Why Is Now the Golden Age of Data Analytics? With Cindi Howson of ThoughtSpot

Eric Dodds 0:05
Welcome to The Data Stack Show. Each week we explore the world of data by talking to the people shaping its future. You’ll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by RudderStack, the CDP for developers. You can learn more at RudderStack.com.

Welcome to The Data Stack Show. Today, we are going to chat with Cindy from ThoughtSpot. And she is the chief data strategy officer, she helps their customers with their data strategy. So she gets to see all sorts of interesting ways that companies are using data. And she is actually as we’ll find out a user of the ThoughtSpot products herself. So she knows all the ins and outs cost us what I want to know. And this is just a personal suspicion I have partially based on experience and a lot based on conjecture. But I think a lot of companies still live in what I would call sort of relative to the options available like a primitive world of analytics where you get the basic reports that allow you to run the business and have a sense of what’s going on. But it’s pretty hard to move past that. But that’s based on my own limited view. And Cindy has worked in data analytics for over two decades. And so I think she’ll be able to help us see like, is that actually the case? So that’s more of my personal burning question. How about you?

Kostas Pardalis 1:24
Yeah, I think Cindy’s like the expert, when it comes to big transformation in the organization of has to do with the use of data. And she has seen these happening from starting from like the mainframe, and like doing analytics on the mainframe to the era of cloud that we live today. So I think there’s a lot to learn from here on what it means to adopt and use your data and what it takes to do that, like I was part of an organization. So yeah, I’m really looking forward to chat with her. I think today, it’s not. It won’t be that technical of a conversation. But I think it’s going to be a very unique conversation.

Eric Dodds 2:05
I agree. Well, let’s dig in.

Cindy, welcome to the show. We are so excited to talk about all things analytics and even more with you today.

Cindi Howson 2:15
Thank you, Eric. My favorite topic.

Eric Dodds 2:17
All right. Well, could you just tell us you’ve had such an interesting journey in analytics, because you really were doing the actual work of analytics, when it sort of became a real thing in terms of technology. So can you start there and just sort of tell us about your journey and what you’re doing today?

Cindi Howson 2:35
Who are so I have to take you back more than 25 years ago now, in Switzerland, and I was working for Dow Chemical, I was known as the Lotus 123 Macro Queen at the time. And then writing focus reports on a mainframe computer. And that lots of detours or let’s say different paths in between, worked for Deloitte and Touche for a little while in Texas, they were just then starting their BI and analytics practice. So we’d have to think back to why 2K was the big emphasis, right, not data, not data warehousing. So we were starting that practice. And then I started my own company. The BI scorecard, which there were two emphases is for two angles to this business. One was comparing the eye products hands-on kind of the Consumer Reports of BI at the other ways helping organizations have a bigger impact with data. I licensed the rights to that research to Gartner and joined Gartner in 2015 and then joined ThoughtSpot coming up on three years now in 2019.

Eric Dodds 3:58
Wow. Okay, I just have to ask, is there a particular report that sticks out in your mind from your time at Dow Chemical that was particularly challenging or surprising?

Cindi Howson 4:12
Yeah, because it was the first he remember the beginning and the end, I guess. But it was the first one where my manager at that time and was asking for plant capacities. And he felt like it was not possible, because our product codes were in one database, and capacities were in another and they didn’t think it was possible. They thought I would have to download everything into spreadsheets, merge it together, then it would be one-off analyses. I’m like, No, I think we can merge these different tables in the different databases. And he was staggered that we did it. It also stands out in my mind because as we iterated on this, really the question He was asking was not just what are the capacities, but it was the why. And it was because there were some cracks in a new facility we were bringing online and the he wanted to take it offline for a period for some maintenance. And they, they couldn’t, he couldn’t do it in time, the cracker exploded, nobody was hurt. But I go back to that because I think Bob Lee was the manager. And I was like, if Bob could have asked his own questions, like you can, in thoughts that we, we would have been able to just operate faster. And so I just look at how the technology has changed. But the problem of letting the business people ask their own questions that that problem still persists.

Eric Dodds 5:52
Fascinating. I want to dig into that. But let’s just touch base on the technology side of it. So one thing that I would love your perspective on is you have been working in researching analytics tools since the beginning and I think in many ways, like the you were, you had a front row seat to sort of the explosion of analytics tools. There are just so many out there, there are tons of different ways to do things. Could you just walk us through what are some of the major shifts you’ve seen over the years in terms of major milestones in analytics and in the technology specifically?

