Reset
Results! Why, man, I have gotten a lot of results! I know several thousand things that won’t work. —Thomas Edison
I don’t announce every project I start, and I don’t finish every project I announce. The Data Market Study is one of the latter. I still think it’s a worthwhile project, especially if I were a business school professor needing something to publish. Since I’m not, it’s time to refocus, which means this newsletter is about to change.
I’m keeping the format, but the content is about to shift to some other things that interest me. Maybe some will interest you, too.
Signals
I had some good conversations with people in the data market, and I appreciate the folks who took the time to talk about this. Early discussions were giving me a Gantt chart-like view of overlapping lifecycles that apply in this market: data acquisition, data integration, and data science all have their own cycles, and it’s not clear to me how much anyone cares to have an end-to-end view.
Combining what I learned from several intermediaries, I was getting a sense of how to differentiate those players, based on which value they added:
Discovery of sources
Evaluation of datasets/streams (for a particular type of client/use case?)
Transaction processing on platform
Data access on platform
Data lifecycle management
This market still reminds me of SaaS markets with a multitude of competitors staking out different positions with overlapping – but not identical – feature sets. It’s still an interesting space, but I’m not trying to work it all out. At least for now.
And therefore?
This is the end of a project; there will be others. For the moment, I plan to use this space to think out loud. I’m fascinated with the broader concept of how organizations can use available sources and methods to know more about what matters. I’ll try some of the draft ideas here, mixed with some of the interesting items and people I’m discovering all the time.
Inspiration
I like what Jen Rice is exploring with O-type Humans: “masters of complexity and integration.”
Dave Karpf says it better than I did: What's in a name? AI versus Machine Learning
Discovery
Volunteers created better search-and-rescue software for drones.
The things you can learn with location data (and why “anonymized” doesn’t necessarily mean much)…
Report from the National Academy of Sciences / Royal Society workshop on data sharing for science.
Something different from the intersection of law, politics and technology: GeoLegalOps.
TBR
Ignorance: A Global History – Only 366 pages?
The Wrong Stuff: How the Soviet Space Program Crashed and Burned – Interesting history, and an opportunity to learn from the mistakes of others?