PAW 2019

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:

Email: tony.ayaz@geminidata.com

LinkedIn

Gemini Data Inc.


[00:02]

Tony, when did you start Gemini?

[00:03]  

We started the company in 2015.

[00:06]  

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? 

[00:19]  

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.

[00:32]  

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.

[00:41]

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.    

[01:47]

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?       

[02:12]   

Exactly.

[02:13]

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? 

[02:32]  

I wouldn’t say ingest, access those systems.

[02:35]

Got it

[02:36]

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

[02:40]

OK, cool.  So that bypasses some PIII?

[02:42]

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.      

[03:43]

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?

[03:56]

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.

[04:12]  

I actually think I saw that tweet.

[04:15]

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.      

[05:04]

Mind-blowing.

[05:05]

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.     

[05:58]

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

[06:03]

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. 

[06:51]

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…   

[07:03]   

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. 

[07:33]

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

[07:37]      

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.     

[08:23]

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.  

[08:32]

Exactly.

[08:33] 

So, who’s your ideal customer?

[08:36]

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.

[08:56]         

Got it.  Favorite customer story?

[08:58]

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

[09:02]  

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

[09:06]  

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.      

[10:21]  

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

[10:26]  

Exactly.  Thank you.

[10:27]

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

[10:32]

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

[10:45]   

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

[10:48]

Thank you for having us.

[10:50]  

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 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:

Email: satish.pala@indiumsoft.com

LinkedIn

Indium Software


[00:02]

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.

[00:22]  

Thanks.  Thank you so much.

[00:23]  

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?

[00:42]  

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.     

[02:04]

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

[02:08]

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.       

[03:07]   

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.  

[03:25]

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…?

[03:37]  

I love that term “operationalize.”

[03:39]

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.

[04:30] 

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

[04:35]

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.    

[05:56]

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…

[06:07]    

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.    

[06:38]

Yeah, for sure.  

[06:39]

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.

[07:44]  

Are you partnering with somebody to do the IoT?

[07:49]

No, we do it ourselves.  

[07:51]

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

[07:57]

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

[07:59] 

OK, got it.

[08:01]

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.

[08:15]

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

[08:20]

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.    

[08:33] 

Yep, makes sense.

[08:35]

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. 

[08:50]      

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

[08:53]

So, you could go right into the web portal www.IndiumSoftware.com 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.

[09:08]

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

[09:11]  

No problem.  Thanks, it’s my pleasure.

[09:12]  

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 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:

Email: ryohei.fujimaki@dotdata.com

LinkedIn

dotData


[00:02]

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

[00:15]  

That’s true.

[00:16] 

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

[00:19]  

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. 

[00:37]  

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.  

[00:52]

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.  

[01:01]

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.      

[01:12]   

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.   

[01:28]

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?

[01:46]  

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.     

[02:28]

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

[02:36] 

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 

[03:51]

Even with a better outcome.

[03:53]

Yeah, even a better outcome.

[03:54]    

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?  

[04:08]

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.   

[05:19]

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?   

[05:47]  

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.   

[06:55]

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

[07:01]

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

[07:06]

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

[07:13] 

Yeah.

[07:13]

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

[07:20]

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

[07:31]

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

[05:34]   

Yeah, thank you very much.

[07:35]

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 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:

Email: michael@thisismetis.com

LinkedIn

Metis


[00:02]

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.   

[00:17] 

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).    

[00:45]  

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.  

[01:15]  

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.

[01:30]  

How is that helping?

[01:32]

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.    

[02:01]

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.”     

[02:32]  

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. 

[02:40]

Oh, you do then.

[02:41]  

Yeah.

[02:41]

OK, got it.

[02:41]

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…”  

[03:12]

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.   

[03:52]

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

[03:55]     

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

[03:58]

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

[03:59]

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

[04:03]  

Oh, wow, there’s so many.

[04:05]

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

[04:09]

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.  

[05:03]

That’s unbelievable.

[5:03] 

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.

[05:12]

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

[05:18]

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.  

[05:33]

Got it.

[05:33]   

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.    

[06:22]

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

[06:26]      

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.”

[06:44]

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

[06:52]

I’ll let it slide this time.   

[06:53]  

Thank you, thank you.  

[06:54]  

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.  

[07:01]  

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

[07:03]  

Well, fingers crossed.

[07:05]

Yeah, yeah.

[07:05]

Only time will tell.

[07:07]   

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?  

[07:13]

Sure, so, one is our website:  Thisismetis M – E – T- I – S.com.  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 corporatetraining@thisismetis.com, and then my email is Michael@thisismetis.com  

[07:31]  

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

[07:33]

Great, thank you.