Cindi Howson 6:34
Yeah, so I think of it really as four chapters. Now, the first chapter really was about report-based analytics without generating SQL or coding in SQL. This was the error of Business Objects and Cognos know when there were acquisitions. So IBM acquired Cognos, SAP acquired Business Objects, Oracle acquired Hyperion, and Siebel Analytics, all in the 2007-2008 timeframe. BI was elevated then because of the scale of some of these deployments, but it was very report-centric. The second generation was really about visual-based data discovery. This was pioneered by Click and Spotfire. Tableau came along and made it easier and outpaced both those vendors. So, that was the second generation where it was about the visualization and empowering the analyst. The third generation is really augmented analytics, or at ThoughtSpot we call it search and AI. So it’s about using search, natural language processing, but then also AI to generate insights that you didn’t know to look for. And the fourth generation is really the modern analytics cloud, still with augmented analytics, but never having to move your data, powered by a cloud ecosystem. Whether it’s the data platforms, like Snowflake, Google, BigQuery, or it’s about the data science as well. But all in a cloud ecosystem. So finally, customers can have best-of-breed capabilities without moving the data.

Kostas Pardalis 8:40
So you mentioned that we have like four stages. And we are from whether there’s already in the fourth one, right, like the modern analytics cloud is like other formation right now. So at what stage of these states in terms of maturing, we are like how you have seen companies embracing like the clouds, and what has been built and exists out there and what remains to be built and figured out.

Cindi Howson 9:04
Yeah, so really, you can’t do analytics without your data. And you can’t get too granular analytics without the scale that the cloud offers. So if I think about where was cloud data platforms, if we go back to a few years ago, less than 10% of the data was in the cloud. The pandemic really has accelerated every organization’s migration to the cloud because it accelerated digital. As soon as you’re digitizing all your customer interactions, then you get into the volume of cloud. It also is the growth of these cloud data platforms. I mean, snowflake last year, the biggest IPO period in The software industry. So I think it gives that credibility and comfort, that cloud is the way to go. And there are predictions out there that by like, 2023, more than 80% of the data will now be in the cloud, we’ll still have some on premises data stores that people just won’t go back and replatform. But that’s a huge change from just 10% a few years ago.

Kostas Pardalis 10:26
Oh, yeah. That’s incredible, actually, from 10% to 80%. That’s what’s crazy. And I know that those were like, started as a company that was very enterprise focused, right, with a lot of like, on-prem installations, and did like the migration to the clouds. And this happened like in, I don’t know, like, two or three years. So it’s not like, a lot of time, right? How you have experienced all these companies that you were working with them three years ago and they have like vision prem installations going to the cloud, like how the process of moving into the cloud has happened and has happened also so fast.

Cindi Howson 11:08
It has happened fast. So sometimes, I feel like I’m watching a movie, I feel like I’m watching an action movie. And I do think what ThoughtSpot has accomplished in two years, eventually will be written up as a Harvard Business Review case study, because I look at some organizations, let’s say Adobe, for example. And their transformation, or even MongoDB, and how long it took, and how quickly we were able to shift, really, and there were two big shifts. So as I mentioned, you can’t do analytics without the data. And our customers were asking us to not we started also leveraging our own highly scalable, distributed in memory engine Falcon, that was the first generation of the platform. But then our customers were asking us well, as we implement redshift, or snowflake, or Google BigQuery, we don’t want to move the data again. So help us connect to that. And so we released what we call embrace mode in early 2020. And that was the first part of the journey to the cloud. Now, many of our customers were running already in a private cloud, our software, so that was also part of it. So here we are, January 31, 2020, we released the spore connectors in embrace mode. And the pandemic was just starting. So think back to that time. And we knew and saw that organizations that had already started with ThoughtSpot. Were handling this chaos of the pandemic better. They were handling their supply chains, they’re working capital better. And we said, Well, how do we enable this for organizations of all sizes, and for that, we needed to go to a full SAS offering Software as a Service. So ThoughtSpot Cloud, we released early in June of 2020, had had some beta customers there, and then fully launched in September of that same year. And now cloud accounts for, I want to say 85% of our new customers, and now 60% of our annual recurring revenues, roughly. So that is a huge shift. It’s fast. And I think a lot of it is that we had the foundations already there for scale. But then I also think it’s the mindset of this team, that our customers needed us to do this.

