“Data-driven” is such a passive phrase. Who the driver is matters a great deal. When you think data science, the image of models, algorithms and statistical methods comes to mind. So, why, then, when you mention data science in many companies do you get the response “that’s all well and good, but I just need a chart to see some things”? I know it feels like that “devalues” data science in some way. “They just don’t appreciate what I can do” is what I hear from those who have experienced those interactions. I don’t think that is quite right.
Nice writeup Eric. Tbh, when I saw the headline I got a little queasy. I've spent a lot of time in the IoT space and the word "actionable" should come with a trigger warning.
I'm curious about your (perhaps implicit) definition of the data consumer. From my reading, it seems that you treat the typical consumer as internal: bosses, devs, analysts... Seems like this is a familiar definition for a typical data engineer.
At what point do we start to talk more about the external data consumers (e.g. our customers). How do we make our data more consumable and actionable for them? Said differently, how do we start to actually incorporate the data into the product, especially as data shifts more and more towards streaming architectures in a lot of use cases?
Great article. I would challenge that there is a #6. Helping the business see the "Art of the Possible" or discover what ELSE can be done with Data. Something they never knew possible or existed. Many times the business asks for what they need reactively due to a specific need. With good Data products, that are valuable, operationalizing them for others to use, and helping users in turn discover what is out there will help companies become a little more "Data Driven."
To me, "Data Driven" means that the company is "Data First." Every discussion, decision and application considers the Data aspects as the foundation.
Digital Transformation STARTS with DATA Transformation (Or at least a solid but nimble Data Foundation).
Thanks for the article and those tips to improve actionability. One thing that I don't understand is the part where you talk about the driver. When we use the expression "data-driven", isn't the data the driver? Of course, the data scientist is imposing a model, but they let the data drive the discovery of insights or patterns. Could you please elaborate more on that? Thank you.