[07:34]

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 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:

Email: mcowell@quanthub.com

LinkedIn

QuantHub


[00:02]

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.

[00:13]  

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

[00:14]  

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…? 

[00:42]  

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

[00:48]  

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

[00:52]

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.  

[01:18]

Which is insane.

[01:19]   

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…  

[01:32]

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

[01:36]  

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

[01:44]

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

[01:50] 

Incredible timing, incredible timing.  

[01:52]

Literally, the whole time this has never happened.

[01:54]

This is amazing.

[01:55]    

OK.

[01:56]

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.     

[02:26]

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

[02:31]  

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.

[02:45]

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.

[03:04]

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.    

[04:04]

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. 

[4:18] 

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.    

[04:41]

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.  

[04:53]

That’s the hope.

[04:54]

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

[04:58]   

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.    

[05:09]

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

[05:15]      

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.

[06:09]

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?

[06:20]

Me too.

[06:21]  

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

[06:26]  

Yeah, yeah, definitely.

[06:27]  

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.   

[07:04]

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.     

[7:48] 

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. 

[08:14]  

Yeah, yeah, that makes sense.

[08:16]  

Uh, what do you think about the show?

[08:19]  

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.   

[08:39]

Yeah, it really is.

[08:40]

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

[08:43]   

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

[08:48]

But it’s been good.

[08:49]  

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. 

[08:57]

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

[09:02] 

Me too.

[09:04]

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

[09:05]

I know.  

[09:07]    

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

[09:12]

Well, you can go to QuantHub.com and we’re offering a free trial.  You can email sales@QuantHub.com, 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.  

[09:32]

So you got a nice spectrum there.

[09:34]  

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.  

[09:44]

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?   

[10:01]

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.  

[10:49]

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.

[11:01] 

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.

[11:24]

Totally.

[11:25]

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.  

[11:38]

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

[11:40]   

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.

[11:52]

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

[12:00]      

Yeah, yeah, definitely, definitely.

[12:02]

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

[12:09]

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

[12:11]  

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 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:

Email: mdocouto@altair.com

LinkedIn

Altair


[00:02]

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

[00:08]  

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.

[00:20]  

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.

[00:29]  

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.  

[00:37]  

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

[00:46]

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.         

[01:28]

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?  

[01:35]   

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.   

[01:57]

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?

[02:06]  

Absolutely.

[02:07]

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.

[02:15] 

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.  

[02:36]

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. 

[02:51]

Yeah, absolutely.  

[02:47]    

Datawatch or before.

[02:50]

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.   

[03:55]

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. 

[04:10]  

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.  

[04:46]

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

[04:52]

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.  

[05:10]

How long have you been with the company?

[5:11] 

Been with the company just over seven years now.  

[05:14]

So quite a while.

[05:14]

Yeah, absolutely.

[05:15]

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. 

[05:34]   

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.    

[06:14]

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.     

[06:49]      

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.

[07:16]

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?  

[07:33]

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. 

[08:19]  

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?

[08:25]  

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.   

[09:10]  

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

[09:12]

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

[09:16]  

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?

[09:21]  

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

[09:37]  

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

[09:39]  

Not a problem.  Thank you very much.

[09:40]

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 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:

Email: lcowan@cicerogroup.com

LinkedIn

Cicero Group


[00:02]

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.

[00:21]  

Thanks, appreciate it.  Thanks for having me.

[00:22]  

Yeah, of course.  You guys are exhibiting here.

[00:25]  

We are.

[00:25]  

What do you think about the show?

[00:27]

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.  

[00:50]

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

[00:53]   

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.

[01:24]

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.

[01:42]  

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.    

[02:04]

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

[02:07] 

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.

[02:46]

Do you have a favorite customer story?

[02:48]

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.

[03:00]    

I actually worked with them pre-IPO days.   

[03:03]

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.  

[03:44]

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

[03:45]  

Uhm, Eric Rasmussen.

[03:47]

I totally know Eric.

[03:49]

You do.

[03:50]

I know him well.  It’s so funny.  

[03:52] 

That’s great.

[03:54]

Yeah, that is…  Small world…

[03:55]

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

[04:02]

Yeah, ‘cause he was in the Bay Area.

[04:03]   

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

[04:08]

I think he was working out of Palo Alto.

[04:11]      

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

[04:17]

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

[04:19]

And I think he’s still there, right?

[04:21]  

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…? 

[04:59]  

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.  

[05:25]  

Totally.