Kostas Pardalis 14:08
Yeah. Okay, I have this, like, I know the wait for our business review to come up with the case study. So I have to ask you, what’s the secret there like, because taking like a company, like an organization, that is everything is like, let’s say, built on a very focused way, like on a very, with very specific go to market motion that they have to do with, like on-prem installations. Everything is like, different when you go to the cloud. And I’m not talking about the technology here, right? Like, this is probably to be honest, like from my experience, it’s hard. But it’s the easiest to manage as a transformation process at the end because the engineers know what they have to do and how long it will take to move from one to the other. But changing the culture of the organization like talking to your sales teams and like fill them up like now We do something different changing the language, the positioning for marketing. How can you do that? And how you can do that scale and so fast? What’s the secret there?

Cindi Howson 15:11
Yeah, so there’s a couple parts to this Kostas I think, first off the founders and the executive team make the hard decisions, we, our mission has stayed the same to create a factor of and world, it’s really just the distribution of the software that has changed. So if we can accelerate that distribution, then people are all in, but it has taken hard choices. Now, I don’t think the problem is internally in ThoughtSpot because we are a tech provider. What is harder is our customers and the culture there. They may not want to move as fast as we move. Although now I see. When I connect our customers, they want to learn from the digital natives, because the digital natives are very quickly usurping and disrupting, what risks becoming legacy competitors, and there are classic examples, you have Hulu disrupting blockbuster, you have large banks that have now digital only banks threatening them. So culture is a big barrier to transformation. There’s research from Randy being the author of fail fast learn faster, that 92% of the organization’s he surveys say that it’s the combination of culture, people and process that are the bigger barriers to being data-driven than technology.

Kostas Pardalis 17:03
That makes a lot of sense. And how will the vendor cannot affect that culture that the customer has? What ThoughtSpot can do to help? I don’t know, the Bank of America is out there, like big institutions, huge and complex processes, embrace this change and accelerate the digital transformation?

Cindi Howson 17:28
Yeah, so there are some best practices here. One is start small, and align to a particular pain or opportunity. And take that as your proof point for the new operating model of where you want to get to emphasize the why not the how the technology and cloud that’s the how, but the why, maybe for a banking customer, it might be delighting your customers and getting an increased share of wallet. So aligned to the why from a business viewpoint. And the technology is the how. Now with that, we also do focus on changing mindsets and behaviors. We do that through things like the data chief podcast, we have a data chief Slack community, we do executive roundtables and lunch and learns, so that we are helping with some of that cross-pollinate pollination between the leaders and the laggards.

Kostas Pardalis 18:38
Okay, that’s very interesting because you make me feel like you have access to the way that these organizations are thinking and working on a very high level, which is not easy to get access to. And I’d like to ask you based on that, from interacting with all these organizations and the people behind them, how do you manage to identify some patterns in these cultures that you see out there? Because obviously each company has their own culture. That’s for sure. But there has to be like some common patterns that we see out there that we can probably identify them, either as, let’s say, common obstacles that we see out there to embrace the digital transformation, or in some other cases also something positive or something that in some cases accelerates the process. So what’s your experience there? What have you seen?

Cindi Howson 19:41
Yeah, so if I look at organizations, so I often say culture and technology is two sides of the same proverbial coin. And somebody said to me, it’s a very expensive coin. But if I look at a company that has a legacy data stack So they’re not, they’re not in the cloud yet. They’re still either just based on that second wave, or even worse, the first wave report centric, then I will see a culture of complacency, settling for the status quo, siloed thinking and resistance to change, lack of leadership. If I look at organizations that have embraced cloud, and the modern analytics stack, I see a high degree of experimentation. And they don’t see risk-taking or failures as failures. They see it as learning and rapid prototyping. And there is a high degree of trust between the data team it and other functions, lines of business, they’re very much purpose-driven, with a can-do attitude.

Kostas Pardalis 20:57
Can you give us an example of a company that you think they are doing a really good job embracing change and experimentation, something that we could get inspired by what?

Cindi Howson 21:09
Yeah, so I think of some of the hallmark customers that I have the opportunity to work with, and they are public references. I look at the likes of Verizon, or motronic in medical devices. But then I also look at some of the digital natives like Cloud Academy, they’re all about upskilling, even Bagel brands, and I’m thinking food now, Bagel brands, Einstein, bagels, they’re doing a lot of innovative things. And you reference large banks. They’re not all slow thinkers, like Bank of New York, Mellon is doing some innovative things and has standardized on ThoughtSpot as well.

Kostas Pardalis 21:57
You mentioned brands and like they’re like some specific industries out there that regulation is also very important around data. How do you see that as a factor that affects change? I mean, obviously, like 10 years ago, the landscape out there, like the legal landscape around like how we work with data was like completely different than it is today. And probably it’s also going to change again, right? Like, we’re not, we’re not done yet. So how do you see regulations playing a role in the adoption of the cloud native mindset, let’s say.

