Decisions Need to Drive Data Science
Blog: Jim Sinur
There has been and will continue to be a significant shift in how data is leveraged in this continually changing world. Until recently, the data science process of collecting, cleaning, exploring, model building, and model deployment ruled the data management mindset. In a world that is “steady as she goes,” this makes a great deal of sense. The amount of data to be curated is growing impressively, but the data science mindset is still on the scene dealing though pressed to its limits with big data. Two things will break this sole reliance on the data science process.
Dynamic Business Scenarios
In a world with operational KPIs staying steady with minor adjustments over time, focusing on data makes sense. That world is virtually gone with the elephant in the room being a pandemics at the moment. Tomorrow it could be natural disasters and the down-stream effects of climate change impacting geopolitical behaviors. There are many business scenario possibilities and combinations headed our way. We can’t afford just to explore data and have knee jerk responses.
Monster Data is Lurking
If you think big data is worthy of concern, just think about the monster data just around the corner, driven by higher volumes, more complexity, and even more inaccurate by nature. Organizations are bound and determined to take advantage of behavioral data that is further away from standard core operational data. Monster data includes all kinds of unstructured data that will contain digital footprints worthy of new types of decisions.
Either of these would require a major addition of new data processes, but combined data science processes alone just won’t suffice. I am not saying that data science will dim, but it needs some new additional turbocharging and methods that are not just focused on exploring structured and clean data.
Dealing with Changing Scenarios
There are several ways of dealing with scenario planning and practicing responses, but here is what I would encourage organizations to do. Many decisions will drive the data that is leveraged during these efforts.
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