Part 2: Data Monetization Strategies
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You know the next thing that's sort of on my mind is: how do organizations really think about their data monetization strategy? There's so many out there, and maybe for both of you both from an ISG perspective, a service provider perspective, and really even an enterprise perspective, what should our data monetization strategies be?
I'll start there. I would start thinking about:
- Is the data by itself valuable?
- Or is the data powering new experiences and new moments of insight?
And so, your monetization strategy could be just giving access to the data in some way. Or it could be building experiences and use cases that are driven by data. And I'll take a classic example: benchmarking which you, Kathy, can talk to is a great example of a data monetization where you're not really always giving away all the data, but you're allowing – whether it's a price benchmark, whether it's cost benchmark – it's the power of the data, but you're really allowing people to take a specific point in time insight. This can often be more valuable, both from a monetization perspective as well as to the customer.
That's good. So, there's different aspects to how you think about data and insights.
I think that's a great point. I know that when we look at the data assets that we have, we really sit back and think:
- Is this data we should just give access to direct? Let people make their own assumptions, make their own connections with the data. We have this data, it's in a structured format, but we'll just give you access through an API.
- Or is this data something we should curate and deliver back more of an experience. As you said, Sunder, give them an experience with the data that shows them what to think about in the data, give them output maybe in form of dashboards that can be customized to them and what their needs are.
So, we go back and forth, and sometimes one data set will be both. We may use our rates database, for example, to expose it to people to just pull rates at their leisure. Or, we'll curate it out. So, you can say, “okay, I'd like to ask a question of the rates database. What are we seeing, for example, in a specific industry with specific skill sets? What are the day rates? What are the averages?” And that's just something we curate into a dashboard versus just giving them a full dump of the data.
Yeah, no, absolutely. You know from an enterprise standpoint, I've got two good examples that I always think about. It always goes to the core of really what are your assets and what are your core enterprise assets? So, the first one is to Transport for London: getting on the tube. What's their core asset; what do they want to do? They want people to ride the tube, right? They want to not only do it from a monetary standpoint, but also to reduce congestion on the streets and everything else within London. So TFL is brilliant. What do you have to do to get more people on the tube? You got to make it really easy. How do you go from station to station?
It’s experience, the user experience.
Exactly. So TFL did something amazing. They took all of their data, they made it completely available free. Then all of a sudden, what you have is lots of people creating apps, creating different experiences that would drive people to the subs, you know, to the tube, which was brilliant. So, they didn't monetize the data itself, but they monetized the experience to drive it.
So, they kind of crowd-sourced their data?
They sort of, they absolutely, crowdsourced data intentionally. And you had a lot of startups that were able to take their data for free nowz, create their own businesses, and they sell things, whether it's on the app and they sell advertising, or other things.
It's all about that experience of riding the tube.
And it created a community around the tube that they wouldn't have created on their own.
They couldn't have created it on their own.
And they've not just crowdsourced the data, they actually crowd-sourced the design or the thinking about it.
The other one that really strikes me – again, it's always about core assets – is John Deere. John Deere is a tractor company in the middle of central Illinois, right. Moline, Illinois: it's not exactly known as the heartland for data. But John Deere said, you know, four or five years ago, what's my core asset? And it wasn't the tractor; it wasn't the farm equipment. Their core asset was the fact that their equipment was on every farm in America.
So that meant they understood the soil; they understood the climate; they understood the environment; they understood what was happening with the nutrients in the ground. And they said, you know what? Maybe our data, our core assets are sensors that we can plant in the ground because we're on all of these acres of land. We have the tractors. We provide and sell that data to climate companies; we sell it to the weather channel; we sell it to IBM, we sell it to Monsanto; we sell it to archer Daniel Midlands; we sell it to Conagra – things that completely monetize their data.
But it wasn't things that you would say, well John Deere is going to create a digital twin and sell that. So, stepping back and really thinking, “what's your core asset; what are the things that differentiate you in the market; and how do you create data monetization strategies around that? You know, Facebook is easy, right? They want eyeballs. So, the more eyeballs they get, they're going to sell data to attract advertisers to get our eyeballs. Easy model. Google: easy model. But I think every enterprise has a strategy to do that once they understand what their core assets are.
I was thinking about something someone said in the sessions we've been having the last couple of days, and it's like: “Don't design for the past or what you have now. Think about designing for the future.” That seems simple, but really, I think, sometimes we have to startle ourselves out of, you know, the status quo and think about: what is the future, and what should we be doing different and being creative.