The importance of being data driven with Justin Borgman

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.

Podcast Episode

Links

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

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

Starburst: https://www.starburst.io

Transcript

Tim Crawford:

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.

Tim Crawford:

Justin, welcome to the program.

Justin Borgman:

Thanks Tim. It’s a pleasure to be here.

Tim Crawford:

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:

That’s correct. I know 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:

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:

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 working on my MBA and basically met some guys in the computer science department while I was in school and informed my first business. So my first startup was called Hadapt. That was a query engine for data and 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 fast analytics on data anywhere particular. That’s what makes it unique that you could have data and in Oracle database, data lake, and 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:

That’s great. 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, 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:

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 the COVID pandemic is a great near term example of that where buying behavior changed almost overnight. Suddenly people were doing banking and grocery shopping and retail online more than they’d ever done before. And I think that’s a great example, 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 the modern age.

Tim Crawford:

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

Justin Borgman:

Great question as well. I’m going to guess that we’re somewhere maybe in the fourth or fifth inning. And I say that because I think the early innings to me were maybe 10 years ago 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 have now developed some level of competency around how they build these systems. And that’s particularly true of internet companies, the Lyfts and Ubers and Facebooks and so forth of the world. But that knowledge is now starting to seep into the rest of enterprise as well. And similarly “normal industries” are being disrupted by upstarts as well. And I think that is all forcing people to take data more seriously and become more agile in the way that they interact with data.

Tim Crawford:

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?

Justin Borgman:

I 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 Airbnb 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 then 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:

Hmm. No, that’s great. So, if I double-click on that a little bit and talk about where those opportunities are, regardless of the industry or if you have specific industries that you think about, where are those strong value opportunities? 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? There are a lot of different opportunities, but where do I get started?

Justin Borgman:

I think a very logical place to start is with what some people call customer 360. 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 what’s known as clickstream data, right? So you want to understand the journey there as they’re even evaluating you as a potential vendor or whatever it is that you sell, to then 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 customer success and really understanding that the sentiment of your customers and how likely they are to want to renew with you or buy again.

Justin Borgman:

So really all aspects of that customer journey are generally creating a stream of data as they work through your life cycle. And that’s an opportunity for you to understand every stage of that sort of funnel, if you will.

Tim Crawford:

One of the things, Justin, that I know you’re passionate about and talk about is the importance of an organization to be “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?

Justin Borgman:

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

Tim Crawford:

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:

That’s exactly right. Yeah.

Tim Crawford:

Okay. So when you become data-driven, how does that help you differentiate? From a value standpoint, how do you differentiate between an organization that is data-driven versus one that’s not? 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:

Yeah. I think for any incumbent business, it’s essential to stay ahead and be able to move quickly with the changing times. And again, not to overuse the COVID example because that’s the example of the day, of course. But I think companies that were able to understand the changing behavioral patterns were able to adapt very quickly and adjust accordingly.

Justin Borgman:

I’ll use a very simple example just locally here in my neighborhood, the dry cleaners, right? 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 can argue maybe I don’t need to wear as many dress shirts, but I still try to when I… speaking of banks. And so they quickly changed their business and made it really one where you could digitally order someone to come by and pick up your dry cleaning from your front steps.

Justin Borgman:

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 that just shows the smart dry cleaner adapted very quickly to their customer. I’m sure there were other dry cleaners that just shut down and had to rely on government assistance to be able to even maintain. So that’s just one example of the faster you can move I think that the more successful you’re going to be in whatever industry you play in, and data is the signal for the types of changes you need to make.

Tim Crawford:

Sure. And when I think about what came first going down that path, 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 what 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:

Yeah. Well, for some organizations and maybe harder than others, I think wherever you have some digital interaction with your customer, you’re able to create data. Now, increasingly with IOT, that could be actually your products itself. 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 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 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:

Yeah. 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:

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 basic understanding of statistics that you might’ve learned in school a long time ago, and brushing up on linear regressions and other sorts of understandings of data. But it’s also becoming comfortable with the tools themselves. That might be business intelligence tools and how you can chart and graph and draw correlations between datasets. It might be learning SQL as a language. SQL, for someone who’s not a programmer, it might sound intimidating, but it’s actually a fairly simple language to learn. You can learn enough to be at least dangerous pretty quickly, and then be running your own queries. And that’s really, really powerful.

Justin Borgman:

And I think 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:

And when you’re talking about the 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:

That’s exactly right. A trendy term these days is certainly data scientists, but very often that implies the Harvard Physics PhD, 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. So it’s, how do you turn everybody into something of a resident data scientist? And I think that’s really where literacy comes into play. The product manager has important questions, the person in marketing has important questions. They shouldn’t necessarily have to get a degree in computer science or advanced statistics to be able to ask the questions that they have.

Tim Crawford:

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

Justin Borgman:

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

Tim Crawford:

Still.

Justin Borgman:

Yeah, still. It’s 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:

It can only go so far.

Justin Borgman:

Yeah. Right. But it’s a great way to get comfortable with data and get started with data. And then there are other tools out there that are really striving to make data more consumable. Tableau 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 things that you want to chart. And 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:

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 citizen scientists, data scientists have been using Tableau to culminate this data, bring it together and present it in a way that the average person can consume it.

Justin Borgman:

That’s absolutely correct. I think that’s a great example that you mentioned there. Here in Massachusetts 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:

That’s great. So, I’m in California, and specifically Los Angeles, which for a while was the COVID capital of the US, not exactly 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, thinking about it across the entire organization. As an executive, why should I be thinking about that, versus the counter of that, which might be, “I need those data scientists. I need a team that is focused on managing and massaging and working with that data.” How do I start to navigate between what seems to be two vastly different perspectives?

Justin Borgman:

Yeah, that’s a really important question. And I think there’s a little bit of philosophy behind the answer that I would give there. I think there’s always a 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.” That’s a great task for that group of specialists.

Justin Borgman:

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 for the sake of scalability, right? If every question has to run through a small number of individuals, you’re just not going to be able to move as fast. You’re hiring people that you think are smart and you think are great, it makes a lot more sense to 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 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:

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:

I think that’s true as well. I think you’re going to be more likely to trust proposals if you will, or budget requests, resource allocations of head count, 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 who says, “I don’t know, I’ve got an instinct here. We should probably spend some money on this.” 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:

And I work in that particular group, and so I understand the context in which that data is representing.

Justin Borgman:

Exactly.

Tim Crawford:

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 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:

I think it is important, at least in the interest of trying to be self-aware in the organization that you’re in of where you stand in that journey of maturity. For a lot of folks, maybe just getting started with data, it’s just trying to stand up a database in the first place and, and get the right data in there and be able to run queries. And maybe that’s the earliest phase of maturity. But I think as you start to mature, there are a couple of realizations that take place. First of all, the technologies that you choose to bring to bear, 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 ultimately people start to come to this conclusion that, as we said in the opener, the only constant is change.

Justin Borgman:

And if you, if you accept that as the reality, that there is no end state, 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 data literacy. So that’s the way I would try to think about it, as sort of a journey rather than an explicit destination that you can arrive at on a specific day.

Tim Crawford:

No, I think that’s a great way to wrap on our conversation too. Is that working with data is a journey, 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:

Thank you so much, Tim. It was a lot of fun.

Tim Crawford:

For more information on the CxO In The Know podcast, visit us online at cxointheknow.com. You can also find us on Apple podcasts or wherever you listen to your podcasts. Please subscribe, and thank you for listening.

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