Cindi Howson 22:34
Regulation is not saying, don’t have your data in the cloud, it’s that regulation, you don’t get to do away with your responsibilities for making sure it is protected, encrypted, whether at rest or in transit, and then respecting the privacy of individuals. So the fear of the cloud there, I think is misplaced. All of those items that I just listed, I think the cloud is more secure, unless you actually have your own data center that is physically guarded, and you’re always on the latest levels of encryption. So regulation, I think, is often used as an excuse. There. There are some constraints related to this, particularly with respect to privacy, but I even highly regulated industries, pharmaceuticals, for example, life sciences, I’m still in seeing them embrace cloud and modern analytics.

Kostas Pardalis 23:45
That’s super interesting. Let’s talk a little bit about the people that are using ThoughtSpot, right, because we’ve talked so far about the collective side of things and culture, but culture like comes from the people who work there. Right? And who is the main user of full sporting organization today?

Cindi Howson 24:06
Our North Star is the non-analyst, the actual business person who has the questions, and the analysts historically have been a bottleneck between the decision maker and the data. So we want to elevate the analyst so that they can work on more high-value high impact analytics, not doing silly things like spending hours formatting a dashboard, or adding a sort button, or worse formatting something in PowerPoint, as one leader said to me, PowerPoint is where data goes to die. So we don’t want that analyst bogged down by the drudgery of the backlog of requests. So we want them to help the business users ask their questions, optimize their questions. So it’s empowering the analyst to do more with less. But actually have everyone ask their own questions.

Kostas Pardalis 25:12
And how is AI Maryland’s natural language processing important?

Cindi Howson 25:19
Yeah, so AI is infused throughout the platform, the most obvious thing will be aI generated insights. So telling you maybe what you didn’t know, to ask. So just because Eric and I were talking about coffee, maybe an AI generated insight would be, is there a certain demographic that is more likely to drink their coffee black, and it would tell you what the outliers were, rather than the demographic that like, saw the Carmel and whipped cream on it. So the AI will generate insights. But AI will also nudge you in terms of saying most other people will filter their search by this particular attribute, have you looked at that, it will also choose the best fit visualization, it will give you things like trending live boards, trending content. So those are more the subtle forms of AI that are baked into the product.

Kostas Pardalis 26:24
Super interesting. I think that that’s our industry, like since its inception, I mean, this trying to do exactly that, to empower non-technical people to ask questions to the data and get the answers that they are looking for. I think a very good example of that is SQL itself as a language. It was never created as a language that was supposed to be used by only engineer for like, technical people. Actually, I can think of many engineers that they don’t know.

Cindi Howson 27:00
I feel like that’s the one language I learned that’s been timeless.

Kostas Pardalis 27:05
It is, and today, we are talking more and more about it. But yeah, it’s not like you don’t have like to know SQL to become like a front-end engineer, for example. Right? So and even then it’s like very different, let’s say, think of like using SQL to ask anally related questions. And using SQL to build like an application, for example, right? And like, create records and things like rich. So how close are we because the two dots even fat at the end? I mean, I understand that ThoughtSpot has done a lot of innovation and more and more business users out there are able to ask their questions without the need of probably even knowing SQL, but have we achieved this, let’s say vision that exists in the industry for the past, I don’t know, like 30 years, probably more, or we still have work to do there. And there are like still problems that we have to solve, like, how do you feel about this?

Cindi Howson 28:07
Well, as an industry, of course, there is work still to be done. And in ThoughtSpot, we say we are only ever 2% done because we really want to make it easier and more pervasive. So if I look at some customers, and that had been on this journey longer with us, I’ll take Schneider Electric, for example, this is where we were talking about best practices aligned to a particular mission, or vision, the why, and then thoughts about is the how, or the technology is the how well Schneider Electric, really has a mission to ensure all people have access to energy. It’s a basic human right. So part of their goal in rolling out ThoughtSpot was to free up people’s energy. And to enable, they started mainly with a people analytics use case, to identify top talent to make sure that they retained the top talent, and their adoption rate is 78%. That’s those are not analysts. These are people, managers, HR professionals. And if you think the industry average is about 25%, and that’s mainly power users. So individual organizations, I would say are there. But as an industry, we’re nowhere close.