[05:25]

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.”  

[06:00]  

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. 

[06:04]  

That’s right.

[06:05]  

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

[06:11]  

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. 

[06:27]

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.

[06:46]

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.     

[07:22]   

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? 

[07:30]

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

[07:40]  

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

[07:43]

You bet.  Happy to be here.

[07:44] 

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 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:

Email: kkallakuri@getdiwo.com

LinkedIn

diwo


[00:02]

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.  

[00:14]  

Quite a few.

[00:15]  

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

[00:24]  

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.    

[01:10]  

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?  

[01:24]

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

[01:26]

Fairly recent.  You have a favorite customer story?

[01:29]   

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.     

[04:05]

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?

[04:31]  

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

[04:38]

Oh, wow!  Totally different.

[04:39]

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.        

[06:08]

Wow!

[06:08] 

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.  

[07:08]

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?

[07:20]

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.    

[08:58]    

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. 

[09:22]

Absolutely.

[09:23]

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?

[10:04]  

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.  

[11:22]

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.  

[11:36]

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

[11:42]

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

[11:48] 

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

[12:11]

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

[12:14]

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

[12:18]

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 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:

Email: jtodd@wolfram.com

LinkedIn

Wolfram


[00:02]

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.

[00:14]  

Thanks for having me, Jamin.

[00:18]  

What do you think about the show?

[00:20]  

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.      

[00:50]  

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. 

[01:40]

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.

[02:39]   

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

[02:53]

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

[03:09]  

The autopilot is crazy!

[03:12]

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.     

[03:27]

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? 

[03:59]

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.  

[04:12]

My wife’s the same.

[04:13]

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.

[04:47]

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.    

[05:42]  

I agree.  

[05:43]

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

[06:04]

You’re doing great.  Keep going.

[08:04]

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.

[06:54]

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

[06:59]

That’s 30 years, folks.

[07:00]

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.

[07:10]   

20-year-old Ph.D.

[07:12]

15.

[07:13]      

15, I’m sorry.  My bad.

[07:15]

No problem.

[07:15]

That’s a big difference.

[07:16

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. 

[08:18] 

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

[08:25]

That’s right.

[08:25]         

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.   

[08:51]

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.    

[09:48]  

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

[09:52]  

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…

[10:31]  

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

[10:34]

Yes.

[10:35]

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.”

[10:45]   

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.”      

[11:07]

All right.  You’re saying Pro.

[11:09]  

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?”      

[11:59]

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…? 

[12:09]

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.          

[13:34]

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

[13:38]

That’s right.

[13:38]

The most important thing in the world.

[13:39]

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.

[13:52]  

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

[13:58]

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.    

[14:42]

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. 

[14:54]

It can be tough, it can be tough.

[14:57]

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.   

[15:14]

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.    

[16:53]

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? 

[17:03]

That’s a tough question.  

[17:04]

It’s actually really tough.  

[17:07]

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

[17:12]

Right, yeah, there you go.

[17:13]

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.  

[17:19]

The Nostradamus of our day.  

[17:21]

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.” 

[17:53]

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. 

[18:11]

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.          

[18:55]

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?    

[19:05]

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.    

[19:38]

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

[19:45]

You can email me at JTodd@Wolfram.com.  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.  

[20:04]

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.  

[20:10]

Thank you so much for having me.

[20:12]

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 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:

Email: james@decisionmanagementsolutions.com

LinkedIn

Decision Management Solutions


[00:02]

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

[00:11]  

Management Solutions.

[00:14]  

Got it.  You are chairing a track today.  

[00:18] 

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.

[00:24]  

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

[00:28]

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. 

[00:49]

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

[00:55]   

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. 

[01:05]

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

[01:11]  

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.

[01:31]

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

[01:34] 

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.   

[02:08]

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

[02:12]

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.  

[02:57]    

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

[02:59]

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.

[03:15]  

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

[03:25]

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.”  

[03:43]

Oh, that’s a very nice shortcut.

[03:45]

It is.

[03:46] 

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?

[03:53]

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.    

[05:37]

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

[05:40]   

‘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.    

[05:48]

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?  

[06:08]      

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. 

[07:08]

So you really have to move upstream.

[07:09]

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?”   

[08:18]  

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.   

[08:27]  

Apparently.

[08:31] 

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.   

[09:18]

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.   

[10:19]  

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

[10:25]  

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

[10:32]  

It’s great for SEO, by the way.

[10:33]  

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

[10:39]

James, thanks so much for being on the show.

[10:40]

Thanks very much.

[10:41]   

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.