“DATA-DRIVEN” TERMINOLOGY IS OVERHYPED

By nurimroatun - March 28, 2022


“Through 2022, only 20% of analytic insights will deliver business outcomes.” (Gartner, 2019)

 

My take: It’s good to have a high ambition but more importantly to have the right ambition. It's good to start by "We want to have data analytics projects/use cases. What should we analyze?" (as long as business values are the objectives), but the better path is "This is our objective, thus X and Y values should be created. What analytics should we perform?"

 

My personal note: I know the title is a bit controversial. Please don't get me wrong. I always think data science is awesome. I am also a big supporter of analytics implementation. Here is just a piece of my thought on creating better analytics  :)

***

 

Data is widely considered this century’s most valuable asset—beating oil, gold, and anything precious in world history. The attention has been gathered and the spotlight has been shone on its ability to drive business decisions. The four-letter word now has another buzzword: data-driven. 

 

The dream of becoming data-driven is basically on almost every organization's wish list. When an organization implements a data-driven approach, it means the strategic decisions are based on the analysis and interpretation of data. The insights, patterns, and anything behind the data will be uncovered to decide the actions. So now, that we have data (tons of it!), we all should jump to the analytics, right?

 

Unfortunately, history repeated itself. Data analytics, as its technological advancement predecessors, is treated as a black box: as if it, by itself, will magically solve all the problems.  In fact, the analytics process as a standalone is far from wizardry. There are a bunch of processes going behind the curtain to ensure that the results benefit us. There are a plethora of details that hold more meaning than the tools and techniques: data governance maturity, analytics strategy clarity, translation and communication effectivity to name a few--business objectives to name the paramount. 

 

In short, neither having data nor performing analytics sets an organization apart from the rest. The success indicator of analytics is not having use cases but gaining business value. Therefore, it should first and foremost be about action and value (Schmarzo, 2020). Values should always be the heart of every process, including analytics, performed by an organization. Consequently, every analytics should always be able to be converted into value. Value creation means we start our analytics with a clear vision. Always have a business improvement opportunity/problem statement in mind. Instead of wanting to have a data analytics project, ask ourselves, why do we want to have one? What problems/improvements do we want to get the answer to? Invariably, have an objective regarding what should be generated through a use case before creating one.

 

Many publications including Gartner reported that most analytics projects failed. Let’s not be surprised that the reason wasn’t that the organizations lack use cases/projects/techniques/tools. No, it was not. After careful observation, it was revealed that many organizations didn’t start with a clear vision and objectives. That particular approach is inherently prone to failure in delivering business outcomes.

 

In conclusion, always know what is the business value our organization wants to generate through analytics. The clearer our vision, the better.  From there, we know what data should we collect, the data governance maturity level should we aim for, which analytics strategy should we implement, and which use cases should be prioritized. (Yes, we have to prioritize them. It means, not every data set should be turned into a use case exactly now. Some can wait. Some can wait even longer.) 

 

Back to the title above: "data-driven" terminology is overhyped. It will continuously fail to keep up with the hype as long as it doesn't put "value creation" at the center. Ultimately, an organization should be value-driven at the core. Always. Without exception.

--------

References:

Schmarzo, Bill. 2020. The Economics of Data, Analytics, and Digital Transformation. Birmingham: Packt Publishing Ltd.

White, Andre. 2019. “Our Top Data dan Analytics Predicts for 2019". https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/. Accessed at 26 March 2022

 

image source: umanitoba.ca

  • Share:

You Might Also Like

0 comments