don’t come from top-down; you go bottom-up. That’s where we think the industry is headed.    


I really like that framing.  I think you’re right that it is a 1+1=3 as you incorporate what you’re talking about, which is the basic business structural insight function and then also layering in, whether it’s the emotional connection or what have you, experience-based part of it as, again, one of the legs of the stool that help the business make the right decisions. 




I think we’ve seen a democratization of access to insights over the last five years.  It’s been kind of unprecedented. So, it used to be the case it was really hard to get insights, you know, data, and then ultimately…  You know, we went from a… We just been in such a data gathering stage inside of the corporations. And yet, at the same time, we feel like – or I feel like – we’re not necessarily smarter just because we have all the data.  Is part of the thesis then figuring out… like taking the customer’s data and then structuring it in a way that can be analyzed and useful for insights?


At least our perspective about insights are experience that we have gone through working with several large enterprises.  Is this whole notion of, “All right, let’s build a data lake; let’s hire a data scientist; let’s build insights.” That’s almost you’re trying to dig for nuggets in a desert.  You don’t start your journey with data. You start your journey with value, right? What is the business problem we’re trying to solve? What is the value that you want to demonstrate coming out of that exercise?  Is there a business reason to do that? Is there an optimization aspect that we’re trying to address or is there efficiency or whatever the case is, right? Unless you start your journey and reverse engineer the process back to the data, this whole aspect of building insights will just remain another deep forest with no direction for anybody.  That’s at least what we think is happening today as for insights because building insights is a very highly technical data scientist-kind of a role. Now, when people don’t understand the true business problem, what insights are you building and what value are they creating? That’s the biggest question.  


Yeah, it’s not enough just to have insights for insights sake.  You really got to have your objective in mind. What your KPI’s are and how that’s going to be a lever and move the business in the right direction.  


Absolutely.  It’s all about business levers.  Unless you cannot move some of those levers with these insights, they’re no good to you.  


Right.  Yeah, that’s fantastic.  If somebody wants to get in contact with you, how would they do that, or the business diwo? 


We have a sales team; we have a business development team.  They could visit us at They could simply reach out by just filling a simple form.  They can always shoot an email to  And one of us will reach back out to the audience to help them understand what we deal with here.  


Krishna, thank you so much for being on the Happy Market Research Podcast.  


Thank you, thank you, Jamin.  It was a pleasure speaking to you this afternoon.  


And all of you who found value in this episode, if you please take the time to screen capture this episode, share it on LinkedIn, share it on Twitter, social media platform of your choice.  Love to get the feedback from you. Have a great rest of your day.

PAW 2019 Podcast Series

PAW 2019 Conference Series – Jeff Todd – Wolfram Research

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Jeff Todd, Senior Account Executive at Wolfram Research.  

Find Jeff Online:





This is Jamin, and you’re listening to the Happy Market Research Podcast live today at Predictive Analytics World.  I have Jeff Todd, the Senior Technology Expert at Wolfram Research, Inc. Jeff, thank very much for being on the podcast today.


Thanks for having me, Jamin.


What do you think about the show?


I think it’s great.  There’s a collaborative spirit that I feel like a lot of the people here…  As an industry, I think people are all trying to solve a lot of hard problems now.  For a while, I think they were trying to get the data all into one place. Now that they’ve got it there, they kind of the real problem ahead of them.  So I think everyone is up against the same wall. And so, rather than trying to everyone get their own competitive edge and find a way to outsmart the other people, they’re all kind of just here to learn and figure out the same problems, which is really exciting.      


It definitely feels like the rising-tide principle’s applying here, where it’s a lot less cut-throat and a lot more collaborative, recognizing the fact that we’re at the very early stages of massive growth inside of major and I call it minor (not in a negative way) but smaller organizations.  At the end of the day, whether it’s optimizing your production line or creating outstanding customer experiences, data plays a key part in the entire ecosystem for success. And the proportion of the decisions that are happening in the organization are still uninformed. In that way I’m saying we’ve got an outsized opportunity in front of us for growth as it relates with insights inside of companies. 


Yeah, I think that’s exactly right.  I think too we see many organizations that are trying to achieve automation for a variety of tasks that have traditionally been manual tasks.  And that’ll divert a lot of human resources. And you certainly hear about machine learning, neural nets, AI as a fear of interruption to folks like cab drivers and truck drivers and replacing them, and the question of what do these people do.  Obviously, there’s going to be a place for those people to go. It always evolves and expands, but I think it’s very exciting that you have both the type of AI and machine learning outcomes that you have where we’ll be able to get more insights out of the data to be able to make new decisions, make new discoveries, new innovations and kind of push things past and have that growth that you’re describing.  At the same time, I think we’ll see a lot of our world start to change a little bit as things that we’ve traditionally been used to interacting with humans become less and less so, which we already see at the shopping markets, grocery stores interact less and less everyday people on the way out.


Yeah, yeah.  Let’s back up a little bit.  You’ve been in the industry for a while.  What do you see as one of the megatrends? How are we going to be different in three to five years


Well, I think autonomous driving is going to be probably one of the first major things that most of us see a huge change in our regular day-to-day world.  I think that it’s already happening. Cars are already on the road as we can tell. I was just riding along with a friend of mine, who had a Tesla, marveling at the ingenuity of speeding down the highway


The autopilot is crazy!


It’s amazing.  I was actually just transfixed on the screen watching it interpret all the cars around, and, in fact, asking my friend how his experience was.  Mentioned that it actually reacts faster than he reacts when someone is about to do something dangerous.     


So, I drove from Fresno to Las Vegas as opposed to fly.  It’s a six-hour drive, one-hour flight. But, by the time you’re done messing around and I had to take some stuff (this equipment and things like that); so, “I’m just going to drive, enjoy it.”  I used autopilot about 30% of the way. I wound up getting a flat because I didn’t use autopilot. And I thought I would speed and I hit a bad spot and yadi yadi yada. Anyway, dang it, why did I take over?  I should have let the machine do it? 


Right, I think you’ll find that’s going to be…  Even people who have fears of the future and issues potentially with it…  I know my friend was ready to embrace that technology; his wife was not. She was very scared to get into the car.  


My wife’s the same.


Yeah, and use that.  But she took a long trip (several hours), and that just completely changed her mind:  the fact that she could kind of just sit back and relax to some degree while the car did the work.  I think people are going to come around to the use of automation to not only vehicles but other parts of their lives.  I will be surprised if we don’t see a little bit of a backlash at first from that as well as maybe even a return or a renaissance of human interaction.  As we get more and more separated from that experience, I think people actually in certain areas want to come back to that.


I think that’s a really good example.  You’re seeing that right now with a little bit more investment for Amazon into brick and mortar bookstores, which is hilarious.  I think about the grocery disruption, as you already pointed out, this happening in the grocer space where now you have fully automated (you just load up your cart and leave) frameworks.  But there’s some interactions in the grocery store that I really like. So I can see that, after the pendulum sort of swings all the way over, I can see like you having a human butcher or that kind person you can talk to and ask questions and still get that connection.  To your earlier point, it’s not going away; it’s going to create higher value opportunities for us as opposed to worrying about the ones and zeros side of life. It’s an exciting time.    


I agree.  


I checked out Wolfram a little while ago, and I’ve been on Wolfram Alpha for about the last 20 minutes because, it’s like a Google-like search engine, but it uses an LP (now this is a lay person telling you what you do).


You’re doing great.  Keep going.


It’s all humility at this point.  Uses an LP to and a bunch of fancy math to…  You can ask it a question and it will search many, many different data sources after it interprets your question and then it answers it in a way that looks like a human being put a report together for you.  And then it also incidentally has all the reference points so you can see what data’s been used in the context it’s been gathered, etc., etc. So WolframAlpha is a really neat resource for anybody that wants to get more information about a certain subject, which is crazy that I just now found out about it. I feel like I’ve been behind, but we can bring that to light now with this podcast.  Maybe you can tell us a little bit about Wolfram and what it is you guys do.


Sure.  So, Wolfram Research is a 30-year-old technology company.  We’ve been around a long time.


That’s 30 years, folks.


30 years, 31 probably here in June.  Actually, so as the brain child of Stephen Wolfram…  Dr. Stephen Wolfram was kind of the run-of-the-mill physics genius.


20-year-old Ph.D.




15, I’m sorry.  My bad.


No problem.


That’s a big difference.


It is a big difference.  It is a big difference. So, in his research, he found that he was growing frustrated.  That if I wanted to pursue statistics and probability, I had to learn one language. If I wanted to do machine learning, I had to learn a whole new language.  If I wanted to do image processing, yet another language. And I had to make them all work together. I believe the belief was that that was not an ideal situation.  There should be one language, a uniform approach to any kind of computation; it should be high level; it should be intelligent; it should be automated; it should be integrated.  There should be a lot of intelligence put into it so that I, the end-user, have the quickest route from my question to my answer with the least amount of coding.  

