Decision-makers all need information to choose wisely. Who can forget the time that the bomb squad blew up a suspicious vehicle outside Workington Police station? The vehicle had actually been parked there by fellow officers after they helped its owner (who was unwell) but they failed to mention this to their colleagues.
Today, data is everywhere. With the rise of various information-gathering tools – cookies, mobile analytics, sensors and so forth – it accumulates by the second. Yet most organisations still have work to do when it comes to ensuring relevant, accurate information is accessed when needed and presented and processed in ways that support better decisions.
We need, in my view, much more focus on identifying the decisions that are critical to the organisation and then supporting them with the right information and analysis. At the strategic level, this usually means supporting decision on what you are trying to achieve, who you serve, the capabilities you prioritise, and your culture. But it is equally vital to dig into the decisions made at the sharp end of your organisation – the daily choices made a thousand times that shape the service the public or your customers receive.
We suggest six steps for organisations seeking to ensure data-driven decision-making:
- Shape your data capture to your decision needs. While some data is so cheap and easy to collect, you may as well have it in case it helps with an unforeseen decision later, being intentional about the reason you are collecting information will help shape your information architecture, and allow you to focus your data quality and analytic efforts. Wherever possible, democratise data, so more decision-makers can access it.
- Build data literacy across the organisation. Not everyone likes or is comfortable with data, but it simply isn’t feasible for many leaders today to be effective without a good understanding. If leaders don’t ‘get’ the value of information and analysis – or know what good and bad analysis look like – your decision-making will be worse, and your efforts to improve data analysis are doomed to fail.
- Present data in ways that overcomes some of our human weaknesses in understanding. Framing of information is powerful. Choice ordering, ways of presenting risk and probability, and even format can ‘nudge’ decision-makers in one direction or another. A picture tells a thousand words, so visualisation can be harnessed to enable better decision making but it can also be misused!
- Build stronger feedback on the results of past decisions. At Leapwise, we try to build feedback loops into as much of what we do as we can. We get feedback from clients, we are testing our meetings software on our own meetings, and are trying to build a culture of learning across the organisation.
- Automate or partially automate decision-making when it is heavily rules-based. Feedback systems can eventually create self-improving automated decision-making. Our machine learning forecasting partner, Skarp, gets ever-better forecasts precisely by refining how it predicts demands as it collects more information and learns.
- Ensure decisions at every level are informed by overarching organisations goals and priorities. Most Agile organisations emphasise the importance of everyone in the firm understanding overall goals, so that they can respond quickly to rapidly changing events. But even more hierarchical organisations understand this. For example, the British Army ensures that when an order is given, it is usually accompanied by the preceding order that it is a response to. Wherever you can, ensure decision-makers have to explain how their decision fits in the broader mission, and there will be less risk of organisational effort becoming fragmented.
Data collection and storage has become cheaper than ever before – and this trend will likely continue in the coming years. But we need to be supremely vigilant about the fact that data is worthless until it is interpreted and actually influences the decisions we make.
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