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.
To explain further, ‘observe’ entails the continuous need for new data; ‘orient’ entails applying business acumen regarding value and outcomes; ‘decide’ entails analytical insight; and ‘act’ entails business execution.
But AI isn’t a silver bullet here. The best decision taken is next to useless if an organisation has little ability to change its ability to respond, or execute. So much of the hype today in IT is about using AI to automate some or all of a decision. Not enough is being spent on ability to execute. Don’t forget there are many different kinds of decisions.
So then we are back to marketing. When we hear the word ‘analytics’ we tend to discuss data and how it is presented to a user to guide decisions. This ends up putting the focus on visualisation software, dashboards and insights, which are described as different to classic reports. In reality they aren’t different – they may be based on forward looking data; but they’re no different to a report in that these things just show data.
Even if we report that ‘analytics’ was meant to include the decision process itself, it doesn’t matter. The market has settled on analytics as a piece of data and not a process at all.
So we are back to the bottom line. If you explore the OODA loop, you’ll realise that any successful organisation will have to perform across several areas:
· Data – the original issue, option, opportunity or thing that warrants looking at.
· Acumen – some kind of business or organisational awareness of what the thing is and to what degree any kind of response is needed.
· Analysis – where the options are explored, weighed and a decision taken.
· Execution – the decision itself is followed through with action.
· Data – more data. Did the outcome happen as expected? What’s learned from the whole cycle?
The market is currently fixated on the “analysis” or analytics aspect of this overall OODA loop cycle, with a big focus on AI and ML. But it needs to go beyond that to focus on how business processes, decisions and actions play out over time.
Either way, models in general – AI, machine learning, heuristics, simulation, even solvers – are very powerful in helping solve problems and exploiting opportunity. We just need to avoid the tendency to assume that one model will meet all purposes.
Andrew White is a distinguished VP analyst at Gartner. His research focuses on the chief data officer role, data and analytics platforms, strategy, governance and stewardship, along with master data management (MDM). Andrew will be presenting at the Gartner Data & Analytics Summit in Sydney, 18-19 February 2019.
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