In this special edition of CapTech Trends, Vinnie chats live with one of CapTech’s data leaders, Arjun Baradwaj, about how companies can expertly leverage their data as a revenue stream through data productization.
Vinnie
Arjun, thanks for joining me today. Can you define what data productization means?
Arjun
Sure, data productization, as a whole, is understanding and harnessing your data to generate revenue in some way or another, whether that's enhancing internal products, building new customer experiences, or really just selling it.
Vinnie
Right. So, we think about where people have been traditionally, I know you have some numbers on this, what we think about data as reporting, analytics… We talked about it internally as actionable insights. How to drive machine learning algorithms from the data. That's one side of it, right? So, talk a bit about how companies are doing that, versus companies that are going the extra step, and actually making it a revenue stream.
Arjun
Sure. And I think it's actually really interesting over the last five years or so since 2016, we've had about a 20% increase from about 30 to 50% of organizations who are now using their data to actually create a revenue stream. But the number that we really want to focus on is that about 85% of folks and organizations that are actually using it to drive insights. And that's our base layer. Driving insights is that intro to data. We really want to focus on the 50% and how we turn that into an increasing number. CapTech just put out our actionable insights survey where we interviewed a number of executives across our clients and other organizations and found that over 40% of folks interviewed still are not happy with where they're at in their usage of data and their use of data productization. What they really want to see it is how to turn a profit off it.
Vinnie
So, rehashing that a bit, 85% of people are using data in a traditional way to gain insights. And almost half of them don't feel like they're doing that well.
Arjun
That’s right.
Vinnie
In the last five years, we've gone from, say, 25% to 50% of companies who are actually productizing. What I want people to take away from that is that may not seem like a lot, but that's a tremendous amount of momentum. We can kind of go into the next topic on this, which is, what is the maturity model? So, I look at both actionable insights on machine learning and prioritization as relying upon good old fashioned data engineering. 80% of this is just getting the right data, the right amount of data from varied sources that's trusted, at the right speed at the right time, getting healthy data. That’s the 80/20 rule. The stuff we're talking about is the 20%. So, what sits on top of that?
Arjun
At the foundational level is where we're talking about actually driving insights with data and really building out your information layer. You have to do the hard, heavy lifting of the data engineering first. The 20% is actually where you're talking about building new customer experiences, and potentially reaching brand new customer markets as well. I think that the very tip of the pyramid, the tip of the maturity model, is really those new customer experiences for brand new customer markets.
Vinnie
I think that’s a really key point, because we have this image as a pyramid, you know, hierarchy of needs kind of thing. But I don't think that tells the best story, and that people think the entire foundation has to be in place before the next level has to be in place.
Arjun
I think starting small and getting those wins early is incredibly important, because that's where you get your feedback loop. You know, you make a successful implementation on a data productization product on an actual build, and then you turn around and learn from that. And that's when you go into your next use case. There you can show the business that you're actually making progress, you can immediately have an impact on customer bases, your current target market, and then you ultimately improve the people process technology part of your entire productization process.
Vinnie
So, we're talking about moving from reporting insights and analytics to productization, which includes selling it directly or indirectly using it to provide new features and functions of your app. So how do companies get there? The first thought that comes to my mind is that a Chief Data Officer role or similar type of data leader in the organization, have an equal seat at the table with digital, with the business, with IT, as opposed to feeling back office. What else is important?
Arjun
I think once you have that person with a seat at the table, their job is to wrangle everyone and get your digital groups and your IT and all sorts of business functions on the same page that data is a foundational asset to your entire organization. So having an enterprise-wide data strategy, and not just saying, the digital department has data here, IT department has data here, and your individual application owners have data there. It's really getting everyone on the same page and treating data as a foundational aspect. And where that really leads to is the importance of data governance. If you understand that data governance, and the ability to control access, privacy, and ultimately the integrity of your data, you get everyone on the same page from a data strategy perspective, then you turn around and you can create products, you can create improve process, where every single part of your organization agrees that the data is accurate. You’re putting a product out where your customers are going to trust it.
Vinnie
Yeah, so speaking about the enterprise data strategy, I'll go one step above that. I agree that it needs to be there. There needs to be a corporate strategy, independent of that, that when I said the Chief Data Officer and others have to be at the table, they have to have shared priorities. And those priorities need to align to a shared strategy, which supports a shared vision, right? But having those shared priorities is critically important and knowing what each other are with the capabilities of all these other functions are, how digital respecting understanding how data can impact digital, right, and vice versa. So great, thank you for chatting with me today. This was great.
Arjun
Thanks for having me.
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