Inspiration for the ‘nimbility’ posts:
I listen to a lot of podcasts. And I mean a lot. When I am not binge consuming true crime podcasts, I explore the pod-sphere for casts about data. There are several good ones, and more being added all the time. My favorite podcast about data, so far, is called Data Engineering Podcast hosted by Tobias Macey. I like it because it is about the nuts and bolts of data management, governance, and consumption. True confession: I don’t always understand everything that he talks about with his guests, but I always come away with at least one or two concepts that I can make my own.
I hit the jackpot the other day when listening to an episode called “An Agile Approach to Master Data Management“, with the guest Mark Marinelli, Head of Product at Tamr. Mark made numerous points that resonated with my own experience, and I am sure I will be revisiting these points in other posts. The one I want to focus on today is Mark’s comment about the need for the “right tool set, right mindset, right skill set” when working with data in today’s era of data proliferation.
Mark’s point, if I can take the liberty of paraphrasing him, is that you can’t always wait for ‘perfect’ data.
Creating a canonical data set takes time. It almost always involves a concerted effort from a team of people and a formal project management cadence. By the time the ‘blessed’ view of the data is released, the competitive opportunity suggested by an innovative data mashup may have evaporated. Hence the need for ‘nimbility‘ when approaching your data opportunities.
(Yes I did just mashup agility and nimble!). ‘Nimbility’ requires a group of people with the right tool set, mindset and skill set.
Specializing Microsoft’s Power BI as I do, the right tool set part of the equation is taken care of, from my point of view. (I realize that there are many other excellent products out there, including Tamr, but I am not familiar with them, so will confine my comments to the Microsoft space.) But what about right skill set and right mindset? How must you think about your data if you are going to be comfortable in a brave new world of data mix-ins that are “home brewed”? How can you use Power BI the Power Query Editor, and the Power BI Service to provide some documentation and provenance to your innovative data combinations?
Next up: A ‘nimbility’ mindset towards data