The value of data does not depend on size or shape of them. It really depends on how useful data are for decision-making. Some data geeks may not agree with me, but they are generally not the ones who fund the maintenance of spit-spot clean data in a warehouse or in a cloud.
From the business perspective, no one would invest an obscene amount of money for someone’s hobby. Sorry for being obvious, but data must be used by everyday decision-makers for them to have any value.
I have shared ways to evaluate various types of data in this series (refer to “Not All Databases Are Created Equal,” where I explained nine evaluation criteria), and even that article, written for businesspeople, can be considered too technical. If I may really simplify it, data is worthless if no one is using it.
Data and information are modern-day currency; piling them up in a safe does not increase their value. Even bigshots like CIOs, CDOs or CTOs should eventually answer to CEOs and CFOs regarding the return on investment. Without exception, such value is measured in dollars, pounds, Euros or Yuans, never in terabytes, megabits per second, instructions per second or any other techy measurements. What incremental revenue or extra savings did all those data and analytics activities create? Or, an even shorter question in a typical boardroom would be “what have the data done for the business lately?”
Like any field that requires some levels of expertise to get things done, there are all kinds of organizations when it comes to data usage. Some are absolutely clueless — even nowadays — and some are equipped with cutting-edge techniques and support systems. But even the ones that brag about terabytes of data flowing through their so-called “state of the art” (another cliché that I hate) systems often admit that data utilization is not on-par with the state of data themselves.
Unfortunately, no amount of investment on data platforms and toolsets can force users to change the way they make decisions. They have to “feel” that using data is easy and beneficial to them. That is why most job descriptions for CDOs include “evangelization” of data and analytics throughout the organization. And often, that is the most difficult part of their job. Another good old cliché would be “You can lead a horse to water, but you can’t make it drink.” Really?
I completely disagree with that statement. First, decisions-makers are not horses, and secondly, we can help them use the data by putting them into bite-size packages. And let’s not even call those packages names that reflect employed processes. When we consume any other product, how often do we care about the process? It’s not just that we don’t want to know what is in the hot dog, but the same is true of even high-tech products, such as smartphones. We just want them to work, don’t we? Sure, some enthusiasts may want to understand everything about their beloved gadgets, but most people could care less about all of the hardships that the designers and manufacturers have gone through.
In fact, I tell fellow analysts to spare all of the details, assumptions and chagrins when they talk to their clients and colleagues about any analysis. Get to the point fast. Tell them major implications and next steps, in the form of multiple choices, if necessary. Have the detailed answers in your back pocket, but share them only when requested. Explain the benefits of model scores without uttering words like “regression” or “decision tree.”