And so, that’s what we’ve been doing over the past 30 years.  And that’s one of the reasons why Wolfram Alpha can do what it does as well as why you haven’t seen another Wolfram Alpha from any other competitor come out in that time because there just isn’t a way to replicate that.  You’d have to replicate the 20 years of that work that led up to the ability to create something like that. So, we work with all kinds of industries. We don’t have any one particular segment or market that we’re heavy in.  We work with anyone who needs to do computation, which is pretty much everybody these days. 


Yeah, to say the least.  The programming language that you developed, that was in the 80s, right?


That’s right.


Yeah, so, I mean a long time.  There’s been a lot of heritage, I guess.  I’m really surprised I haven’t heard more about Wolfram.  I mean I was doing stats through the 90s, using SPSS predominantly.  Intuitively, from what I’ve gathered so far, it looks like I could get similar outcomes easier had I been using Wolfram.   


Yeah, there definitely has been a progression of features that have been added into the language over time.  I think, in the beginning, a lot of the symbolic math was predominantly what people knew it for and used it for.  So, it got a heavy presence in academia, and I think then, as it matures, most people maybe had the idea that it was this symbolic package.  That’s what we started with. It certainly could do numerics, but in its day, that wasn’t the focus. Later down the road, we began implementing just continuous and huge amounts of functionality, entire sets of functionality that would equate to a third-party program, several third-party programs each release.  And so, we began to become this extremely comprehensive system that, I think, as you say not as many people knew about. And so, they would come and revisit. And I think I have yet to have someone visit the booth or come and talk to in person where their jaw doesn’t hit the floor as least at some point in time where we show them in one function what they remember taking them half a week and 100 lines of code to try to figure out.    


So, Wolfram Alpha’s free.  How do you make money?


So, Wolfram Alpha is free.  There are some Pro Features that you can subscribe to.  So, a lot of students are customers of ours, and they will go in and they’ll put in all their kind of difficult symbolic integrals and differential equations, even calculus and algebra, as they’re trying to learn.  And one of the nice things about the Pro Subscription, it’ll actually allow you to show steps on every one of the problems. I certainly wish I had that when I was in college because I had my teacher for about one hour a day and then I had the back of the book with all the answers, but I didn’t have how to solve everything for the rest of the time.  And so, Wolfram Alpha to some degree has actually been a substitute teacher for many people in helping them work through that. We also have a custom version of Wolfram Alpha…


Really quick before you talk about the custom version.  So, have you seen Incredibles 2?  




So, like that scene where he’s like, “New math!”  That’s like right there thinking, “That’s awesome.  I can’t wait to get home and show my kids how smart I am.”


That’s right.  Well, you know and I’ve had so many people say, “Thank God.  It saved my life in college. You guys were the reason I graduated.”  Every time I have a… meet somebody, I say, “Do you have kids?” And they say, “Yeah, I’ve got some people who are just about heading into middle school or high school.”  I say, “Go find Wolfram Alpha. It’s going to save your life when they come home and show you the math they’ve never seen before. It’s going to help you out.”      


All right.  You’re saying Pro.


Yeah, so we actually, as a means of making money, so we actually have the ability to take the technology of Wolfram Alpha, the free site, and everything that we’ve put into that and layer that on top of organizational data.  So, imagine being in your corporation and you – the CEO, CFO, manager, employees – being able to ask natural English questions of your own data and being able to get that back, as you said, as a report like a human gave to you ad hoc with no pre-scripting, with no business intelligence, no IT guy that’s gone off and made this one-off thing for you.  It has the intelligence kind of baked into it. So there’s an AI sits between the person asking the question, all of the data, and all the algorithms.  You ask a question. It goes out, finds the data, finds the appropriate algorithms, applies it, supplies you back with the reports, the answers in real time.  So you can continue to ask questions, rather than wait two weeks for Report No. 1, wait two weeks for Report No. 3, and then forget, “What did I even ask for?”      


What kind of data is it able to query?  I’m thinking about at an organizational level.  Like are you guys pulling in stuff from Excel files or…? 


Yeah, it could be any.  It supports 180 different file formats.  So that could be Excel files, CSV, text files; it could be JPEGs, GIFs; it could be a wide range (HDF5); it could be a wide range of file types.  It could be any kind of database; it could be streaming. For example, we have financial data in the free version. You can ask it, “What was Microsoft’s last 30 days closing price?”  That will change: if you ask it throughout the day, that will change as it updates ‘cause it’s a streaming service that we’ll pull down from. So there’s a variety of different data sources that you can feed into that.  It could be images. You could perform image-processing techniques in natural language on images you have somewhere in the company. So, it’s a variety of things that they would want. And it could be any department: sales could be looking at sales figures; HR could see what team member is under which manager; they could see what projects people are working on; who has a birthday today.  There’s all kinds of variety of things that they could use as well as engineers could have entire specs of their models of their machines that they might want to ask, “Does this bolt fit this specification?” And we have connectivity, and we’ve worked with Amazon Alexa; we’ve worked with Apple Siri. So there’s actually ways that you could ask a question live. And I’ve actually worked with folks like Dow Chemical where they’ve got their hands in the gloves; they’re doing chemical experiments and they would like to ask a question.  Maybe, they need to know the melting point of methanol right now.          


It seems important to me right now.  I’ve never thought of it until just now.


That’s right.


The most important thing in the world.


The most important thing in the world.  So they want to ask that question. Instead of having to stop the experiment, pull their hands out of the gloves, walk 15 minutes back to their office, ask the question, come back, they could just ask, “Alexa, ask Wolfram for the melting point of methanol.”  Get that and then continue the experiment.


That’s really cool.  Is part of the challenge in the market, it’s so broad?


Absolutely.  It’s a really hard thing to have a little bit to help every single person.  People often ask, “Which market do you guys market to? Which vertical are you guys in?  What solution space do you guys fill?” And I often have to just show them like a list of literally every other technical program that exists and say, “This is our list that we compete with.  This is our list that we have to know a little bit about” because in a given week I might talk to Morgan Stanley; I might talk to Pfizer; I might talk to Disney; I might talk to Nationwide Insurance and all about different things.  They’re all doing computation, working with data, but they’re all after different industries and different markets. And we kind of have to be able to know a little about everything, which can be difficult.    


Yeah, for sure.  Well, the good news math is math regardless of the sector.  So the applications… The context is important though, to your point, and you do need to be like a subject matter expert, so to speak, in every freakin’ sector. 


It can be tough, it can be tough.


As you…  You think about the success that you’ve had over the last few years, do you have a specific customer story that resonates with you?  Like, “Gosh, this was such a great outcome” or example of them applying Wolfram to their business, then getting some benefit.   


I think any time you can be part of a technology or integrate a technology that you use every day or that you can point toward.  I think a lot of times our technology gets used in a way that get obfuscated. It was in the research, in the R&D step, or it was by some engineer.  You never get to see the eventual outcome. People don’t necessarily know that you were part of it. Obviously, it’s nice for me to know that when people are using iPhones or when I’m at home talking to my Amazon Alexa, there’s a part of Wolfram in the back-end maybe helping with that.  On a personal note though, I was talking with someone here from NIH about a potential project with them. We worked with a company called Christy Health. Christy was actually a person that had a really hard life. They were, I think, the first heart and liver transplant in the western U.S.  They also had a kidney transplant last year. And so, Christy was the wife, partner of the person that ran Christy Health.  

In doing so, he found this huge issue with reporting medical data.  So, as he would do dialysis and as he would work with her on all these things, he found that there was just… things were getting scrawled on notes; things were not getting reported back correctly; and it made it really difficult for him and for Christy to be able to advance and to be able to get better and be treated correctly and for doctors to understand what they have gone through.  And so, he actually used our technology to stand up services that other people like him and like Christy could actually download themselves to better manage their own health because it’s so important. And so, when I get a chance to work with organizations or work with people who are really making a difference in our day-to-day lives for things that really matter like people’s lives and their health and their love for each other, that’s actually a really great thing to be a part of.    


Oh, for sure.  That’s awesome.  I love that story.  You’ve seen the industry evolution.  Looking forward three to five years, how are we going to different? 


That’s a tough question.  


It’s actually really tough.  


Yeah, and usually I think we rely on Stephen Wolfram to look five years in the future for us.  


Right, yeah, there you go.


That’ll be out.  So, maybe, I’ll just look and see what’s going to be in Mathmatica 13 and I can tell you what the next three to five years is going to be.  


The Nostradamus of our day.  


Interesting story, right?  Today, I think you’ll see in the news Python and Jupiter are just discovering and touting the idea of notebooks as an interface for programming.  And many people are saying, “Oh, this is great. Now I can marry my text in line with my code in line with my results. And it’s an awesome way to show people all these results.”  And we always get a little bit of chuckle because we actually had the notebook 30 years ago, and that’s been our main way of working in Mathmatica for all that time. So, talk about living in the future, I mean that was started 30 years ago, and it’s just now kind of becoming “the thing.” 


Yeah, it’s funny, huh?  Like all these old school practices, like even the cloud, we were doing cloud computing back in 2000, but it was not called that. Everybody launched their cloud solutions and it took me like two years to figure out I needed to start calling what we’re doing the cloud.  It’s funny. 


