Keeping Up With Data #88
Many companies in traditional industries are now being approached by their customers asking for more data-, or analytics-powered solutions.
These companies have often been dismissing the importance of data, analytics, and automated intelligence in their line of work, and are therefore very unprepared for the requests made by their customers. These requests are putting them on the back foot, forcing them to reactively respond to needs their internal functions and processes are not ready to meet.
Trust me, building a data function under external pressure with very real deadlines and consequences is no fun. One then realises that data is not a team to hire, or a tool to buy. That data function cannot be siloed from the rest of the business. That a culture shift is often needed. And much more, all under surveillance of customers who are increasingly considering moving to competitors with every week you waste.
If you are not yet in the situation, count yourself lucky and start getting ready. If you are in the situation, get external help — you’re not the first one with the problem.
Either way, after waves of technology and digital transformations, data and analytics will be the next one.
Here are my recommended reads to get you ready.
- 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)
Not very high risk of avalanches now, but it’s good to know the Alpine country has a dedicated research institute constantly looking to improve the warning systems. And it’s refreshing to read about snow in the summer, isn’t it?