The importance of being data driven with Justin Borgman

Introduction

This week I’m joined by Justin Borgman, the Chairman and CEO of Starburst.

Justin shares his perspective on how and why organizations are focused on data now more than ever. He discusses what it means to be data driven and the value that comes from it. As part of the conversation, we touch on how to navigate data literacy and step through the indicators related to data maturity.

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Speaker Profiles

Justin Borgman Twitter: https://twitter.com/JustinBorgman

Justin Borgman LinkedIn: https://www.linkedin.com/in/justinborgman/

Starburst: https://www.starburst.io

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Audio File

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Featured Hashtags

#CxO #CIO #Leadership #Data #DataDriven #DataLiteracy #DataMaturity #CIOitk

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Podcast Transcript

Tim Crawford 0:01
Companies are looking for new ways to transform their business. Technology plays a critical role in this transformation. Speed and innovation in both technology and thinking are key to this shift. Hello, and welcome to the Cxo in the Know podcast, where I take a provocative but pragmatic look at the intersection of business and technology through the lens of leading CxO executives. I’m your host Tim Crawford, a CIO and strategic advisor at Avoa. This week, I’m joined by Justin Borgman, the chairman and CEO of Starburst. Justin shares his perspective on how and why organizations are focused on data now more than ever, he discusses what it means to be data-driven and the value that comes from it. As part of the conversation, we touch on how to navigate data literacy and step through the indicators related to data maturity. Justin, welcome to the program.

Justin Borgman 0:57
Thanks, Tim. It’s a pleasure to be here.

Tim Crawford 0:59
So, Justin Borgman, you’re the chairman and CEO of Starburst, and we’re not talking about the sweets company today. If I’m correct,

Justin Borgman 1:08
that’s correct. No, a big disappointment for many. We don’t make candy. We do actually data analytics, and in particular, we allow you to query data anywhere, so in different data sources, and return very fast query results.

Tim Crawford 1:23
So why don’t we get started by maybe just a little background on yourself? So let’s talk about who you are, who Justin is, and your role as CEO.

Justin Borgman 1:32
Sure. So for me, my big data journey, I guess, began maybe 11 years ago. I was a business school student. Prior to that, I had been a software engineer, but I was I was working on my MBA, and basically met some guys in a in the computer science department while I was in school, and formed my first business. So my first startup was called Hadapt, that was a query engine for data in Hadoop, and doing data warehousing analytics in Hadoop. That company was acquired by Teradata. I became a VP and GM at Teradata, and then ultimately found the inspiration for my second business, which is Starburst, and that was really around an open source project that was created at Facebook to do you know fast analytics on data anywhere, and in particular, that’s what makes it unique that you could have data in an Oracle database, data in a data lake, data in various types of data sources in the cloud, on prem, and be able to access it regardless of where it lives. So, founded that in 2017 and since then have been building Starburst to the company that it is today.

Tim Crawford 2:35
That’s great. You know, one of the things that comes up in conversations with fellow executives is this focus on data, and if you think about organizations and what they’re thinking about, you know, data is a focal point more than ever. What’s your take on why that’s the case now versus in the past?

Justin Borgman 2:55
Yeah, great question. I think the fact of the matter is that the world is now moving so quickly that change is just a way of life, and I think like the COVID pandemic is a great near-term example of that, where buying behavior changed almost overnight. You know, suddenly people were doing banking and grocery shopping and and retail, you know, online more than they’d ever done before, and I think that’s a great example of just the pace of change. And in order to adapt and be successful in that pace of change, you really need to rely on data to tell you where to go. And I think that is becoming really the essence of survival in kind of the modern modern age.

Tim Crawford 3:39
Do you think that that companies are understanding that on the whole, or are we in the the very early innings, using a baseball metaphor of companies really understanding why they need to focus on data? Where are we along that that spectrum of time?

Justin Borgman 3:56
That’s a great question as well. You know, I’m going to guess that we’re somewhere. You know, maybe in the in the fourth or fifth inning, and I say that because I think the early innings to me were maybe 10 years ago, as as as more and more things started to become digital in the first place, and people started to struggle with how do I even store this data, how do I analyze this data, how do I do these things at scale, and now the early adopters who were early back then are have now developed some level of competency around how they build these systems, and that’s particularly true of internet companies. You know the the lyfts and Ubers and and Facebooks and and so forth of the world. But that knowledge is now starting to seep into you know the the rest of of enterprise as well. And similarly, normal quote-unquote industries are being disrupted by upstarts as well, and I think that is all kind of forcing people to take data more seriously and become more agile in the way that they they interact with data.

