Keeping Up With Data #88

5 minutes for 5 hours’ worth of reading

  • How to unlock the full value of data? Manage it like a product: Managing data like a product is a trending topic promising better success than grassroots or big-bang strategies. Grassroots approach can lead to inefficient, duplicated work and resources. Neither works the big-bang strategy (though often being triggered by big management consulting firms), which is resulting in isolated, technology-first, centralised teams. The article presents very nice graphics about data as a product (a benefit of having smart people working with dedicated graphic designers). The takeaway message — how to get started with data products — is at the end. Among other advices, the authors recommend having dedicated management and funding. If you want to make data products successful, they must have an owner, and budget. After years of strategic work with C-level executives, management consultants know that better than anyone. (McKinsey)
  • An 11-point checklist for setting and hitting data SLAs (with an SLA template): Data products offer an ongoing service to consumers who expect certain level of the service. That’s where the data service-level agreements can be very useful. Formal data SLAs are helping build the trust of consumers in data. There are six elements the data SLAs should include: purpose, promise, measurement, ramifications, requirements, and signatures. But the consumers will only trust the data if the SLA is met. The article shares some strategies helping data teams hit data SLAs. Empty promises are worthless (maybe even harmful in this case), so data teams will appreciate these pointers to help them deliver on the public promises made. (Databand)
  • Artificial intelligence and avalanche warning: The Swiss institute for snow and avalanche research is publishing an avalanche bulletin daily. So far it has been a domain of three experts studying the changes in the weather, updated weather forecasts, feedback from observers, mountain guides and backcountry tourers. Now, they are also consulting an AI algorithm generating its own appraisals. Two things stand out: the experts are still in charge, it’s yet another data source for their decision. And secondly, it’s interesting to read that the model only works for dry-snow avalanches, and that wet-snow avalanches and snowpack stability require different models (already available). (SLF)

In case you missed the last week’s issue of Keeping up with data



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Adam Votava

Adam Votava


Data scientist with corporate, consulting and start-up experience | avid cyclist | amateur pianist |