Terminology changes, I think we’re going to see a lot more…  It’s interesting because every, I think, four to five years there seems to be trend.  Before all the machine learning, neural networks, and AI, which is like the huge trend right now, it was all Big Data;  it was “How do I process all this data and where do I put it?” and “I need to set up a data lake.” and “I need to figure out where we can have a strategy for that.”  And before that, there was probably GPU computing. And before that, it was grids and clusters and “How do I get more out of stuff?” These days, I don’t hear about grids and clusters anymore.  I don’t even hear that much about GPU even though it’s still a great part of the competitional landscape. So I don’t what the next four- or five-year thing is going to be where everyone goes crazy and goes after it, and it’s going to be the next big, huge topic that we have a conference around.          


Yeah, that’s right.  That’s awesome, that’s awesome.  So, conference has been interesting:  great speakers, great attendees, lead generations.  All that going pretty well for you guys?    


Yeah.  You know I’ve been at shows that have had 1,000 people; I’ve had shows that have been 50,000 people.  And it’s really about whether you’re at a show where your message and your technology is relevant to the people that are there.  And I’ve been to shows where we get 1,000 leads, and not a single one of them is really worth anything because it was just not anyone that relevant to what we do.  And we’ve been at a show like today where almost every single person I talk to has an extremely relevant problem that we can help to solve. So certainly, I’d much rather have a discussion with those people every day all day than just be at a show and just chug numbers all day.    


They are my favorite type of people.  Jeff, if somebody wants to get in contact with you or Wolfram, how would they do that?  


You can email me at  Certainly, you can just call in Wolfram direct line:  1-800-WOLFRAM and ask for me personally. I’d be happy to talk with you.  Or you can come to our booth here at Predictive Analytics World if anyone’s listening and come on down and we’ll be happy to walk you through a demo.  


Of course, we’ll include that information in the show notes for those of you who are listening.  Jeff, thanks very much for being on the Happy Market Research Podcast.  


Thank you so much for having me.


Those of you who have found value in this episode, if you please take the time, screenshot, share on social media (LinkedIn, Twitter), I’d greatly appreciate it.  Have a wonderful rest of your day.    

PAW 2019 Podcast Series

PAW 2019 Conference Series – James Taylor – Decision Management Solutions

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews James Taylor, CEO of Decision Management Solutions.

Find James Online:



Decision Management Solutions


My guest today is James Taylor.  We are live at Predictive Analytics World, Marketing Analytics World.  His business is Decision…


Management Solutions.


Got it.  You are chairing a track today.  


Yeah, I’m monitoring the business track.  I’m kicking it off right after the keynotes, and then I’m monitoring it for the next couple of days.


That’s great.  Obviously, you know who’s on the panel.


Yeah, there’s a panel; there’s a whole bunch of presenters.  It tends to be the ones who are not so much talking about how to build a predictive analytic model so much as how to use a predictive analytics model:  how to put it into production, how to get people to adopt it, what the challenges are, getting people to understand what a model does, and those non-modeling kinds of things. 


Which actually the better that goes, the bigger the lever of the model, right?  I mean… 


Yeah, I’d go further.  I would say that if you…  There’s not difference between business value and analytic values.  If your analytic, however good it is, isn’t actually being used, then it has no value. 


That’s right, that’s right.  The ROI on that’s really easy to calculate.  Now, this is not your first show.


No, I’ve been, I think, to every Predictive Analytics World.  I’ve been coming here a long time. I know Eric and the Rising Media folks well.  And it’s been fun to watch it grow and watch it change from really a very niche kind of show where’s there’s people from financial services and credit cards to one where all sorts of people are here.  It’s great.


Now, your business, tell us a little bit about what it is that you guys do.


So, Decision Management Solutions focuses on companies that are really trying to automate decision-making.  So, there’s some high-volume transaction where it’s not obvious what to do. So, you have to decide what to do for each transaction.  And, obviously, that’s driving a lot of analytics. A lot of people what they want to do is they want to use machine learning, analytics, AI to make a better decision about these transactions, but they’re high enough volume that you can’t just show stuff to people and hope the people can handle the transaction, you got to automate it.  So we help build those automated systems, into which you can embed these kinds of analytic models.   


Do you have like a favorite project or ideal customer-type?


Uh, my favorite project probably is around like “next best offer, next best action” kinds of things.  If you’re a multi-line company, you’ve got lots of different products or your products have complex eligibility like insurance products, then it’s easy to say, “Oh, you should make the next best offer to this customer this moment in the customer journey.”  But actually, figuring out what that offer is – given what they already own, what they’re allowed to buy, which products go with which other products, what the rules are – is a non-trivial problem for most companies. And those systems tend to be more fun ‘cause they’re not as heavily regulated as some other decisions.  So you don’t have to worry about the law quite as much as you do; you have to worry a little but not a lot. The key concern is privacy but the kinds of systems we build don’t need to who you are, they just need to know things about you.  


So, you’re not dealing with PII, that’s how you’re able to bypass.


No PII, exactly.  It’s one of our sort of rules of thumb.  We don’t care who you are; we just need to know what kind of person you are, what kind of products you own.  We need that data, but we don’t need to know which particular customer we’re talking to. And that makes it…  It’s great: you can use the cloud; you can use advanced analytics.


It opens up a lot of stuff as soon as you can divorce the PII from the actual….. It’s such a big issue.


It’s a big deal, yes, and particularly as companies are trying to move to the cloud, and particularly with analytics, and you need that horsepower.  And they get very nervous when you start making decisions about customers ‘cause they all want to know about PII. So we have lots of meetings with infosec people, who come in, and we describe how it works and they go, “Oh, OK.  We’re done.”  


Oh, that’s a very nice shortcut.


It is.


Because you’ve been part of the ecosystem for a while, what has been like one of the big trends that you’re seeing emerge in the space?


So, I think there’s a couple of things that have really changed.  One is, when we started, getting from what the analytics team built to something you could execute was often a huge production:  a lot of coding, a lot of recoding. And so, we would talk about deploying the model as a big barrier. Nowadays, you look at the modern tools that are out there, and they got one-button deployment.  And one of my colleagues has this great phrase: He says, “You know it’s not about deployment; it’s about employment.” However easy it is to deploy the model, are you employing it? Are you doing something with the model?  And so, that’s always been our focus, but now we can’t talk about deploying the model as a sort of phrase ‘cause people go, “But I’ve got a button for that.” I go, “Yeah, I know you have a button for that. Now you have an API that calls your model.”  Still, “Is anybody using it? How’s it going to be used? Can you explain how it’s being used? Can see if it’s being used appropriately? Can you see what the key factors were in the model when you used the model ‘cause you don’t care what the factors were when you didn’t use the model?  You only cared when you used the model.” All those kinds of questions come up. That’s really shifted the conversation away from that.      

The other thing, I think I would say, is I see more IT departments that care more about analytics.  When we started, the IT departments were like, “Don’t talk to me about the predictive analytics guys.  They run their own server; they do they own thing. I just try not to fret about it.” And now, I wouldn’t say that they’re totally in it. But we meet analytics teams that are part of IT; there’s a lot more integration.  A lot of BI teams are trying to get into data science, particularly analytics. So there’s a lot more overlap. I think that’s a really good thing because I think IT in most big companies is the key barrier for a lot of analytics projects.  If you don’t get IT to buy in, then you’re not getting there.    


Yeah, absolutely.  It’s a big budget and also a gatekeeper.


‘Cause they’re like, “You’re going to destabilize my system with this probability nonsense?  No!’” So you have to find a way to get through that problem.    


Yeah, yeah, for sure.  You’ve got a lot of management consulting around the actual, not just the execution, but, as you said, the employment of those insights inside of the organization.  What are you seeing as other trends? How’s the space going to be different in the next two years?  


I think one of my pet projects is get business analysts, more generally, to include in their requirements the need for machine learning, the need for analytics ‘cause one of the things I see at the moment is the only people who are really thinking about how you might use machine learning are people who know how to build machine learning models.  But in most big companies, they don’t write the requirements documents; they don’t write the specs. And so, by the time, they get involved the requirements document’s written; the IT budget is set; the project’s up and running. And then, you’re constantly trying to bolt things in and add things to dashboards. And it’s never integrated. And so, one of my pet projects is to get business analysts to be much more specific about saying, “This system is making this decision.  I wonder if we could use data to make it better. What would that look like?” And get them to drive that into their requirements so that right at the beginning of a system’s life cycle, people are talking about, “What decision does this system make? How do we use analytics to get better at that decision?” We’re going to get scale in companies. 


So you really have to move upstream.