Eric Dodds 29:46
That’s fascinating to me. Those numbers are pretty wild. 25% adoption mainly by power users. I actually want to circle back and ask a question about the AI-enabled analytics or insights. Of course in ThoughtSpot but just in general, and— I’m saying this in part because it’s really interesting to hear about “are the power users leveraging the AI Insights,” is one question that comes to mind. But I think even beyond that, and I’m speaking a little bit from just personal experience here, as AI assistants and analytics has sort of become more common, even in a tool like Google Analytics where now you’re getting served, oh, your number of sessions from this particular channel is lower than last week, right? A lot of times, I will just dismiss those, right? Because it’s like, okay, I kind of know what I want and I don’t really know what’s going on under the hood. They’re producing some sort of insight, and they don’t really understand my business context or the questions that I’m asking. And so this is complete conjecture, but it wouldn’t surprise me if there’s a low rate of adoption for AI-assisted analytics, especially the power users on some level they’re like, I don’t trust this as much as I trust myself, or like that I trust my SQL, like, I know I can get to the right answer. Do I trust this machine? What if they give me the wrong number? That could really screw things up. So I just love your perspective on that.

Cindi Howson 31:20
Yeah. So Eric, it’s interesting because designing for openness and trust are some of our key design tenants. And you’re not going to trust blackbox AI. So what we give you is full transparency into seeing what was the sequel generated? What were the algorithms used? And even what were the inputs to that model, and you can control? Maybe there are certain attributes, or if you’re a data scientist features that you don’t want to be input into that model. So really, without that transparency, it can create noise, and we don’t want noise.

Eric Dodds 32:09
Yep. super interesting. Yeah, fascinating.

Kostas Pardalis 32:12
Eric, I have a question here. So this should be like, very interesting what you just said right now. So you give like all the auditing trails that are needed there for someone to go into audit what’s going on. But who’s responsible for that? Who’s going to hold the AI? Because the power user, and that’s the interesting part here: we create abstractions so we can let the people that do not have the technical knowledge to go and use the technology. But there are times where we have to audit the technology. So who inside the organization should do that?

Cindi Howson 32:49
This is where we see the role of the analyst, shifting from just a drudgery, dead-end dashboard developer, to what we call the analyst of the future. And we’re seeing an emerging role as well for the analytics engineer. But this would be the analyst who is optimizing the AI or looking at the AI generated insights, but they don’t have to code it if they want to take something and pass it back into a full data science platform, they can do that, but we don’t want it to be a blank canvas. We want to give them that starting point.

Kostas Pardalis 33:36
That’s super interesting. Like, I love this, how empowering the end user, or the non-technical user also transforms the existing roles. So it’s like their roles gets obsolete, they just change and it becomes different.

Cindi Howson 33:55
Yeah, not obsolete. No way. And look at our labor market. The need for talent in this space is so tight, I was talking with a customer last week, a new customer. And he, I think I can share us a pricing manager and he said, I lost my analyst. He’s like, I’m not a data person, I just need to figure out this pricing and the prices that we’re setting, and he could teach himself ThoughtSpot. And he said, thank goodness, I can because we haven’t been able to fill this wreck for the analysts that we need. So we have to let the analysts stop the lower value work teach other people to fish. And that frees up the time for the analysts to get to the new data sources, ingest them faster, but then also work on these AI optimizations.

Eric Dodds 34:53
One question in there because I was actually thinking about this and you just said it the education piece of it. So, in all us a specific example, maybe that will resonate with some of our listeners who have worked on the analytic side. So, I love the idea of this self-serve let’s let the decision maker, let’s empower them, right. And in many ways, I totally agree with you, like, let’s remove the drudgery from like, the whole data engineering, like, analyst role, right? Because report building can be brutal, like cleaning it up, redoing the visualizations 20 times, all that sort of stuff, it’s like a great if we can get rid of that, like, everyone will be happier. But there is an educational component, right. So if I think about it, I’ll just use a specific example, we were working just on our team on some cohort reporting, both rearward facing, and then sort of forward-looking forecasts. And those can be incredibly helpful. But if you’re not familiar with it, or you haven’t looked at them a lot, it can be a little bit disorienting, right? It’s kind of like you say, Okay, I need to think about like, this isn’t a point in time reporting on a timeline for everything, it’s like, related to just a very specific subset of users and there are time decay elements and all that sort of stuff. What are the ways that you’ve seen that sort of education happen best? So you just mentioned the analysts sort of moving from maybe just a report building type role, to actually actively educating? Do you see that in the products ThoughtSpot other products as well? Is it the responsibility of the analyst? Is it a culture-wide thing?

