MAKE DATA TALK # 1 Data-informed vs data-driven

One of the questions that I'm asked more than anything else is to explain the distinction that I make between being data-informed and data-driven, and why it's so important to get right. I believe that we should only ever use the language of being 'data-informed', and that we should stop using 'data-driven' completely.

Being data-driven is like when a jockey rides a racehorse that has blinkers on. Trainers put blinkers on racehorses so that, as the horse is sprinting towards the finish line, the shields that sit on the outside of their eyes stop them from being distracted by the things that are happening around them (such as the movements of other jockeys and other horses). However, the reality is that the position that the horse is in, and the position of those other horses and the jockeys matter; and these factors ultimately determine where the horse wearing the blinkers finishes at the end of the race.

When we are data-informed, however, we have our blinkers off; we can take in more data, we have greater understanding of the context, we can see the whole picture, and we are not solely focused on one single metric or outcome (i.e. the finish line).

When horses wear blinkers, the idea is that they face forward and just focus on the finish line to try to win the race. We don't want to do that when we use data; however, the language of being 'data-driven' almost implies that it's what we do. We never want to be solely driven by an external motivator or target, with complete disregard of the context and landscape in order to get there.

In addition, using the language of being 'data-driven' it's not useful for us individually, or more broadly in our organisations, where we're trying to build cultures where people don't fear data.

The reason that this distinction matters so much for me is that when I first started using data, I was in a workplace that was incredibly data-driven. What it meant for me as a middle leader, was that we each had a single metric that we had to focus on and we had to improve each year. Essentially, after a 12 month period, this one metric would come be released, and our success was determined by whether or not we hit our target. The entire organisation was incredibly driven by data. All of the decisions that were made were geared towards success in this final, annual metric.

As a leader, my monthly meetings throughout the year (and in some cases, even weekly meetings) were centred around the things that we were doing to try and hit our annual target. At the end of the 12 month period, if we did achieve the target, we got a pat on the back... We had done what we were meant to. However, if we didn't achieve it, we were performance managed for the next 12 months to ensure we hit the next target. I was lucky that this never happened to me, but only because I came into a team where their previous results were very low - meaning that I started from a very low benchmark, and one that was very easy to improve on.

Unfortunately, however, there were other people who were performance managed because they didn't hit their target. Not only was this detrimental to, and incredibly negative for those individuals, but it had a negative impact on the entire staff culture. For 12 months, other staff saw these people being performance managed; they supported them, wondered what it would feel like to be in that position... and the thought of 'I could be next' was never far away. For everyone involved, it was a high pressure situation, there were exceptionally high expectations on hitting those targets, and a very negative culture for all employees.

On the flip side, when we data-informed, we value quantitative metrics, we set goals and we work towards achieving them; however, we also take into consideration what we know about the context, the market, geography, demographics, strengths of the team, different product and service types... the list goes on... But when we are data-informed, we consider the context, as well as the numbers, and we use them together.

In addition, we also take into consideration the experience that people bring, including the things that they've previously tried, what they know has worked in the past, what hasn't been tried, where they've seen success (and failure) in other organisations. We tap into case studies and research and draw on information from a range of sources.

When we're informed by data, we do not have a single metric that we're aiming to achieve or to hit at the end of a fixed period with complete disregard for the context and landscape that it exists in; we combine quantitative metrics with context, professional experience, and research, and we use all of that together to inform the decisions that we make and the actions that we take.

(I just want to zoom in a little bit on the inclusion of professional experience. When we're data-informed, we don't want people to be solely relying on anecdotes and experience to guide the decisions that they make and the actions that they take. But we do want to honour their experience. We want them to be objective as possible, and tap into the quantitative data as well. When we are 'driven' by data, it is implied that professional experience and expertise is irrelevant - because all that matters is hitting the target, no matter what it takes. Whereas, when we are 'informed; by the data, we acknowledge them as humans and acknowledge the wealth of experience that they bring to the conversation and to the journey.

However, if we're driven by data, we essentially ignore that wisdom, and we're almost implicitly saying, 'your experience doesn't matter'. That's not how we create fulfilled employees who want to keep showing up for us and keep working towards our goals and working to better the organisation. We rely on our people and our teams, so at all times, we want to make sure that we're respecting and acknowledging their professional experience and how valuable it is.)

The final thing worth mentioning about being data-informed rather than data-driven is that as we know in our workplaces, language matters... We need to get our language right, and whether we do or not will have a major influence on how successful our use of data will be. There might be people in your organisation who you're asking to use data for the first time; and there will be people who fear numbers and fear data, and think they're not good at using them. So if you turn up tomorrow and say, 'new focus for this year, we're going to be data driven', how do you think those people would respond? (In my head, I can see the person who's fearful of data almost just shrinking into their shell!)

On the other hand, being data-informed is much friendlier language; it is also more open and inclusive language. When we can articulate that belief that we want to be informed by data, that we're using quantitative, we're using our understanding of context, and we're tapping into professional experience and research, it is a much easier 'sell' to our people... Particularly those who are fearful of it.

So if you've read this far :)... One quick action that you can take is that from now on, don't ever use 'data-driven' again. From here on in, you are always 'data-informed'.

This newsletter started as a podcast; if you'd like to listen to this episode and/or follow the podcast, check it out here.