Here's a look at the latest data news and developments that caught my eye.
Registration open for Strata 2011
You can find out just what has us so excited about data at the O'Reilly Strata Conference, Feb. 1-3, 2011, in Santa Clara, Calif. early registration rates are available through December 14.
The Strata program features tutorials on data and visualization, an executive-level briefing event on big data, and two days of conference sessions and keynotes. We'll hear from big business, startups, and the brightest developers and researchers. Watch for further details about the schedule over the coming weeks.
We find ourselves at the beginning of an industrial revolution of data, heralded by unprecedented volumes of data and connectivity, cheap and ubiquitous computing, and advances in interface technology. Strata will be the defining event of this movement, so I very much hope you'll join us there.
From data to money: Building a startup
Thanks to commodity computing power, it's possible to build a startup business based around big data and analytics. But what does it take to do this, and how can you make money? These questions were addressed recently in blog posts by Russell Jurney and Pete Warden.
Jurney takes on the question of how many people you need to start a data product team. He draws out the ideal roles for such a team including: customer, market strategist, deal maker, product manager, experience designer, interaction designer, web developer, data hacker and researcher.
Quite the cast, and not really the ideal starting point for a product or business startup, so Jurney condenses these roles into the more succinct definitions of "hustler," "designer" and "prodineer" -- a minimum of three people.
Analytic products are such a multidisciplinary undertaking that in a data startup a founding team is at minimum three people. Ideally all are founders. There are probably exceptions, but that is the minimum number of bodies required to flesh out all these areas with passionate people who share the vision and are deeply invested in the success of the company. Someone needs to be good at and enjoy each of these roles.
Once you start, and have a minimal product, Jurney recommends quickly connecting with real customers, and taking it from there. The next step is making money, of course, which is what Pete Warden has been thinking about.
After running through a "thousand ways not to do it," Warden reckons finding a way to make money is the most important question for big data startups. He paints the stages of evolution a data product goes through to actually deliver value to customers.
- Data: You need it, but selling it raw is the lowest level of business. Warden writes "The data itself, no matter how unique, is low value, since it will take somebody else a lot of effort to turn it into something they can use to make money".
- Charts: Simple graphs, which at least help users understand what you have, but "still leaves them staring at a space shuttle control panel, though, and only the most dogged people will invest enough time to understand how to use it."
- Reports: Bring a focus to what the customer wants. Many data-driven startups stop here and make good money doing that. But there's further to go: "It can be very hard to defend this position. Unless you have exclusive access to a data source, the barriers to entry are low and you'll be competing against a lot of other teams".