Cindi Howson 36:45
It’s all of the above, Eric, but think about what you described, you use the word co cohort, some people may not even really know what that is. And as an industry, we have spent so much time trying to teach business people, we’re non-analysts technology, and we don’t get to teach them the language of the business. And then the more finer grained analytics, whether it’s clustering or cohorts and what have you. So what we have to do is make the technology so easy, that we can shift the focus to the data. And what is it really telling us? Daughter of a DJ here, so I use music analogies, and I think about how you create a Spotify playlist today, did you watch a YouTube video to learn how to do that? Did you read a manual? Did you go sit in a training room class, you learned it largely yourself. Now, I’m gonna suspect my college-age kids probably gave me a few tips to get going. But I think of my father, the way he would create his playlists and the sound system, you really were a professional, the same thing has to happen with data and analytics. So the analyst may teach the business user the starting point. So give them a live board as a starting point. And they might become a coach or somebody, I love this term, somebody used the word a data Sherpa, so kind of a guide, just a quick half hour, here’s how you get started. You might follow up with some lunch and learns. And then more sophisticated things are baked into the product. Let me show you your data. Let me show you how to explore this cohort, for example, but teaching the language of the business that this is really data fluency. And that has to be ongoing at all levels.

Eric Dodds 39:01
Sure. Yeah. I was talking with someone who worked on the analytics team at Swift, and they’re doing some really interesting things. But they it was so interesting. They said, we do monthly “Lunch and Learns” with our marketing and sales teams, right? And so like, we actually push reports, and then we’ll do like a lunch and learn, and we’ll walk you through it. And they can ask questions, and but what struck me about that was, I was like, Wow, that’s so simple, but so great. And then I thought it’s so rare, right? I mean, it just seems like so few companies actually do that. Would you say that’s true?

Cindi Howson 39:37
I don’t think that’s true anymore. Every customer that I work with, they are doing lunch and learns or data and donuts, things like this ThoughtSpot Thursdays. So the data fluency even now is a board room conversation for most organizations and or let’s say for most forward Thinking organizations? Sure. When it’s not, I would be concerned about those organizations.

Eric Dodds 40:05
Yeah, yeah. No, that’s, that’s really encouraging similar question just around analytics. So if we think about the analyst who’s just doing report building, I think in that environment, a lot of times you are kind of limited to sort of your basic analytics, right? I mean, you have these like, unbelievably powerful tools, and you’re doing the most basic four or five reports that you kind of used to manage the couple of KPIs that drive the business, right. But there’s this whole world available. Do you see a distribution of that? I mean, I know ThoughtSpot customers, sounds like they’re a little bit more forward thinking. Industry-wide, though, do you see a lot of companies that are sort of using powerful analytics technology, but are still sort of in the kiddie pool, as it were, in terms of the analytics that they’re running?

Cindi Howson 40:58
So just stuck in descriptive analytics. So I don’t get these tears, descriptive, what’s happening? And that’s report centric, diagnostic, exploring the why. And then predictive, what’s likely to happen? I actually don’t like the term predictive, or it’s too confined because I think about segmentation, that is an advanced analytics, and they really prescriptive, what is the action I should take based on this insight. And so we do know that many are stuck in that very basic descriptive reporting, but I think this is where, again, the best companies, are well, on their way to diagnostic and predictive.

Eric Dodds 41:48
Got it. Super interesting. Yeah, it’s really encouraging to hear, actually, we all have our own sort of little slices of the world that we see. And it’s really encouraging to hear in your wide purview that there is a big change happening in the industry and that a lot of these problems sort of seem like, okay, we’re stuck in descriptive analytics for years and now organizations are breaking through that.

Cindi Howson 42:14
Yeah, and maybe so because you see the lens or the bias that we view things through. So I think it’s important to dwell on that for a moment. One of the reasons why I joined ThoughtSpot is when I was at Gartner, and I would do customer reference calls. It felt like I was always talking to the more innovative companies that were ThoughtSpot customers, no interest. And, and so I do think, yeah, my lens now is very much the leaders. And, and they are there, they’re also creating new markets. So I, another customer from Europe. Now also part of the US brand, just eat takeaway. Food delivery was a niche segment, three years ago. Now look at the volume of data you’re creating from your app, and then powering restaurants to tell them what people are ordering. That is that that has disrupted whole markets. And thank goodness, we’ve had that in the last few years. So I do think my lens skews more towards the leaders. But that’s the natural evolution of things as well, I think the ones that stay stuck with just reporting, they’re not going to be able to keep up.