Tim Crawford 4:54
Do you think that they’re getting disrupted because those disruptors are able to use data? In ways that the incumbents aren’t, or do you think there are other extraneous reasons why? I

Justin Borgman 5:06
think that’s a big piece of it. I think that the disruptors are very often disrupting from a digital dimension, if you will, whether that’s Uber and transportation, or you know Airbnb and and the hotel industry, and it ultimately comes down to a necessary ingredient, which is being able to understand your customer super well. When you do that in a digital way, you have a whole lot more data points on your customer that makes it possible to understand your customer in a way that a traditional brick and mortar business maybe never could do. So I do think that’s a really core component of driving disruption.

Tim Crawford 5:42
No, that’s great. So, you know, if I kind of double click on that a little bit and talk about where those opportunities are, you know, regardless of the industry or if you have specific industries that you think about, where are those strong value opportunities? You know, assume I’m an executive. I’m listening to this. I’m thinking about okay. So data, yes, I know I need to use data. Where do I get started? I mean, there are a lot of different opportunities, but where do I get started?

Justin Borgman 6:09
You know, I think a very logical place to start is with what some people call customer 360. Like really, just trying to understand the journey of your customer holistically through every step of interaction, from being an early prospect, maybe coming to your website and how they navigate your website, all of that creates a digital trail of of what’s known as clickstream data, right? So you want to understand the journey there as they’re even you know evaluating you as a as a potential vendor or whatever it is that you sell, to then you know, the usage of your product itself. Product level data is another interesting source of data. You’ll learn a lot of patterns about what people use, what they don’t use, what they like, what they don’t like, all the way feeding into kind of customer success and really understanding the the sentiment of your customers and how likely they are to to want to renew with you or buy again? So really, kind of all aspects of that customer journey are generally creating kind of a a stream of data as they work through your life cycle, and and that’s an opportunity for you to understand every stage of that sort of funnel, if you will.

Tim Crawford 7:16
You know, one of the things, Justin, that I know you’re passionate about and talk about is the importance of an organization to be, quote unquote, data driven. We’ve been talking about how data is so important for organizations, but what does it really mean for an organization to be data driven? Why is that important? I

Justin Borgman 7:38
think it’s important, kind of like we talked about in the opener, increasingly, you know, making the right decisions, making the best business decisions, comes from understanding your business, you know, and understanding your market better than your competitors do. So I think it’s really essential to to success, and I think part of being data driven means developing people, tools, overall understanding of what data can do. It’s it’s sort of a combination of a maturity, you know, data literacy, data engineering, as well the actual infrastructure to facilitate these kinds of analytics, and you know, really trying to sort of change the approach to how you do business, such that data is ultimately driving the decisions that you make, and and not sort of gut instinct, which was maybe the way that it used to be done.

Tim Crawford 8:26
Sure, I felt like okay, we need to go after this particular market at this point in time, but now I’m actually going to cement that in data that shows what market and how we should go about that.

Justin Borgman 8:39
That’s exactly right. Yeah.

Tim Crawford 8:41
Okay. So when you become data driven, how does that kind of help you differentiate? You know, from a value standpoint, how do you differentiate between an organization that is data driven versus one that’s not? Right. You’ve talked about how well, one that might be leveraging data more effectively could be a disruptor in your particular industry. Are there other ways that being data driven provides value?

Justin Borgman 9:06
Yeah, I mean, I think for any incumbent business, it’s it’s essential to sort of stay ahead and be able to move quickly with the changing times. And and again, not to overuse the COVID example because that’s the example of the day, of course, but I I think you know companies that were able to understand the changing behavioral patterns were able to adapt very quickly and adjust accordingly. You know, I mean, I’ll use a very simple example just locally here in my in my neighborhood. You know, the dry cleaners, right? Like, you know, originally you had to go to the dry cleaners and drop off your clothes. Obviously, in COVID, that became a less appealing thing. And you could argue maybe maybe I don’t need to wear as many dress shirts, but I still try to when I’m you know speaking with banks. And so they quickly changed their their business and made it really one where you could digitally order someone to to come by and pick up your dry cleaning. You know from. Your front step, so you just put it in a bag and leave it outside your door, and they’d come pick it up and wash it, and then bring it back, which was actually a better quality of service than I was even getting before. And I think like that just shows like you know the the smart dry cleaner adapted very quickly to their customer. I’m sure there were other dry cleaners that just shut down and and you know kind of had to rely on you know government assistance to to be able to even maintain. So you know that’s just one example of the faster you can move, I think the the more successful you’re going to be in whatever industry you play in, and and data is the way data is the signal for for the types of changes you need to make.

