Do you really want to be data-driven?

I was once asked by a CIO to help him present to his board on why they wanted to become “data-driven”. After five minutes into my presentation, a board member and I concluded that based on what we had heard outside that meeting, they didn’t want to become data-driven – they wanted to become customer-driven.

To be data-driven is a dangerous catch phrase. Data, or even technology, for its own sake without a clear line of sight to an outcome – social or business – is a terrible waste. So, stop talking to business leaders about technology and, instead, talk about how data and analytics can drive better business decisions and outcomes.

Data and analytics investments that are tied to measurable business outcomes are more likely to produce reportable benefits. The problem is that organisations struggle to determine which investment drives which business outcome. Understanding how data and analytics capabilities create business value continues to be a challenge for all.

Gartner’s data and analytics team has been hard at work on this challenge for a while, recently exploring concepts such as storytelling, information as a second language and data literacy. These are all similar ideas, focused on training ourselves to stop talking about data and analytics and being data-driven. Instead, talk more about how a business operates, behaves and changes using better data. The emphasis is not on data or analytics.

I recently read an article that explored the notion that a data-driven business collects and analyses data to help humans make better business decisions, whereas a model-driven business creates a system built around continuously improving models that define the business. In a data-driven business, the data helps the business, while in a model-driven business, the models are the business.

Is this a play on words and clever marketing? Well, a little ‘yes’ and a little ‘no’.

Conceptually the argument is solid – we can use software to model decisions, responses and, in some cases, use artificial intelligence (AI) and other techniques to help streamline parts of the ‘observe, orient, decide, act’ (the OODA loop) process for automatically learning from experience and updating the next decision.