My guest today is Michael Yaksich, Director of Customer Strategy at Cruise. Headquartered in San Francisco, Cruise is a self driving technology company that will offer a ride hailing service initially in San Francisco. Prior to joining Cruise, Michael has worked in insights at Hyundai, BrandIQ, Cadillac, and Honda.

Find Michael Online:

LinkedIn

Website: https://getcruise.com

Find Us Online: 

Social Media: @happymrxp

LinkedIn

This Episode’s Sponsor: 

This episode is brought to you by Clearworks. Clearworks is an insights, innovation, and customer-experience company. They help clients understand their customers better, identify opportunities for innovation, and create products, services, and experiences that matter. Their clients are diverse in size and industry but share one important thing: a passion to drive more business by driving more meaningful human connection. For more information, please visit them at www.clearworks.net.

[00:01]

On Episode 224, I’m interviewing Michael Yaksich, the Director of Customer Strategy at Cruise, but first a word from our sponsor.

[00:07]  

This episode is brought to you by Clearworks.  So, we have a couple of sponsors on our show. I just want to underscore how much I appreciate those of you who have sponsored the Happy Market Research Podcast.  It makes a ton of value to the ecosystem that is actually transcending market research right now. I say “transcending”; that’s probably the wrong framework, but exceeding, moving beyond into user experience research as well as data analytics and insights.

Clearworks, thank you so much for your sponsorship.  For those of you who don’t know, they are insights and innovation and customer experience company.  They help their clients understand their customers better, identify opportunities for innovation, and create products, services, and experience that actually matter.  Their clients are diverse, both in size and industry, probably like all of ours, but they do share one important thing, which is a passion to drive more business by driving more meaningful human connections.  You can find them online at www.clearworks.net.  Again, it’s www.clearworks.net.  And again, thank you so much for your time.     

[01:20]  

Hi, I’m Jamin Brazil, and you’re listening to the Happy Market Research Podcast.  My guest today is Michael Yaksich, Director of Customer Strategy at Cruise. Headquartered in San Francisco, Cruise is a self-driving technology company that will offer a ride-hailing service initially in San Francisco.  Prior to joining Cruise, Michael has worked in insights at Hyundai, BrandIQ, Cadillac, and Honda. Michael, thanks for being on the Happy Market Research Podcast today.

[01:51]  

Yeah, thanks for having me.

[01:53]

So, tell us a little bit about how you wound up in research.

[01:56]

At least from all the people I’ve ever spoken to, it’s pretty common to say it’s not something you set out to do from the get-go.  I mean how many people have said “Yes” that you might have asked this question to in the past? 

[02:09]   

One.  So, I’ve done over 140 interviews, and I’ve had, I believe, one.  I might be mistaken, but I really think it’s one person that said intentionally they set out in college to be in market research or consumer insights.  

[02:23]

Yeah, I feel like that it’s more of a recent phenomenon.  I mean, growing up, my career aspirations went from wanting to be a marine biologist at Sea World and all those things that come with that and all the way to wanting to go into finance and then eventually discovering sociology in school, which kind of led me down this path.  I guess that’s a normal discipline that might lead people down this pathway. But, to me, ultimately, in the end, I think people are really drawn to what they’re passionate about because of something they’ve gone through or something they’ve experienced or even where they come from.

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: [email protected]

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 ...

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: [email protected]

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,

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: [email protected]

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,

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: [email protected]

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,

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: [email protected]

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.

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: [email protected]

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 cod...

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: [email protected]

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 ...

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: [email protected]

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?

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: [email protected]

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,