Creating a Data Culture

A sports team can have the world’s best players and still fail to perform. In the same way, an organisation can have all of the right technology and be gathering all of the right data, and even have a great data team managing and analysing the data, but if the organisation doesn’t have a data culture, the true value of the data will not be realised.

To develop the sports analogy, we can use (association!) football. A great goalkeeper can have a huge impact- crucial saves make the difference between a 0-1 loss and a 0-0 draw. But, without a coherent attack the goals required for a win don’t come, and the result is 0 or 1 points instead of 3. Similarly, a great striker can take the slimmest opportunity and create a goal, but if there’s a complete lack of service, or a leaky defence is letting in more goals than you score, the end result is less successful.

Like a goalkeeper, a data team can ‘play out from the back’, which can achieve wins, but if they are constantly consumed with being reactive, making crucial defensive ‘saves’ (ie answering ad hoc queries; investigating source data issues), their organisation isn’t as operationally efficient as it could be. Similarly, your data team might be coming up with insightful analysis and ‘scoring goals’, but if the understanding of the data doesn’t flow backwards and forwards from and to data owners, you will not be getting maximum value for their efforts.

The ideal place for your data team then, is in the midfield - playmakers who also provide cover for the defence. There are different ways to look at the other parts of the team (we could also break a data team into their own full team formation!). 

The goalkeeper could be the systems that produce the data. In front are the developers who build the systems. Using effective monitoring and testing, the data team can support the developers to make sure the defence against bad data is watertight, but it’s crucial that the developers see beyond the defence - they understand what happens next, and how they can help shape the game.

In front of the midfield are the operational teams who use insights to take action. In our analogy, action that drives value is a goal. Sometimes the data team lays on the equivalent of a great cross, and literally, the forwards just need to deflect it into the net. But there will be more wins if these players can also create opportunities themselves - effectively the equivalent of self-service BI. These users might also be data owners (eg a marketing team), and they will sometimes need to track back to keep the defence tight!

Where are the business leaders in this team? Well, we’d hope they’re the coaches. With an ability to appreciate the bigger picture - from a basic understanding of the technical underlyings to a desire and curiosity to understand how the data can drive value. They lead by example in driving the effective use of data, and understand that just having a solid goalkeeper or star striker data team is not enough.

So how do we bring about a data culture? Well, as a starting point, just stating ‘we have a data culture’ or ‘we need to have a data culture’ doesn’t bring one about. Let’s assume that an organisation has a desire to build a culture. There may still be pockets of resistance. If teams perceive that data is being used to ‘spy’ on them, or replace their roles, or if they simply don’t trust the data, they may be reluctant to engage with the it. Forcing people to ‘use the data’, does not give a productive outcome. In this case it’s useful to take people on a journey - explain how the data is gathered, the steps that are taken to ensure its accuracy, and build up the story of how the data can make their role easier, and how they can use it to add value.

If teams start to understand the power of the data, their curiosity will hopefully lead them to ask and answer more questions for themselves. However, teams still need to be trained to understand and interpret data. The form this training takes depends on lots of factors - role; current proficiency; needs etc, but the goal should be to foster data literacy. 

Counterintuitively, having a data team who are seen to do ‘magic’ can have a negative impact on data culture. If this work is seen to be some kind of sorcery, it will seem out of reach to everyone else. There will always be ‘heavy lifting’ that needs specialist skills, but upskilling to close the gap will allow the specialists more time to do this. 

Back in the football world, even players who are seen as less than academic to the fans are described as having a ‘football brain’. This is the equivalent of data literacy. The greater your teams’ understanding of the data, from creation to interpretation, the better they will be able to play an active part in the flow and function of data. To build confidence everyone should be given the opportunity to experiment, but with the constructive support of the data team.

One final crucial point- you need your business to understand data, but you also need your data team to fully understand the business. They need to see the processes that produce data in action - following real customer journeys, shadowing colleagues to understand how their work generates data and understanding how they use data. Data (and other technical) teams need to empathise and build consensus to achieve results for your organisation.