Piles of data, piles of dough, right? Not so fast. Despite an increasingly urgent, broad range of needs around processing data juggernauts, we’re seeing just as many startups go *poof* when it comes to turning Big Data into Big Money. Or Mini-Money, for that matter.
Data-driven startups should know by now that out of the gate, transforming data into cold hard cash requires a lot more than just showing up with a great team and a great idea. A holodeck would be ideal, but until we live in the future, we found one useful analysis that breaks down steps to filling up your bank account with data – and our translation of his model for wider application.
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Two years ago, Google Chief Economist Hal Varian was telling us that one of the sexiest (his term) future jobs was going to be related to statisticians wrangling data. No one disagreed, as stacks were piling up and a variety of needs were emerging, but what seemed to be missing were the connections between data and desire: crafting complex scaling into commodity, and connecting producers and consumers of data in harmonious, profitable and hopefully long-term relationships.
It’s no surprise that in the time since Varian’s opining (2008), we’ve seen oodles of small startups setting their sights to capitalize on Big Data. And now, we’re learning from their failures. But Big Data doesn’t need to be the place bright startups go to die. After a number of startup breakups with El Data Grande, Pete Warden came up with a tangible analysis of what the path is from stacks to riches. How To Turn Data Into Money is one way to approach a complex topic in a landscape of changing tools, and it’s well worth a look. He describes the process of identifying how to make data turn a profit. Warden reinforces the notion that we’re still in the early days of really knowing where the ‘big wins’ are with Big Data.
The overall issue is this: From the outlay, many startups are going to be sitting on a large bucket of data but won’t be in a position to imediately know where the monetization sweet spot lies. As Warden suggests, they will have to go through a series of processes that enables them to zero in on how to provide the maximum amount of value by iterating in partnership with their customers/users.
Warden begins with suggesting the first step might be to summarize the data and provide simple graphs. This allows everyone, your customers and your own team, to really understand what the data might show.
As feedback is obtained from this initial process, key metrics and other indicators can be focused on in reports. This will begin to allow you to answer specific questions that will (hopefully) be of value to your customers.
It’s no surprise that your customers, once identified, are going to be where you go for answers to their needs. Iterating your business in response to working with your customers – which is always valuable no matter what vertical you are in – will ultimately bring you to a point where you can provide business intelligence and actionable recommendations for your customers based on what they are already doing with the data.
Being able to point out specific trends, suggestions and points of friction (contextual to your data’s domain) should be of great value and something your current and future customers will be willing to pay for.
Finally, Warden touches on how your shiny data should be presented – dashboards are commonplace, but clearly we’re in very early days in this space. What’s important to remember is that what’s being built here is a combination of product and consultation. If you need an initial framework to begin tackling the problem Warden’s post works as a great framework, but given that Big Data could be about anything, you will need to consider your domain space, the nature of the data and your own expertise to be able to know whether this will work for you.
What we do know is that this space is going to be competitive (and rapidly changing) and remaining nimble will ultimately be crucial to success or failure.
How To Turn Data Into Money (petewarden.typepad.com)
Image via the USGS Data Dictionary.
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