Keeping Up With Data #66
5 minutes for 5 hours’ worth of reading
Brent Dyke’s LI post about the data analytics marathon created so many positive reactions that he turned it into a Forbes article (see below). I’m also using sport analogies often when talking about data and analytics. But I slightly prefer cycling to running (both as a data analogy and as a sport). The reason is that in cycling you don’t have to be pushing hard all the time, but you have to go full gas when it matters. And with data and analytics it’s imho the same — it’s not just about the effort and perseverance. It’s about targeted calculated effort. Racing tactics matter in data analytics too.
I hope you’ll enjoy the following articles about data literacy, metrics layer, and data analytics marathon as much as I did.
- Data Drives the World. You Need to Understand It: An essay on the importance of data — the fuel of the digital economy. “As Aiken and Harbour note, data is unique (it keeps its value), non-depletable (you can’t use it up), non-degradable (it’s accessible forever), regenerative (it can be used over and over), and its cost diminishes with use.” All that makes data an incredibly powerful asset. And with power comes responsibility. Being data literate is now more important than ever. What is data literacy? “There are two very different kinds. One is knowing how to analyze and interpret data. […] The second kind of data literacy involves understanding how the data economy works […] and how much your data is worth.” And boy, it’s worth a lot! (Time)
- The metrics layer has growing up to do: Shared metrics layer, or headless BI, is gaining a lot of traction lately. Why repeating the work to define the standard business metrics again and again? But where to define these metrics once and for all? Should we encapsulate the metric definition, the whole semantic layer, or even both semantic and query generation layers? ThoughtSpot’s CTO is arguing for the last option because it makes it truly independent on the visualisation layer. But it brings another challenge: “we need is a language that expresses a business user’s intent, and combines the intent with predefined data models to generate queries.” Is SQL such a language? (Amit’s Newsletter)
- Data Analytics Marathon: Why Your Organization Must Focus On The Finish: Succeeding with data analytics is a long-distance effort. Organisations need to get through multiple key milestones on their data analytics journey. And the journey is not short, it’s not a sprint. It’s a marathon! And once you finish, you start another one. Again, and again. But failing to finish a data analytics marathon makes all the effort (and investment) useless. The finish is where your company is rewarded with a business impact. And just like in a running marathon, the last mile — taking action — is the most critical. The author comes with three suggestions to make the last mile easier: (1) automate early-stage tasks, (2) narrow the scope, and (3) foster a stronger data culture. (Forbes)
Another busy week is coming to an end. I haven’t done much running (apart from running the analytics marathon!) and no cycling at all. But there is a whole weekend to change that!