Oh, yeah, right.  My title of my talk today is “Doing It Backwards” ‘cause I feel like people say, “I’ve got the data.  See what interesting things I can find out about the data. And then I see if I can use that.” And I’m like, “Well, that’s backwards.”  You actually need to start by saying, “What problem am I trying to solve?” and then see what analytics you need to solve it, and then see what data you need to build the analytics.  They’re like, “Well, but where’s the research? Where’s the hypothesis testing? And I’m like, “Yeah, if you’re the kind of company that’s good at research, you should do a little bit of research.”  But most companies are terrible at that kind of stuff. And you’re going to be the same kind of company in ten years time that you are now. So focus on the things you have to do to be that kind of company and use analytics to get better at them.  Rather than trying, “Maybe, there’s a new business in my data.” Well, maybe, but probably not. If you’re an insurance company today, in ten years time, you’re going to be an insurance company. The only question is whether you can use data and analytics to be a more profitable, more effective, more successful insurance company or not.  So that means you got to start by thinking about, “What does it mean to be an insurance company? What decisions do I make?”   


Isn’t that interesting how it’s like, it’s really helping frame the business as a whole beyond just…  That was a very good answer.   




What I find just fascinating is that we’ve seen this like democratization of insights or analytics across the organization.  So, it used to be relegated to a few, and now it feels like it’s seeping through many, many different, even IT, which is amazing.  So, as we continue to evolve… I loved the way you depicted it: moving upstream and being involved in the early stages of the product and then having that actually built into the key requirements.  I mean it’s going to be an exciting world because really brands are at a spot where they get to decide if they’re going to win or lose on Day 0, based on how they’re incorporating analytics into their platforms.   


Yeah, absolutely.  One of my favorite phrases I often talk to my typical customer is I like to say a “big, boring company.”  And people keep telling them to use machine learning and AI. They got to be agile and nimble and think like a startup.  And I’m like, “No, being big and boring is actually part of their value proposition. Buying life insurance, you’re buying it from big, boring company.  You’re banking with a big, boring company. That’s kind of the point, right? If you want to buy a tractor, you want to buy it from a big, boring company, right, ‘cause that’s the point, right?  And so, it’s not enough if we say only small nimble startups, digital natives can use analytics pervasively. Big, boring companies have to be able to use it pervasively. And that means we’ve got to fit the way they build systems, the way they think about their business, which tends to be lots of documentation, lots of thinking, long lead times, requirements. If we don’t get data thinking into that process, they’re never going to be pervasive users of data.  And they need to be.   


My guest today has been James Taylor.  If somebody wants to get in contact with you or your firm, how would they do that?


So, the easiest way to find it is  It’s a really long URL but all the words are spelled exactly the way you say it. 


It’s great for SEO, by the way.


That’s always good.  And then, gets me.  And that’s an easy way to find us.


James, thanks so much for being on the show.


Thanks very much.


Everybody else, if you please take the time to screen shot and share this episode.  Special thanks to Predictive Analytics World and Marketing Analytics World for hosting this particular episode.  I hope you have a fantastic rest of your day.

PAW 2019 Podcast Series

PAW 2019 Conference Series – Gerhard Pilcher – Elder Research

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Gerhard Pilcher, CEO of Elder Research.

Find Gerhard Online:



Elder Research


I’ve been working with data in the technology field, but in a weird…  I have a really weird… I have a really crazy career path. 


All right.  Let’s hear it.


Before I went to technology, I was CTO for a telecom firm, inventing the new DSL subscriber line (high-speed over copper).


What year is this?


This was in the years from about ’90 to about ’99.


So early 90s, all the 90s.


All the 90s.


That was a crazy time, right?


It was a crazy time, and things were booming.  It was insane. Well, then had a tragedy sort of happen in my family and realized I wasn’t spending time with my young sons and wife; so, I bought a road construction company.     


Was that better?  I don’t know.


I don’t know.  It was crazy. It was like being a farmer.  You know you’re dependent upon weather and uh…  But the interesting thing is technology was just starting to come into the road construction business.  So it was fun. I got to do some things like do oil sampling in predictive maintenance on our large equipment, which allowed us to avoid downtime and more costly repairs.  I got to bring in GPS grading. And, instead of having people out there pulling strings with wooden stakes and doing traditional surveying, the operators had computers in their equipment, and they could see where the road grade needed to be.  And people don’t realize how much goes into actually building a road and compacting it and getting it ready for pavement. And so, that was a lot of fun. And I ended up we were cutting roads at a higher quality than the state was able to measure that quality; and ended up getting invited to Washington, D.C., to talk about grading and that kind of thing and how they could bring their quality processes along because they were actually taking quality out of the roadways.  So a little bit crazy career there.  

And then I decided at a late age that I’m going to go back to grad school and study more about this statistics and analytics stuff.  And so, my son was just going to be a freshman at NC State and was looking for a place to live. And I made this decision with my wife, but he wasn’t quite aware of what was going on.  And so, I called him one day; I said, “You know you’re having trouble finding a place to live. I’ve got the perfect place for you. It’s an older roommate, probably can cook and clean a little bit for you, but it seems like a decent guy you know.  What do you think?” He said, “Well, give me his number. I’ll call him and check him out.” So I started rattling off my phone number, and he goes, “Wait a minute. That’s your phone number.” “Oh, yeah, that’s my phone number.”  


That’s a crazy story.


“What do you think about spending a freshman year of school with your dad?”      


I don’t know if I’d like that very much, but it’s funny, as a dad, I’d love that.  So, fast forward where we are now, there’s been so much evolution inside of our space.  What do you see as one of the megatrends that’s going to like the next kind of wave that takes us the next three to four years?  


Gosh, I think the next wave really is about calming down from some of the hype of data science and realizing how valuable it is but realizing that I have to have a foundation to really accelerate data science through my organization.  So I think people are starting to think more about their data strategy and how do I treat data more like I treat cash flow into my business. Cash comes into the business; I want to increase the value of that cash as it moves through the business so that I’m worth more at end of line.  We call that internal rate of return. Well, I think people are realizing that data is in that same space. We can actually use data as a currency to improve our business. And some signs of that and some signals of that, if you think about it… If you go back to the mid-80s and you look at what the difference was between our book value of companies, on average across all the publicly traded companies and what the market capitalization or what the market was willing to pay for that company, there was about a 15%-gap on average (some more, some less, of course).

Now today fast forward, that gap is about 87%.  What is the difference in that gap between the market valuation and our book value?  And many people think we’re not accounting for the value of data. Interesting we’re in the information age and accountants haven’t figured out how to put data as an asset on our balance sheet so we can account for that.  So, there’s a lot of trading of data for other services and things that are all off-balance-sheet and non-taxable and been used as a currency but not being recognized officially as a currency or as value in the way that we account for things publicly, right?  And so, many companies are starting to value data internally, and it helps them understand how to make better investments of data and how much to invest in data     


I have never heard – like I’ve talked to a lot of people – the analogy that you just did of connecting the dollar that goes into a business to the dollar that goes out of a business, right?  What’s the x-return on that dollar? Data has not ever been framed like that. Maybe, I’ve been a bad listener but in that exact way. But I think that’s exactly the way that we need to be framing it, and it’s about how do we maximize the return or the overall value, which ROI on insights is something I’ve heard a lot, but not framed like from an accounting lens.  And I think that’s exactly how we have to be thinking about it. That makes perfect sense to me.   


Wow, great because I’m trying to take that message to CFOs and everything and get them to think that way ‘cause they’re saying, “Why are we doing this data analytics?  Why are we spending this money?” I said, “Look at it the other way. Why do you spend money in your business?”  


See it as an asset on your balance sheet.


Right.  How do you manage that?  How do you manage that asset?  You’ve got ways to understand that.


And it’s funny.  And it’s more transparent than something like goodwill.  


Right, it’s much more transparent.  You can measure it. Back in ’86, a guy named Appleton talked about data, the data we create like in normal operations or the data we might buy.  Once we start combining that, putting context around it, asking questions, he said it becomes information. Once we start building models around that and making decisions around that, then it becomes knowledge or intelligence for our business.  And we work in the intelligence space as well at Elder Research, and we know the value of that data and increasing value of that data. So it’s one way to think about it. It was actually a Gartner person wrote a book last year called Infonomics.  And I’m sorry I can’t pull the author’s name out of my head.  But it’s a great book, and it gives you some ways to measure data as an asset.  Measures that are used by the M&A community and things like that. And so, I think they are good ways, and I’m encouraging the companies that we’re working with to begin thinking about that and measuring data in that way.   


Info what?




Infonomics.  So, tell me about your business; tell me about Elder Research.


Elder Research, we’ve been around a little more than 25 years.  So, we were doing this thing we called data mining that’s now got this new cool term called data science for a long, long time across a lot of different verticals and have gotten a lot of experience.  We actually started out building hedge fund trading models. That’s a hard place to make money and to survive, right?  


You go to be right.


Got to be right.  And you got to know that you’re right.  And it’s one of the things that we evangelize.  So much of data science today you see done in not a disciplined manner.  So, coffee’s good for you; coffee’s bad for you. Wine’s good for you; wine’s bad for you.  So you just drink both, and you hope they balance out.  


I do.  If I’m right once and wrong once, maybe to your point, but average I’m perfect. 


Average, I’m good.  I’m going to the mean.


That’s right.   


