Data-driven decision-making and decision-driven data analytics may sound remarkably similar. What the data shows we must surely follow, right? Not necessarily.
For all the talk about how harnessing data analytics can help revolutionise the ways businesses run, and the successes they achieve, that’s yet to truly materialise in any meaningful fashion. According to Accenture, for instance, out of a survey of almost 200 US executives, less than 30% said data analytics produce highly actionable insights.
So, how do we better make use of all that data at our disposal? After all, there’s no question that there’s value to be gleaned there, it’s just a question of how companies approach their analytics, and that’s where decision-driven data analytics comes in.
Why might data-driven decision-making not be the solution?
There are two main reasons why data-driven decision-making isn’t always as effective as we expect it to be. The first is that it can find us asking the wrong questions, and the second is that it relies on the data itself being of a high-quality (which frequently it isn’t).
Reframing the questions
One of the main issues with data-driven decision-making is that it frames questions in the wrong way. In other words, data analytics is so praised that it obscures leaders from asking the questions they ought to be asking, purely because they believe they have to use the data (often historic) available to them, leaving little room for innovation.
Rather than decision-makers and executives asking the questions they want to ask, and then mining data to answer those questions, executives are jumping to the data before anything else. This, in turn, shapes the questions they ask, and therefore the decisions they make. What’s the issue with that?
Well, it significantly narrows the ability for truly-disruptive decision-making, and the insights it does grant don’t often end up being all that helpful. Data should be used to find solutions to questions posed; it shouldn’t necessarily automatically be the driving force behind the questions themselves. That’s not to say the latter can’t be the case, but it shouldn’t be the default.
Data quality
Another issue with being overly reliant on data for your decision-making is that not all data is created equal - there are good-quality data and bad-quality data. And poor-quality data? Well, it can cost businesses in a major way. According to Gartner, for instance, businesses incur average losses of $15 million per year as a result of poor-quality data.
If you’re making decisions based on data, and that data is bad, then it doesn’t take a huge mental leap to realise that those decisions might be bad, too. Perhaps you’re working from duplicate (or incomplete) data sets, making assumptions pinned on inaccuracies? Or it could be that the data has been poorly manipulated or collected, and again, therefore, is not as trustworthy.
You can put all your hopes in the world on a prize stallion to win at the races, but if it turns out that that horse is actually lame, then you’re not going to get the results you want. It’s the same with data. Data-driven decision-making can work well using high-quality data. With poor data, however, it’s another story altogether.
Data-driven decision-making isn’t bad, it just needs to be used properly
It’s important to note at this point that data-driven decision-making isn’t an inherently bad strategy. It makes perfect sense that we use the information available to us to make smarter, better-informed decisions in the workplace.
After all, if we didn’t use the data at our disposal, our decisions would be based on nothing but gut feelings, and that could spell disaster. Provided that good-quality data is used in the ways outlined above (with decision-making at the heart of things) using data offers many benefits to an organisation, including:
- You get clearer feedback for market research. Using data enables companies to better understand their customer base, the decisions they’re making (and not making) and why.
- Using big data can help a company continually innovate with a steady stream of incoming data, rather than them having to rely on more generic, or infrequent streams of data.
- It can help a company realise cost savings. Operational inefficiency is a major problem for many companies, and looking to see where money is being wasted unnecessarily is hugely important. By analysing the data available, companies are able to identify those inefficiencies and tighten up their financial model.
- It introduces greater objectivity. Decisions are easier to make when emotions and feelings are taken out of the equation. By pulling from the data sets companies have available to them, they can make harder decisions with more certainty, knowing that the data is there to reinforce the said decision.
Final thoughts
A lot has been made about data-driven decision-making in recent times, but companies would be better off refocusing their attention first and foremost on the decisions they want/need to make and then using data to inform those questions and decisions, rather than the other way around.
Data has always been a weapon in the arsenal of decision-makers and executives, but it should never supplant those decision-makers.
Decision-making is a complex and multi-faceted discipline, one with many different approaches you can take. Here at the UTS Open, we offer a range of decision-making-based courses for you to consider, including our Decision Making Under Uncertainty, Rationality and Incentives in Decision Making and Behavioural Decision Making” microcredentials.
