PAW 2019

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

June 27, 2019 Chueyee Yang 11:34 0 Comments

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

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

June 27, 2019 Chueyee Yang 9:31 0 Comments

Find Satish Online:

Email: satish.pala@indiumsoft.com

Indium Software

PAW 2019 Conference Series – Ryohei Fujimaki – dotData

June 27, 2019 Chueyee Yang 8:00 0 Comments

Find Ryohei Online:

Email: ryohei.fujimaki@dotdata.com

dotData

PAW 2019 Conference Series – Mike Galvin – Metis

June 27, 2019 Chueyee Yang 7:55 0 Comments

Find Mike Online:

Email: michael@thisismetis.com

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

June 27, 2019 Chueyee Yang 12:26 0 Comments

Find Matt Online:

Email: mcowell@quanthub.com

QuantHub

PAW 2019 Conference Series – Mark Do Couto – Altair

June 27, 2019 Chueyee Yang 10:07 0 Comments

Find Mark Online:

Email: mdocouto@altair.com

Altair

PAW 2019 Conference Series – Lawrence Cowan – Cicero Group

June 27, 2019 Chueyee Yang 8:07 0 Comments

Find Lawrence Online:

Email: lcowan@cicerogroup.com

Cicero Group

PAW 2019 Conference Series – Krishna Kallakuri – diwo

June 27, 2019 Chueyee Yang 0 Comments

Find Krishna Online:

Email: kkallakuri@getdiwo.com

diwo

PAW 2019 Conference Series – Jeff Todd – Wolfram Research

June 27, 2019 Chueyee Yang 20:28 0 Comments

Find Jeff Online:

Email: jtodd@wolfram.com

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.