So, anyway, so I think that that part of it is really important.  So then, what happened in 2001 after 9/11, our founder, Dr. John Elder, was really well known in the space, and the President asked him to come sit on a committee to look at how our intelligence community could share data better.  So you now have the Office of the Director of National Intelligence, who is supposed to bring data together from our intelligence agencies to make sure we don’t miss another 9/11-style attack. Well, that got us into the intelligence world and a large piece of our business now is in that space, trying to help with counter-intelligence and insider threat and things like that.  Then, we expanded into the federal civil government, working for places like the IRS, FCC, Health and Human Services…


Highly regulated spaces.


Highly regulated, Department of Labor, trying to help them with some of their problems and some of the things they need to tackle so that we can use our tax dollars in a very efficient way.  So, that’s a little history.


Give me your favorite customer story.


Wow!  We have this thing called recency bias.  So I’ll give you a recent one. I can’t really reveal the client, but what was fun about it and ties into what, I think. the questions you’re getting at and digging around here is they asked me to come in and help them with a long-term analytic strategy plan.  Now this is a company that had sort of put together an analytics team, hired 40 people, and then said, “What do we do with them?” Which is exactly opposite the way I would approach the problem or encourage them.   


Ironically, big companies that do it this way.  


Yeah, yeah, they try to figure it out as they go.  


“We need AI.  Let’s hire a bunch of people, and then we’ll figure out why.”


Yeah, and then we don’t even have the business questions, and the people are doing this, and they start having attrition and everything, you know?  And so, I said, “OK, well.” They shared with me what their two-year plan was. And they said, “We got the two-year plan. we think, down pretty well.  We want you to help us with three years. Tell us what technology we should use and all this. And, by the way, you have three days to figure it out.”


No problem.


I said, “OK.”  Well, I’s spent some time with the company and spent some time in their operations to understand it a little bit better.  And so, I really thought about this, and I really thought about it from the technology point of view. And here’s what happened.  If you think about technology, and we’re in it all the time in data science: GPUs, MS processing, natural language processing, and those are some of things that we’re interested in.  What we thought was state-of-the-art four months ago has been surpassed four times in that four-month period. So, what I actually went back to them and said was, “I think you’re focused on the wrong thing.  Don’t focus on technology. Focus on what analytics can do for the business in the three- to five-year timeframes.” So, as an example, one of the things in their two-year plan was self-serve: how do people come in and get some of our analytics and data in a self-serve model?  So, I said, “That’s good. That’s a good two-year plan. Three-year plan: instead of deciding what technology ‘cause it’s going to be different by the time you get there, let’s look at how do you turn that into what I would call instant awareness. So, instead of self-serve, you’re pushing now information that, based on your analytics, you know they should be thinking about.  Think about how that would work in a health care space. It could be amazing, right? And so, now they’re self-serving on things that you’ve already given them information about or a direction you’ve already pointed them in. So, rather than thinking of it through a technology lens, look at it through a business lens and say, ‘What should we be doing in the business in three years?’  And then, you’ll be better able to select a technology as it comes along during that period, right?” So. That’s it.


That’s super interesting.  Thinking about the space in general, it feels to me like we are, it feels to me like we are entering into like a big uplift in businesses generally becoming more analytics in their decision-making process.  Is that what you’re feeling? Where do you think we are on that J-curve? Are we like at the beginnings of it? Middle of it? Cresting? 


I think we’re just at that knee of the curve where it’s really, really taking off, and they’re figuring out.  And part of that is because there’s a cultural change that has to happen, all right? And people have to think that way.  And a lot of it is too, we’re overhyped. And I love technology. Don’t get me wrong. And I’m not trying to slam technology or product providers.  But what I’m seeing is, look, they’re out there saying, “Hey, I’m going to give you analytic platforms and things are going to be wonderful.” But, if we hadn’t defined that business problem well, if we’ve just bought a platform and hired a bunch of people and we don’t even know what business problems we’re going to solve or even alignment with our mission and strategy, then we’re going to fail fast, you know, and hard.  And it’s not going to be pretty.  

And then we start getting the data organized and together and visible to everybody, transparent.  Then the data science. We really have great tools in data science to be able to build models once we have data and a problem to solve.  And then, the really hard part that we need to think about is how do we put it into somebody’s hands so it fundamentally impacts their decision process on a day-to-day basis, right?  And we don’t think about that enough. We think, “Oh, we just put it in a BI tool or whatever.” But now we have dashboards of dashboards of dashboards, you know. And nobody looks at them.  So, we hadn’t thought about that delivery process that says. And if you think about analytics, the whole purpose is to impact, to give better information into a decision process. If we haven’t done that, then we’ve failed.  But part of that is delivering it to you so that it makes absolute sense to you; it’s intuitive to you; and I’ve anticipated the next three questions you’re going to ask, right? So then, you can ask those questions and get some confidence in the data that you’re seeing.  But we have to do those things well, and we don’t think about them enough. And then, beyond that is how do I begin to get people to adopt and change over to that.  


This is an exciting time.  


Yeah, it’s very exciting.


We are probably going to get run over.


Yes, we are.  There are people all around us, making lots of noise.  Oh, my gosh.


It’s terrifying.  If somebody wants to get in contact with you or someone else at Elder Research, how would they do that? 


They can go to the web and look for or  And either way, they’ll find us and jump on our website. I’m listed on that website.  Everybody else is. We love to get contacts in, and happy to talk to people. 


Gerhard, thanks for being on the Happy Market Research Podcast.


Super, thanks for having me. 


Everybody else, I hope that you will take the time to screen capture this, share it on social media.  Really appreciate your time and attention. As always, have a wonderful day.

PAW 2019 Podcast Series

PAW 2019 Conference Series – Brian Shindurling – Big Squid

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Brian Shindurling, VP of Marketing at Big Squid.

Find Brain Online:



Big Squid


Hi, I’m Jamin, and you are listening to the Happy Market Research Podcast.  We are live at Predictive Analytics World, Marketing Analytics World, and many other worlds.  My guest right now is Brian, VP of Marketing at Big Squid. You guys have the coolest booth, I think, on the show floor by the way.   


Oh, thank you.


The hot pink’s great.  I have a kick-ass sticker that I’ve added to my bag.  Thanks very much for the tchotchke stuff.




Tell me a little bit about the company.


Cool, so, we’re Big Squid.  We’re based in Salt Lake City, Utah.  We’ve been around for ten years actually.  The company’s kind of evolved out the marketing and analytics world.  We’ve done a lot of business intelligence consulting in our years past.  And, really, kind of the genesis of where we are today was we were consistently hearing a lot of the same challenges with our customers where leveraging business intelligence for an analytics environment, you’re building really interesting and cool dashboards all the time, but you’re always looking at data in a historical context, which led our customers to the next questions, which is “What’s going to happen?  “Why is it happening?” and “What can I do about it?”     


I mean that’s the Holy Grail.


Absolutely.  That’s right.  So, a couple of years ago, we launched our product we call Kracken.  It’s an automated machine-learning platform.    


Bad-ass name.


Thank you, thank you.  And the approach that we’ve taken is to integrate with the analytics infrastructure that BI analysts are using every day.  So, most of your enterprise data warehouses that are on the market today, most of the major business intelligence platforms…  We’re able to round-trip data in and out of those environments where we’re basically just enhancing it with predictive metrics to give analysts a little bit better idea of what’s going to happen.  


That’s really cool.  What kind of data are you dealing with?  


It depends.  We deal with all kinds of different data.  We’re kind of a horizontal play. We work with companies across basically any vertical that you can think of.  The data that we play with is always structured, again coming out of kind of that business intelligence environment.  


So it’s been cleaned.


It’s been pretty well curated, pretty well cleaned.  This is data that has been used or is being used for reporting on a day-to-day basis.  So we’re lucky in that sense; there’s already been some thought behind the business questions that we’re trying to support with analytics.  Yeah, structured data and set up in a way that it’s being used in reporting environments.   


Who is an ideal customer?


Good question.  So, our ideal customer is the BI-analyst or data engineer, those that are leveraging these platforms like a Snowflake and/or a Tableau, Looker, Click (Places where they’re leveraging data on a day-to-day basis to derive insights and then reporting on and telling stories to their executive stakeholders about what’s happening in the business and what do we think is going to happen, how should we be thinking about making things better.  But they haven’t really been classically trained on data science and machine learning in the past. So what we’ve done is we’ve created a platform that enables them to very easily navigate towards that concept that Gartner calls a “citizen data scientist.” So, leveraging an automated platform that really brings the R and the Python, the math, and the stats that most data science practitioners have in their back pocket to the analyst who is more closely aligned usually with the executive personnel, the stakeholders who are making decisions on a day-to-day basis.  


There’s been a lot of movement not just in this space but across the board like in primary data and others where it’s like this democratization of access to the actual insight, which prior was almost impossible because it required, at a minimum, some advanced math and stats, which is like Master’s level.  And now, it’s like you’ve got these solutions that are allowing the common folk, as you said Gartner cited correctly, the citizens… So, when you kind of frame things out a little bit more, is the buyer the analyst inside of the organization? Or is it happening at the CTO level? Who’s the one that’s sequestering the budget? 