Kostas Pardalis 43:43
I’d like to ask you to share a little bit of your knowledge around the changes that are happening in the structure of the companies because of these transformation that is happening right now. For example, you have the title of Chief Data Officer in the field. So what is the role of the Chief Data Officer and also what other roles you see emerging or changing, we are getting transformed. We mentioned already the data analyst how from being let’s say, the person who’s going to build and maintain dashboards becoming like something different to support this new environment, new reality, but what other roles you see changing and if you’ve feel like something else is going to be introduced?

Cindi Howson 44:32
Yeah, and to clarify, my official title is Chief Data Strategy Officer. I work with our customers on their strategy. So so minor detail, but this is the CDO role within many organizations now, I think is I want to say it’s well established, but it continues to evolve. Because the first-generation CTOs were really the data guardians and gatekeepers. And as they’ve continued to evolve to really, how do we apply the analytics for business value, their roles have been elevated, I’ve seen some evolution of the title as well, Chief Data and Analytics Officer. So I think it’s senior levels. It’s evolving and where these roles may have started. In it, I increasingly see them reporting to the Chief Digital Officer or to the CEO directly, not at the senior level, within, let’s say, the contributor roles. One of the predictions I wrote this year, is that the analytics engineer, will replace the data scientist as the world’s sexiest job, Tom Davenport, and DJ Patel came up with the sexiest framing of the data scientist, but data science has was slowly losing its luster, because they’re not working on high-value things, many of the models they build are never operationalized. And they’re done off to the side, a lot of the work is about data quality data cleansing. And many of the data scientists lacked the domain expertise to apply the models or even build the model. So I think this analytics engineer has the domain expertise, they are well versed in cloud technology. So they’re able to work in a more agile way. And it’s the delight of working with newer technologies as well that you can cleanse, transform the data, operationalize some of these things, all in an open ecosystem.

Kostas Pardalis 46:57
How you would define an analytics engineer? What’s the backgrounds of this person? And where they come from?

Cindi Howson 47:05
Yeah, they I mean, they might have evolved from the role of a data scientist or they might have evolved from the role of the analyst who is looking to have a bigger impact.

Kostas Pardalis 47:17
And how do you see the more traditional engineering roles, like relate to that, like data engineers, for example?

Cindi Howson 47:26
Well, even data engineer, I would say, still a relatively new role. If we were talking in an on-premises world, then we might be talking about ETL developers, whereas data engineers, now you’re, you’ve kind of shifted the order of things, extract, load and transform. And so the data engineers are responsible for these pipelines. But then the analytics engineer, they might also have some of their own pipelines, but they also know the domain. And their end goal is not moving the data into a data lake house. But their goal is really about creating the analytics. So another role, where some of these things come together is the data product owner. And this is where people get very afraid or passionate because we talk about concepts like the data mash, and people are like, wait a minute, what about master data, this is going to be chaos. And yet, we have to go down this route, the more we have conversations about it, we’ll tease out what are the problems and pitfalls to avoid. But for sure, the idea of building one centralized data warehouse and one centralized IT team is going to do this and it’s going to take six to nine months. That’s not the pace of business these days.

Kostas Pardalis 48:57
Yeah, yeah. I have a feeling of like, we used to say the during like, let’s say, the past decade, that’s I, I call that decade, the SAS decay because we weren’t it was all about like building SAS and moving my coviz into the cloud of applications. I mean, we used to say that, like every company is going to be calm, like a software company, right. And I have a feeling that the next step is going to be every company is going to be a data company.

Cindi Howson 49:22
Yes. I agree. We agree. Yes. Data powers, every company, and data is now part of everyone’s job. So this, this is where the workforce is going and where industry is going. Somebody was asking me what is the digital economy? And I said, the digital economy is the economy and you don’t have a digital business operating well unless you are leveraging the data that is generated by these digital interactions. If you think financial services, the days of knowing your personal bank teller down the street, you don’t know that you only know your customers through the interactions on an app, for the most part.

Kostas Pardalis 50:18
100% I totally agree. That’s a very, very interesting point. One last question for me, then, like, I’ll give the stage to Eric, just as a continuation of like, the roles that we talked in so far. And like the changes that are happening there. What’s the impact on IT as a function in the combining of all these ages? Because we used to carve it as like, let’s say, I mean, the function that takes care and controls everything else has to do with the technology inside the company. But it seems that like things are radically changing. So where is it going as a function in the organization?

