The One true measure of achieving analytics capability maturity

This weekend was not great for me. The sudden drop in temperature gave me cold that started getting bad by Friday evening and was worse by Satuday. I still started working on my Deep Reinforcement learning lessons (because all I had was the weekend to work on it), hardly got anything while going through the lessons and videos, still ended up attempting graded questions, and got most of them wrong. Nothing seemed to be going my way.

So I needed to relax.

One thing that I love doing that helps me relax, along with two other hobbies (gardening and drawing), is assembling electronics projects using kits. This is what got me into experimenting with IoT projects (mostly Arduino) at home and then into customizing a laptop/machine for deep learning (on which I am yet to run even a single deep learning project- I mean one that needs the computing power that I have built into this thing).

So, I had just replaced a HDD in my β€œamazing” customized laptop and was now taking the old HDD apart to check out the internal components.

Before I embarked on my journey of opening my laptop, my wife and I were having a discussion about something that veered into a question she had- If someone were to assign one, just one criteria to evaluate whether an organization has achieved true analytics capability- what will that be ?

Sidebar: Why do I mention my wife often in such discussions ? Does she like discussing “nerdy” topics ? Well, she is also an analytics professional for sure but does she like discussing these things all the time ?…not so much. But she is stuck in a marriage with someone who after day’s work, reads about the same stuff that he just decleared he was done with ….and then after dinner, creates some infographics…on the same topics. Jesus…she has been in a weird marriage for more than 12 years now πŸ˜‚πŸ€£πŸ˜. So when I am not actively irritating her, we discuss only one of these three things- something related to what I am reading, our son or our in-laws πŸ˜‰πŸ˜‚ And most of what I read, is kind of nerdy, even if sometimes entirely not related to data or analytics (For example: I read “Narconomics: How to run a drug cartel” last week 😁)

When she asked that question (“If someone were to assign one, just one criteria to evaluate whether an organization has achieved true analytics capability- what will that be ?”), I was pissed off with how my weekend was going so I did not put much thought into answering that question. But as I was destroying the old hard disk drive (by experimenting with it), it occured to me how the two pieces of the HDD that I had in my hands could be used to answer her question. And after having explained it to her using the HDD components, I realized that it was indeed a beautiful analogy, so I created a video this morning with the same example- answering the question:

What is one true criteria to measure holistic analytics capability of an organization ?

No- Citizen Data Scientists are not Data Scientists (and they should not be)

Now, don’t confuse the term “citizen data scientist” with data science capabilities. Heck, even data science itself is not a rigidly defined term but it includes a gamut of analytical approaches, with advanced analytics approaches on one end of the spectrum. Your true data science capabilities should be a distinct capability and both, centralized strategically in hubs across the organization as well as embedded in some key functions.

What the term “citizen data scientists” means is that every person in your organization, across each function, developes a certain level of analytics capability. And of all the analytics approaches you take, this will be the most exhaustive one.

Sidebar: Do you know that Amazon hires area managers (folks managing a certain area/process of a warehouse (ex: Inbound) from business schools ? This would have been considered insane a couple of decades ago and beneath the “eliteness” of many MBAs. But now MBAs line up for those roles and Amazon leverages the power of Analytical minds to run their day to day operations. These folks eventually then rise up into the operations leadership roles across the warehouse operations organization. Amazon realized long ago that that the true power of analytics comes into play only when you embedd it in your front line. The more “analytics powered” folks you have on the frontline, more progress will be made towards developing true analytics maturity.

The reason it is an exhaustive approach is because this can not be a blanket approach. Just to plan and design what type of capability is needed will be a massive exercise in itself, since you have to customize it for every role in every department. And on top of it, this needs to happen AFTER you have redesigned the organizational structure to align with the new reality. It will be an extremely detailed and painful strategy to define but one that will pay off handsomely for you.

Views expressed are my own.

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