Another great question.  It’s an interesting space.  This is absolutely an emerging market; it’s red hot right now.  What’s fascinating is most often companies haven’t budgeted specifically for… 


It’s like a new dollar, right?


It’s a new dollar, absolutely.  That’s a good way to describe it.  So for us, we take an approach where we try to create champions, our actual users, people who are excited about being able to expand their skill set to have a bigger impact on their organization, to make a name for themselves.  But in almost every occasion, we have to navigate our way up to executive level and, most frequently, C-level sponsorship in order to reallocate budget into the environment where they can make an acquisition of a product like ours.   


Yeah, totally.  Is the initial sales strategy a little bit like a B2C.  I know you’re not connecting in it at that level. But does it feel a little bit more starting in the trenches of the organization?


It can be, yeah.  I like to think it the way we approach the market as kind of top-down and bottom-up.


Yeah.  Both directions?


Both directions, yeah.  And, if we’re lucky, we meet in the middle.  We have an informed executive, and we have a champion who knows exactly how they want to leverage it, and they’re pushing for the insights that they can build. 


So, with all the success that you’ve had (this next question is going to be kind of hard to answer) but I’d like you to pick one example of a project where it was just your favorite.  You connected with it; it seemed like it went really, really well; clearer kind of view of how people are using you.


Yeah, OK, cool.  I’d say for me my favorite customer story is probably that of Skullcandy.  For those who don’t know, Skullcandy is a very innovative player in the audio space, kind of a life style brand.  They build headphones of all shapes, colors, and sizes.   


I have some.


OK, good. I hope you enjoy them.  The story with Skullcandy is they were trying to leverage some kind of predictive analytics to inform the way they’re developing their products.  So, what they were able to do, actually leveraging some NLP earlier on in their data pipeline, they were able to call out key words in customer reviews from places like Amazon and things like that.  So they were getting an understanding of, from a commentary perspective, where are things failing. If we have a one- or two-star review, what are the key words that are being used over and over again.  We took the outcome of the NLP model and started to feed it into a supervised machine-learning model with all of the attributes around specific products, new products going to market. And the cool part about their story is they’re able to now predict what new products that they’re launching are going to have failures and where.  So, before they go too far into market with a new product, they have the opportunity to retool, improve the quality of their product, increase the customer experience, make a better product, improve the user’s experience, make everything feel better, and lower their warranty claims costs overall. So they’re having to deal with less claims coming inbound; they’re having to deal with less returns and shipping costs and sending free product, and all those types of things.         


That’s really cool.  That is really cool. I love that.  So, you said… It sounded like what they were leveraging was existing reviews like Amazon-type, Yelp-type stuff, right?


Yep, exactly.


So, you’re able to incorporate things like social media as well as primary data sources, any type of data. 


Sure, yeah, yeah.  If the data is being captured as long as it can be piped into a data warehouse type environment, we can ETL things together.  It becomes a really rich data set that’s fit for machine learning.  


That’s really cool.  So, what do you think about the show?


The show’s been outstanding.  It’s been really, really fun, actually.  We’ve had a lot of fun dealing with a lot of data science practitioners and kind of getting their insights on where and how they might leverage something that’s a little bit more automated, where their pain points are, and how these emerging technologies can speed up their workflows and, ultimately, enable them to do more.  We’re also had some really fascinating conversations with analysts who are reading and learning and trying to understand this data science space. I think that’s why a lot of people have come to this event; overall, it’s just to learn more about what is this predictive analytics world, so to speak. So it’s been really fun. I feel like our message has been resonating really well, and it’s been a very productive show for us, absolutely.     


I think the attendees here are…  I mean you have this really nice cross-section between business professionals (these are like buyers) and then also executioners (the people that are actually doing the analyst side of things).  So, you’ve got a very nice representation across those two. And then, you got a lot of big brands, right? Big budgets are represented as well. And then, the show floor feels very friendly.  


Yeah, I agree.


Yeah, it’s kind of nice.


Yeah, it’s been really fun.  We’ve had upwards of 15, 20 people hanging out in our booth at any given time.  Wish we had some Polaroid action going on. Come to the booth and get a Polaroid.  And people have just been kind of light-hearted and having a good time. It’s been fun.


That’s great.  Brian, if somebody wants to get in contact with you or sales at Big Squid, how would they do that?


Probably the best bet is to visit our website  Obviously, fill out a contact form there. Really easy to find.  You can also check out a free trial on our website as well. Sign up and get a 14-day trial and get kind of a feel for what automated machine learning looks like and feels like and what types of insights you can derive. 


And, of course, we’ll include that information in the show notes.  Brian, thanks for being on the Happy Market Research Podcast.


Thank you very much for having me.


Everybody else, appreciate your time.  This show is coming to an end. If you found value in this episode like I did, I hope you will screen capture, share it on social media, LinkedIn, Twitter.  I’d greatly appreciate it. Have a wonderful rest of your day.  

PAW 2019 Podcast Series

PAW 2019 Conference Series – Bob Selfridge – TMMData

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Bob Selfridge, CEO and CTO of TMMData.

Find Bob Online:





Hi, you’re listening to the Happy Market Research Podcast.  I’m Jamin, your host. Today I’ve got Bob with TMM Data. We are live at Predictive Analytics World, Marketing Analytics World, Health Care.


There’s a giant list…


There’s a giant list.


of events.  Email is in there somewhere.  Deep Analytics, there’s lots of good stuff here. 


The email one to me is kind of interesting.  I’m like, “Gosh, email’s dying.” 


You think?  You think so?


Yeah, open rates are decreasing.  There’s actually divisions of companies now that aren’t even responding to emails.  They only use Slack and other…anyway. So, Bob, thanks for being on the show.   


Thank you for having me.  I appreciate it.


Let’s start out.  Talk a little bit about TMM Data.  What do you guys do


Sure.  We’re about a 13-year-old company.  I started it back in 2008 on my front porch, closed in the building, brought people in:  one of those good, old-fashioned, kind of bootstrap things. We really started TMM was originally Track My Marketing.  So we were all about channel marketing and, ah… Well, at that point, it was multi-channel; now it’s omni-channel ‘cause we have to have cool new words.  But being able to set this up so that you could do reporting.  


That was like early pre-social media focus market too.


Right, right.  2008: I mean it was around, but nobody was really diving in.  It was more of a fun thing to have that us old folks were starting to play with more so than day-to-day operational thing that it is now.  But we started measuring that around 2012. My phrase is, as I like to say, “It’s about the data dummy, not about specifically marketing data or analytics.”  And we really refocused the company to becoming a data-integration company. We saw, while campaign management and marketing analytics is still very the core of what we do, we find that a lot of folks are still struggling with simple things.   They’ve got spreadsheets coming in email; they’ve got, of course, 7,000 marketing technologies out there, floating out there in the atmosphere that they need to pull that data in and merge it and meld it and marry it and all the cool phrases we use now.  So, our goal is to make it easier for analysts, whether they be predictive analysts, marketing analysts, financial analysts, just analysts that are fighting. We just did a survey with Digital Analytics Association here last year. People are spending 40% to 60% of their day just copying and pasting, cleaning data to start doing their work.  And our ultimate mission at the company, our official mission statement is “Meet data needs painlessly,” very short and sweet.   


Oh, I love that.


But we want to be able to allow analysts to come in at 8 in the morning and start doing their job instead of spending four to six hours cleaning up the data to then move to the next step to start doing their job.  So, that’s kind of ultimately our goal in life.


An ROI on that is really easy to get to.  That’s the nice part.


Well, it is.  Unfortunately, for us and unfortunately for some of the analysts, the really good analysts, end up doing the extra work and working into the wee hours.  And the senior management still get all their reports because they just put in extra hours. So the ROI to the individuals on the ground, certainly the feet on the ground really know that there’s a great ROI to it ‘cause it saves them a lot of time.  Sometimes, although it’s getting much easier in the last two years, selling it up because they’re going to want to get my reports now. And it’s like, yeah, but you could get double the amount of reports, and better reports, had you not had to spend your whole day copying and pasting.     


And the reality is there’s such high turnover among the people that are generating those reports.  And if you can have a little bit better quality of life… To your earlier point, even if… ‘Cause cleaning and structuring data sucks; it’s not very fun, right?  It’s very grindy kind of work. And the less of that you can do, the more interesting stuff you can do.   


Absolutely.  Wow, it’s funny you say that because we actually like to say we like to be the plumbers, is what we want to do with our platform.  So, like real plumbers, it’s not real friendly work; nobody likes it, but everybody has to have it. So, that’s ultimately where we want to be in an organization.    


Oh, that’s great.  So, who’s your ideal customer?