Tim Crawford 10:36
Sure, and when I think about kind of what came first going down that path, you know the the different steps you might take. Do you understand the customer first, or do you look at the data first? Like for example, your dry cleaning example, would I look at the customer first, or would I look at data? And especially, maybe you don’t have as much connection to the customer. What about organizations that are less touch centric.

Justin Borgman 11:03
Yeah. Well, for some organizations, and maybe harder than others, I think you know wherever you have some kind of a digital interaction with your customer, you’re able to create data. Now, increasingly with IoT, that could be actually your products itself. You know, like Tesla creates a tremendous amount of data from the vehicles themselves that go back to Tesla and allows them to do predictive maintenance and understand what kinds of issues are going to impact other vehicles based on that. Look at the data sources you have. Try to understand what you can glean from that about the direction of of the business and where you want to go, and also think about how you can add additional data sources. What other things can you measure? You know, and really try to digitize as much of your business as possible because it makes it measurable, and then allows you to really gain insight from that.

Tim Crawford 11:50
Yeah, you know, one of the things that I often hear that is very much tied to being data driven is data literacy. How does data literacy come into play from your perspective.

Justin Borgman 12:02
Well, data literacy is really having the understanding of how to actually use data, how to use the tools of data, how to understand data, and that includes everything from kind of basic understanding of of statistics that you might have learned in school a long time ago, and brushing up on on you know linear regressions and and other sorts of understandings of of data, but it’s also becoming comfortable with the tools themselves. You know that might be business intelligence tools and how you can kind of chart and graph and draw correlations between data sets. It might be learning SQL as a language. You know SQL for someone who’s not a programmer, might sound intimidating, but it’s actually a fairly simple language to learn. You you can learn enough to be at least dangerous pretty quickly, and then be running your own queries, and and that’s really really powerful. And I think like increasingly organizations want to be able to move in the direction of having self service consumers, basically consumers within their organization, that can consume the data that they need at the time that they need it, and not have to necessarily go to a central authority in IT or what have you to ask questions of the business’s data.

Tim Crawford 13:15
And when you’re talking about consumers of the data, you’re talking about, for example, employees or folks that would be outside of IT, maybe it’s a business unit or product group that is looking at data.

Justin Borgman 13:28
That’s exactly right. You know, a a trendy term these days is certainly data scientist, but very often that implies the Harvard physics PhD who who became a data scientist because there weren’t any physics jobs available, and that person’s essential to the organization. Don’t get me wrong, but you can’t find enough of those, right? So it’s how do you kind of turn everybody into something of a resident data scientist, and and I think that’s really where literacy comes into play. The product manager has important questions. the The person in marketing has important questions. You know, they shouldn’t necessarily have to get a degree in computer science or or advanced statistics to be able to ask the questions that they have.

Tim Crawford 14:09
But if I’m not one that’s necessarily carved out to learn R or SQL or one of these other languages, I mean, can I still be data literate and use a tool like Excel.

Justin Borgman 14:23
Yeah, absolutely. We often talk about in our industry that Excel is probably the most popular BI tool out there.

Tim Crawford 14:30
Still, yeah,

Justin Borgman 14:32
still is still to this day because it’s really simple to use and it’s very powerful. Excel isn’t scalable. That’s the only issue. You can’t manage massive amounts of data with it, so it does have a limit to what you can do. But

Tim Crawford 14:44
can only go so far.

Justin Borgman 14:45
Yeah, right. But it’s a great way to get get comfortable with data and get started with data. And then, you know, there are other tools out there that are really striving to make data more consumable. Tableau is is one that comes to mind. As a business intelligence tool that makes very beautiful visualizations relatively easy by dragging and dropping data sets and and things that you want to chart. There’s another one called Looker. There are a number of different tools out there, but I think the industry as a whole is very much trying to work hard on making it more consumable to a broader audience.

Tim Crawford 15:20
Sure, and we’ve all probably experienced Tableau, whether we knew it or not, through the pandemic. A lot of organizations, a lot of health departments, even kind of citizen scientists, data scientists, have been using Tableau to culminate this data, bring it together, and present it in a way that you know the average person can consume it.