Cindi Howson 50:57
I’m a little nervous to answer that. Because like, some people will get angry and upset. But there was a very provocative article in the Wall Street Journal. Is it time to get rid of the IT department? Well, and at first I looked at, I was like, wow, somebody just, you know this is clickbait. But when you read it, the arguments were very well presented. And it’s more the concept of a centralized model was designed for an on-premises world where you had to buy a big mainframe or a big server or what have you, and operate that. So I think we need technologists, but those technologists may not be only centralized, I think it’s really about when do you have a federated model, and you federate to get domain expertise. So if you think about even going back to a transactional application, maybe you’re buying your CRM in the cloud, and it’s that domain person that will decide what’s the best CRM, but then you’re going to look for economies of scale, in terms of when you want to centralize something. So I think it’s still about you want centralization for some governance and career development. But you every, every domain will have a technology aspect to it.

Kostas Pardalis 52:44
Yeah, and I guess at the end, like, as every other function in the company goes through transformational, so it has to be last rites and like goals. Thank you so much. Eric, all yours.

Eric Dodds 52:55
Yes. Well, I think we’re close to time here. I just have one additional question for you, Cindi. I was gonna say this as a personal question, but maybe it’s actually not. Do you still enjoy digging into the numbers and doing analytics? I know that you run a lot of strategy but I also know, even just on a personal level, it’s not always a great use of my time to sort of dig into reports, but I still love doing that for some reason, and just with someone with your history and experience, I’m just interested to know if you still enjoy that.

Cindi Howson 53:33
Absolutely. I do. Even this morning, actually, there was a new feature that we released on the platform, I’m like, Oh, let me try that. And then it was like, oh, which data set? Do I want to do this with that I actually am allowed to share. So as a business, I look at our customer satisfaction numbers, I look at our interactions, I look at our sales, I also look at the diversity that we have in our organization, as well as in our industry. So my husband told me he had to ban the word data on our anniversary. He’s like, for one day, can we just not hear that word data? And then I think we had the news on and data was on the headline. So I think I still have the last word there.

Eric Dodds 54:23
So great. No, that’s really fun. It’s just fun to hear that even though you’ve done so many interesting things with research and strategy that you still have a love for digging in.

Cindi Howson 54:36
The other exciting thing is having done this for 25 years, I feel like it’s now finally this will be the defining decade for data and it’s not some after thought it’s really a fourth thought and it powers business. Well, then it makes the world a better place. But maybe we have to save that for another commenter.

Eric Dodds 54:59
Yes, we definitely should. Cindi, this has been such a wonderful conversation, we’ve learned a ton about, really the history of analytics. And then also the way that the most forward-thinking companies are doing this, both from a technology standpoint, and I think more importantly, a cultural standpoint. So it’s been really helpful. Thank you so much for your time again.

Cindi Howson 55:20
A pleasure talking to you, Eric and Kostas.

Eric Dodds 55:23
Okay, here’s my takeaway. I thought about this a lot, actually, throughout the episode, maybe to the point of getting distracted, but you asked some good questions so that didn’t matter, so thank you. But it was really clear that some of her earliest experiences as an analyst working for Dow Chemical, were formative in large part because they had a product explosion in a plant, which could have harmed people, I mean, no one got hurt. But the way that she described remembering that experience, and then once, that being a really driving a big driver behind her passion for even the product, ThoughtSpot and saying, if we had this, we could have avoided it. I just think that’s a really special dynamic, when you believe in a product because you know that it could have solved a catastrophe that you were a part of, in some form or fashion earlier. So I just thought it was really, really neat to tie that formative experience to what she’s doing today. And I just always appreciate people who have that level of authentic belief in what they’re doing and the product they’re working for.

Kostas Pardalis 56:35
Yeah, 100% agree with you. From my side, I would say that, like, I found extremely interesting, the whole discussion that we had around the transformation of the roles and say, in how this is also associated with cultural transformations that need to happen in order for companies and organizations out there to embrace and utilize the data that they’re generating, right. I mean, we should spend more time with like, discussing about that stuff. I think he has like a lot of insights and insights that are like very interesting for everyone who is a professional in this new space. So yeah, I love I mean, the full conversation that we’ve had, but like these bottles, a conversation and I found it, like extremely insightful.

Eric Dodds 57:18
Totally agree. All right. Well, thank you for joining us again on the show. Subscribe to get some great episodes and your favorite podcast listening app, and we will catch you on the next one.

We hope you enjoyed this episode of The Data Stack Show. Be sure to subscribe on your favorite podcast app to get notified about new episodes every week. We’d also love your feedback. You can email me, Eric Dodds, at eric@datastackshow.com. That’s E-R-I-C at datastackshow.com. The show is brought to you by RudderStack, the CDP for developers. Learn how to build a CDP on your data warehouse at RudderStack.com.