Traditionally, it’s been marketing departments for large enterprises or medium-to-large enterprises.  Some of our larger companies we work with like T-Mobile and Sony and Comcast, they’re marketing organizations that have a great IT technology environment where they work but the marketing information that they need access to is not a real high priority.  You know when there’s frustration there I say, “Look, the engineers at Comcast are keeping the internet running for about 75% of the country. They have higher priorities than the analytics data from yesterday that they want to try a new regression test on.”  So, what we want to do is fill that gap. We want to be friendly to IT because if we make IT uncomfortable with us or we try to shadow IT (those kind of things), it becomes a real problem for the organization, and it causes hardship for the folks we’re working with.  So ultimately, we want to be IT-friendly, but we want to be able to fill those gaps that IT can’t fulfill for marketing or the business side of the shop. And we’re finding more and more as BI departments are coming of age, we’re seeing that a lot in the last couple years where the middle ground either an IT person’s been assigned to run an analytics/BI group (‘cause we got to make the business folks happy) and try to fulfill the needs of both of those worlds at once.  That’s kind of where we’re coming in, and it’s been a good model there, which is kind of our second group, so to speak: so marketing departments directly and now the new BI/analytics groups that are being created in their centers of excellence, etc., throughout the industry. We work with them to be (I hate to say “middle ware” ‘cause that’s a bad word, or it was traditionally) but uh…   


I’m sure there’ll be a new fancy way, word.  Don’t worry.


We’ll come up with something.


We will, absolutely.  We did clouds, one of my big pet peeves, terms.  We had remote servers before. But anyway… so, do you have a favorite customer story?


Yeah, probably one of our oldest customers is Comcast.  Of course, for those in the country that aren’t aware of who they are, they’re Xfinity now that’s what they kind of rebranded.  But they’ve got phone services now; they have mobile services; they’ve got VoIP for telephone; they got internet security systems, and, shockingly, they have cable TV still.  That still exists too. And one of the challenges they’ve always had is trying to figure out what is actually happening with some of their customers. A really cool thing just from simple data-integration perspective that we’re able to do with them is one of their struggles has always been cost of customer services, right, on those.  And one of the neat little projects we were able to do was: We get notified when a new customer they call it connected, when I new customer gets plugged in. So, we have access to those records. So, we’re able to take that information, merge that with the service records to know when a truck physically went to a house and plugged somebody in.  And then the third piece is when did that customer log in and get their magic Xfinity account so they could start doing their cool stuff on Xfinity. Those three pieces of information started saying things like from order to logging in; that kind of told us the maturity level of the customer they found. That’s where we found. So, somebody the day they got their internet if they had their account within 6 hours, they’re probably pretty technically savvy.  And it just so happened that those people, all of their service requests were bottomed out; they were self-served; they didn’t want to deal with it. The folks that took six days became another level, and what they would start doing is immediately the very first time they opened their internet up, when they went in, they would put them to the Help page on how to set up their account so that they could move them along.


That’s huge.  


So, you knew the segmentation.  And then the people that waited 30 days, they actually were starting to work through processes to do like a follow-up call because until they have that account there’s a lot of things they couldn’t do.  And, honestly, they couldn’t be tracked to know whether they were good customers or bad customers. More importantly, were they self-served or did they need extra help?      


I love that!


If it had been more than 30 days since they had their account (This is my personal favorite), we actually did a segment so that the very first page they came to was not the help-tech stuff.  They went to how-to-program-your-remote page ‘cause if 30 days to get an account, chances are they were struggling with how to even get the remote set up to watch TV. So that was the first thing they saw versus the knowledge base.  And being able to do that… And they were able… The mix of all of that reduced cost by almost two million dollars a month in services and calls to support centers.     


And you know, by the way, the most important thing is the customer experience is better. 


Correct.  The customers weren’t frustrated because most of their calls from users was, “How do I program this stupid remote?” because they’d already tried and been frustrated.


It’s very frustrating.  Yeah, exactly.  


It was just a really neat story.  And it was just a couple different data points from four different data systems, right?  None of them talked to each other because they didn’t need to. Normally, you order online; a ticket goes to a trucking system; the contractor goes to a house, plugs it in, and says, “Done.”  And none of that needed to be aligned, but once you start aligning it, you can do cool stuff.  


When you think about like…  So, there’s different business owners there that you’re having to integrate as well, right?  It would be easy if it was just data, wouldn’t it?  




But the human part is…  So, you must have had like a champion internally at Comcast in order to…  that could navigate those channels successfully.  


Well, yeah.  We actually were very lucky:  We had a person that we worked with for three or four years there, who left the company, went to chase something else, and then got bored, and turns out we had a position; so, we were able to hire her.  So, we didn’t hire her from a customer. We were trying to help… And she went back to serve the customer. 


So she knew…


So she knew the players to go chase down.  We had three or four champions: one from each of those channels of data.  It was very helpful to have that road map, so to speak, to go chase. So we got lucky in that case.  But there was a corporate sponsor that said, “Look, there’s only so much… With the competitive nature of the internet as it is, the margins there are not very high ‘cause they’re all competing very heavily between Fios and Comcast and now MyFi and others coming out.  Satellite’s gotten much better, much bigger recently. So they got to figure out where they’re going to make more money. Sometimes, it’s not about making more money; it’s about spending less money. We had a champion in the customer-success department, who was new, who said, “Look, there’s got to be a better way to do this ‘cause we’re just losing money hand-over-fist for things that seem unnecessary:  fighting with people over remotes. Why can’t we just get them what they need, you know?” Having forward thinkers like that, and that’s tough at a lot of organizations.  

A company the size of Comcast or Sony, they clearly have really good people they can recruit.  At the conference here, as we’ve talked to folks throughout the week, it’s been interesting ‘cause it really depends on the maturity level of who’s running your analytics department how deep they can go.  Someone, “I have my marketing data; I want to throw it somewhere and have pretty reports come up and I don’t know what to look for. So give it to me.” And others are coming to us with, “I work for a university, but I’m in the research lab for the medical department, and I’ve got all these specific needs.  Please don’t tell me you’re just giving me reports. I need to be able to play with the data, look at the data, work with it.” And we try to be more of a blank slate, which sometimes is not good for immature – not “immature” – new to the industry, right? Some industries are just catching up, and they’re just trying to figure out how their ad spends are.  That’s where they’re at. So we tend to work with the more mature, larger enterprise that can go deep.         


That’s makes a lot of sense.  So, the conference’s been good?


So far, so good, yeah.  It’s been a neat mix with the different folks.  It is a little tough when you see somebody up, you got to kind of analyze, “OK, which conference are they attending?  Which portion of the conference are they attending?”  


Eight conferences.


There’s eight different people.  The email folks have a different status they want to talk about.  Then the predictive versus the deep analytics want to go super technical, etc.  I’m very lucky: health care, they want to go security and privacy. Finance is like nothing can ever leave the building, you know.  So it’s been… That’s tough at a conference like this ‘cause there’s a great…, but it’s also a great mix. Sometimes people like to play “Stump the Chump.”  That’s the word I like to use. I welcome it. Yes, I really do ‘cause you get those deep analytics and predictive folks that are like hard-nosed coders and PhDs in data science, and they love to ask that kind of stuff.  And I’ve got a couple new acronyms this week that I had never heard of. So I got to look them up and learn something new; so, that was good. I hope they come back ‘cause I promised them I’d look it up and give them an answer.  So, yeah, it’s kind of fun. And then I get to play “Stump the Chump” ‘cause I had one super smart guy that… PMML, I think is the language: Predictive Modeling Meta Language or…            


I’ve never heard of it.  


I had not either, but it was a PhD from a university.  So, the next guy was another PhD, and I’m like, “So, have you ever heard of PMML?”  So I got to play it back and, when he said, “No,” I felt much better about myself. Turns out it’s just another format in XML.  So there’s really no magic to it. I’m talking geek now but…   


I love that.  I love that.  


“Stump the Chump”.


It’s so funny.


We’re still using SQL, right?  It’s amazing to me, like 30 years.  Is that what it’s been? It’s been a long time.  


Well, yeah.  And then we created a whole industry of no SQL so that we could then build apps that let you do SQL and no SQL because that’s what we needed to do.  And my poor marketing staff are very confused when they said, “Well, how do you do no SQL or how do you do SQL with the no SQL?” She like, “I thought was no SQL.”  But we get there. it’s tough. We like to make up new terms. That’s always good.  


If somebody wants to get in contact with you at TMM Data, how would they do that?


Simplest way – go to our website, www.TMMDATA or call our phone number, 855-55FORDATA.  See, easy, that’s kind of fun. 


I like that.


Feel free to visit our website.  We’re actually also do several other shows.  So you’ll probably catch us at other shows as well.  


And we will include that information in the show notes as well so people can have at their fingertips a click to the website.  Bob, thanks so much for being on the Happy Market Research Podcast today.


Thank you very much.  It’s been great talking to you.


Everyone else who’s listening, if you found value, please, please, please take the 30 seconds to screenshot this and another minute to post it on LinkedIn, Twitter, wherever your social personas exist.  Be greatly appreciated. Have a wonderful rest of your day. 

PAW 2019 Podcast Series

Ep. 223 – PAW 2019 Conference Series – Allison Swihart – Syndetic

Welcome to the 2019 Predictive Analytics World (PAW) Conference Series. Recorded live in Las Vegas, this series is bringing interviews straight to you from exhibitors and speakers at this year’s event. In this interview, host Jamin Brazil interviews Allison Swihart, CEO and Co-founder of Syndetic.