Justin Borgman 15:42
That’s absolutely correct. I think that’s a great example that you you mentioned there. I mean, here in Massachusetts, where where I live, the COVID statistics are actually presented. At least in the city of Boston, the COVID statistics are presented in a Tableau dashboard. And so, whenever I want to see the latest numbers, I see a little Tableau down there in the right hand corner.

Tim Crawford 16:02
No, that’s great. That’s great. So I’m in California and specifically Los Angeles, which for a while was the COVID capital of the U.S. Not exactly a moniker of data that we’d like to have. But getting back to data literacy, one of the things that you’ve talked about is the importance of being data literate org wide. You know, thinking about it across the entire organization as an executive. Why should I be thinking about that? Shouldn’t I? You know, versus the counter of that, which might be, I need those data scientists. I need a team that is focused on kind of managing and massaging and working with that data. I mean, how do I start to navigate between what seems to be two vastly different perspectives?

Justin Borgman 16:50
Yeah, that’s a really important question, and I think there’s a little bit of philosophy behind maybe the answer that I would give there. I think I think there’s always purpose to having a specialized group for particular tasks. So, for example, you’ve got those really strong data scientists. Maybe they need to build a machine learning model. Maybe they need to create a recommendation engine. If you buy this pair of pants, you might want this pair of shoes. You know that that’s a great task for that group of specialists. But I think broadly, the reason you want this knowledge to be as ubiquitous as possible is because I think it’s important, and this is certainly something I wrestle with as CEO as well, to try to decentralize as much as possible, just in for the sake of scalability, right? Like if every question has to run through a small number of individuals, you’re just not going to be able to move as fast, right? You’re hiring people that you think are smart and you think are great. You know, it makes a lot more sense to kind of give them the tools to be able to iterate faster and ask their own questions, get their own answers, and move on with the business without having to to necessarily funnel that all through one bottleneck. So I think that’s why it’s important. I think this is all really in the name of velocity.

Tim Crawford 18:05
Does it also impact your response? Considering those folks out in the other organizations understand the data or the context for the data, does that play a role in this too?

Justin Borgman 18:18
I think that’s true as well. I mean, I think you’re going to be more likely to trust proposals, if you will, or budget requests, resource allocations of headcount, or what have you, that are data driven. It’s just naturally going to be more credible as well. I’m not going to be as receptive to somebody says, “I don’t know, I’ve got an instinct here. We should probably spend some money on this, right? As opposed to the person who comes to me and says, “I’ve done the analysis. Here’s what the data tells me,

Tim Crawford 18:44
and I work in that particular group, and so I understand the context in which that data is representing.

Justin Borgman 18:50
Exactly.

Tim Crawford 18:51
Yeah. When I go through data, though, one of the other conversations that often comes up is this concept of data maturity. So I guess the thing I’m curious about is kind of your perspective on: is data maturity important? Is it something I should be thinking about, and how should I be thinking about it? What’s the importance of it? Are there stages to that maturity? How do you think about data maturity?

Justin Borgman 19:16
I think it is important, at least in the interest of of trying to be self-aware in the organization that you’re in, of where you kind of stand in that in that journey of of maturity, you know, for a lot of folks, maybe just getting started with data, it’s it’s just trying to stand up a database in the in the first place and and get the right data in there and be able to run queries and and maybe that’s the the earliest kind of phase of of maturity, but I think as you as you start to mature, there are a couple realizations that take place. First of all, the technologies that you choose to to bring to bear, you know, might be increasingly cloud oriented. They might be increasingly open source derived, just because there’s a lot of benefits from working within a community that open source. Offers, and I think you know. Ultimately, people start to come to this conclusion that, as we said in the opener, sort of the only constant is change, right? And and if you if you accept that as the reality that there is no end state, like the end state is always changing, you’re always working towards building out your data infrastructure, your data literacy. Then you want to try to create an environment that facilitates that, you want to choose technologies that are adaptable to a changing future. You want to build a culture of learning and training within your organization to continue to further that that data literacy. So that’s the way I would try to think about it as as sort of a journey rather than a an explicit destination that you can arrive at. You know, on a specific day.

Tim Crawford 20:42
No, and I think that’s a great way to kind of wrap on our conversation too. Is that working with data is a journey. It’s not. It’s not an end state as you go through this. So, Justin, we’re going to have to leave it right there. Thanks so much for taking part in the episode today.

Justin Borgman 20:58
Thank you so much, Tim. It was a lot of fun.

Tim Crawford 21:02
For more information on the CXO in the Know podcast, visit us online@cxointheknow.com You can also find us on Apple Podcasts or wherever you listen to your podcasts. Please subscribe and thank you for listening.