Find Allison Online:





Hi, everyone.  I’m Jamin Brazil, and you’re listening to the Happy Market Research Podcast.  We are live today at Predictive Analytics World. I am standing with Allison, the CEO of an up and coming company Syndetic.  You can find information on them at Allison, thanks for being on the Happy Market Research Podcast.


Thanks for having me.


Well, let’s start with what do you think?  I mean this is Day 2 or 3.


Day 2.


Day 2 of the event.  What do you think about the event so far?


I think it’s been wonderful.  It feels like there’s a really good energy here compared to some of the other conferences that I’ve been to recently.  The audience is slightly more technical, which is great for us. And I am getting a lot of good feedback on the talks. And I think that the decision to kind of combine the different PAWs together into one Mega PAW.   


Eight PAWs


Were there eight?  There have to four PAWs.  What kind of animal is an eight-pawed animal?  I mean… There should be four PAWs. Anyway, yeah, I think it was a good decision because everyone who came for the marketing side versus the kind of deep machine learning side is mingling together, and you’re getting lots of great conversation.    


It is actually interesting seeing that convergence of…  One of the terms that a previous CEO I had on the podcast said is “diversified data” or “data diversification,” meaning…  You see this in executive teams. So executive teams that have diverse gender, ethnicity, whatever, they outperform non-diverse groups.  It was interesting having him creating that connection with respect to different data. And I hadn’t actually thought of it in the way that you just articulated it, but that’s actually pretty relevant also where you’ve got broad disciplines that are now being combined into a single event, which is kind of cool.


Yeah, yeah.  And having people of different titles and at different levels in the organizations, I think, is really important for conferences.  I know there’s a lot of summits where people kind of focus on just the CIO level, but I think at this event, in particular, we’ve talked to a bunch of people who are analysts, who are doing the actual work, which is really important to bring their perspective of the individual contributor, not just the manager.  The manager might be making the decision to purchase one piece of software over another, but you need the perspective of the kind of “boots-on-the-ground” in order to, I think, make the best decision. So, I think they’ve done really well.     


So, you have a relatively new company.


Brand-new.  We just launched this year.


Congratulations.  What month?




Awesome.  So literally brand new.


Literally brand new.


Tell us what your company does.


So, we are a virtual data warehouse, which means that we allow you to leave all of the data where it lives:  in databases, spreadsheets, third-part services that you access via API, cloud-hosted services like say you use SalesForce.  You have an HR system; you have an accounting system plus you have a non-premise Oracle database. Leave all that data where it is; we virtualize it into a data warehouse as if it were in the same physical location.  So you can query against it, using plain SQL, and define the right slice of data for every project you want to do, and then you can syndicate out the results in different ways. So you can take a slice of data for say you’re building an e-commerce website, and you want to expose a slice of data to Shopify, but you have data in different underlying systems.  Instead of having to copy the data all into one place, you can through Syndetic virtualize it, syndicate it out, without having to do the copying.    


That’s pretty cool.  


Yeah.  It’s actually been around…  So, data virtualization is not new.  It’s actually been around for a long time.  It used to be called data federation, but what we bring that’s different is that we’re the first web-based, cloud-native virtualization system, which is really important because it allows the business side and the IT side to collaborate inside of the same system to define that slice of data,  So, if you have, for instance, metadata that is living in a spreadsheet somewhere and it’s important that it live in that spreadsheet. Like we take a very business-friendly approach. We love spreadsheets. We think that there’s a reason that Excel continues to exist and everyone uses it. If there’s metadata living in that spreadsheet that you need for a specific purpose, you shouldn’t have to copy that into a data warehouse.  You should be able to use the workflow that’s appropriate for your business and still get that project done. So we cut time to deployment in half or more. And your analytics are not dependent on the last batch job that you ran to copy the data. There’s also a whole host of other benefits: security, abstraction…   


So, you’re not actually copying that data and moving it into…




You’re just querying it based on where it resides.


And the queries get pushed down to the underlying data sets.  You can, if you want… We give you the option to materialize the view, it’s called, or cache for performance reasons.  But what we say is that should be project specific.


That was my next question.  There’s a lot of overhead.


Yes.  So, well, actually the performance is very good even in the virtual, non-cached version.  But, if you’re running a query on a 850-million-row database, you might want to cache that, and you can just with the checkbox.  So we make it very easy. 


That’s awesome.


Yeah, thank you.


Did you found the company?


Yes, I did.  Myself and one co-founder, the two of us.


Two of you.  It’s terrifying, right?  


Yeah, it’s terrifying although it’s very exciting.  And the two of us ran a previous tech company together.


So good relationship.


Yes.  COO and CTO.  So it doesn’t feel entirely foreign.   


You’ve ridden the ride before.


Yes, yes, the crazy ride.


Who is an ideal customer?


An ideal customer for us is a company between 500 and 1500 people, big enough to have data in lots of different places.  Oftentimes these companies are going through M&A. And, when you do that, you’re acquiring not just operational overhead, maybe a revenue stream, but you’re also acquiring data sets.  And IT integration is a nightmare. So we provide a really good option for those types of companies. We’re industry-agnostic. Based on our personal backgrounds, most of the companies that we’re working with now come from finance or we also have some customers in the insurance and higher education space.  But really any company can use Syndetic.


It’s funny you say that, M&A.  So, the last company I was CEO of, there was eight acquisitions.  It was like this big kind of rollup sort of typical… And the data management on our side – because there was literally eight distinct data – and ironically all of them were data companies. 


Oh, that’s interesting.


But it made it even more exciting.  So, having a unified view of performance and invoicing and customer usage, all that kind of stuff, would have been…  Like we spent a year and hundreds of thousands of dollars, trying to do the integration (actually, it was over a million dollars), trying to do the integrations, creating that sort of like – BI is not the right term – but overarching view, and it was a pain in the ass.


Yeah, there’s a phrase that gets thrown around very casually, is the “single source of truth,” right?  You want a “single source of truth” for your data. Our hypothesis or our opinion on that is that there’s never going to be a single source of truth.  It should be project specific. And you may want to use, for instance, if you saw a physical good… The name of your product that you want on your e-commerce website may be different than the name of the product that you need to use for regulatory filing or that you need to submit to a trade partner. And that might be for business reasons like you want to market your product in a different way than you care about putting onto a report, but it might also be for technical reasons.  You might be sending data to a trade partner or inputting data into a system and there might be character limits, right? So you by virtue of the way that you’re interacting with your other partners in other systems, you can’t use the same single source of truth. And so, we say that’s OK; you shouldn’t have to. There might be a product name that’s living in one system that’s different than a product name in the other system. And differentially, you want to use different versions of the truth at different times, and no one is better than the other.  


So, we’ve seen a lot of growth in data, I mean, volumes of data, and it’s only expanding.  What do you see as some trends that are materializing right now like the last maybe three or four years?  And then, where do you think the industry’s headed?     


So, I think a really big trend and also on the people side…  A big trend is that companies are starting to hire data engineers and data scientists, and they are throwing them into or giving them a mandate of “bring predictive analytics to our organization,” but they’re not really preparing them with the tools that they need.  And so, I think they’re putting the cart before the horse a little bit in that they are so excited about the kind of end-state objective: “We’re going to spin up a bunch of Hadoop clusters; we’re going to have Big Data; we’re going to use machine learning; we’re going to do AI.”  And you can’t really do that without having all your ducks in a row on the integration side. You should buy the systems that are appropriate for the amount of data that you have is another big, core tenet of ours. So don’t buy a system that is meant for pedabytes of data if you don’t have pedabytes of data.  It will take you much longer to deploy; it’s going to be much more expensive; and you can do that later. Buy a system if you have terabytes of data or gigabytes of data. If all of your data is structured, don’t worry about fancy AI on unstructured data.   


Not helpful.


Do the simple thing first.  And so, we think Syndetic is really well-positioned for that kind of incremental growth.  


…and supporting it.  As you look forward, what do you think, where do you think the industry’s headed?


So, I think, for this conference in particular, talking about predictive analytics, I think that we can separate out a little bit the predictions and the analytics.  I think right now we’re still on the analytics phase where people are figuring out what to do with the data they already have. And it’s a little bit backward-looking.  “Let’s take data. Let’s figure out how to apply some statistical models on it to make better decisions.” I think we’ll get to the predictive part over the next four to five years where we can actually do things like decide how to allocate resources based on who we think our future customers are going to be, not necessarily based on who our customers are now.


Oh, that’s very interesting.  If somebody wants to get in contact with you, Allison, or somebody else on your sales team, how would they do that?


So, please go to www.Syndetic, and you can schedule a demo there or you can write to us at


Perfect.  Thanks so much for being on the Happy Market Research Podcast.


Thank you so much.


Everybody else, if you found value in this episode, please take time to screen capture, share on social media.  I really appreciate it and hope you have a wonderful rest of your day.