As part of a digital transformation, many organisations realise the immense potential of the data they already have or will be collecting in the future. As such, they embark on a journey to understand the people, processes and technology required to become data-driven.

This usually results in a data strategy, and once the strategy has been developed, either in-house or by an external agent, it is typically presented to senior management and approval is sought for budgets to establish/procure the people, processes and technology needed to execute on the data strategy. 

The first step is usually to buy the latest and best technology to ingest, store, process and analyse the data. A lot of effort goes into ITTs, RFIs, RFPs and then eventually appointing a provider or providers. 

The technology provider will typically handhold in getting the technology deployed or recommending a partner to help you. As part of this process, you might even get a few use cases up and running, it’s usually standard practise to include this in the SoW, and so you feel you get value from day one.

Whilst you are buying the latest technologies, your designated head of data is busy recruiting a team. Based on the data strategy, they write the job descriptions, building a profile of a unicorn data scientist. Then the hard part of recruiting starts, with sacrifices made in finding the unicorn to finding a data beast who can at least execute parts of the data strategy. 

Once the technologies and team are in place, you start to tackle the low hanging fruit, delivering reports to teams who previously couldn’t access the relevant data, or building some analytical models to help Marketing target the right customers.

However, even with all this in place, debates based on hunches are still had at the senior level on what to do next. Data is often absent or if present is overruled by opinion. Analytical models start to be questioned as they don’t align with people’s views and expectations of the world, and as more and more reports are generated, utilising all the data team’s time, with very little actions resulting from them.

Then the inevitable happens, and your almost unicorn data scientists start to leave. Teams start ignoring centrally procured technologies for more role/task-focused ones, and some teams decide to also hire their own data analysts. All the while, you are no closer to being a data-driven organisation. 

So why isn’t your organisation leveraging the data they have, to be data-driven? In one word: “culture”.

In my two decades (and a little more) of working in the data space, I have found that for those companies who excel at using data versus those that do not, there is a belief across the whole organisation, from the top, that decisions, actions, and plans must be informed by data. 

It doesn’t mean that you just blindly follow what the data says, but when you need to decide as an organisation what to do next, you at least base that decision on some robust evidence.

However, many organisations use data as a crutch. Something to support a position they already have, to show that the decision they have already made can be retrospectively supported by data. In such scenarios, any data that doesn’t validate their viewpoint is dismissed or unfairly critiqued so that people begin to doubt it. 

Sometimes, I have seen data siloed to only serve the role of measuring the past. It is never used to drive the future. In one organisation where I consulted on a data monetisation strategy, I was explicitly told that I should focus on how the data can be used to generate more sales, and not to focus any effort on how data could improve the product or optimise the customer experience. Apparently, the teams responsible for that had many years of experience, and data was not something they ever needed.

So, to be data-driven, or as I prefer, data-informed, you must engage in a culture change or adoption process, whereby every part of the organisation can understand how data can be involved in the decision-making process. Even if their role doesn’t directly require any data, understanding how other parts of the organisation that they impact or are impacted by need to use data. 

Individuals need to be supported on how they can use the data, interpret the findings, and validate their thinking with data. They also need to be shown how far data can take you as, with all things, data doesn’t solve everything and has limitations. Bringing teams together by basing decisions on a robust evidence base can show how joint decision making can happen or conflict of ideas can be resolved. 

Being data-driven is more than getting the best technologies, hiring the right people, or even having the processes in place. It’s about winning hearts and minds. No matter how compelling the argument is to be data-driven, some will not see it that way or even have differing views about being data-driven. Pushing through the data agenda without taking people along with you is a sure-fire way to see data not being used.

If you don’t have a plan to change your organisation’s data culture, then get in touch with us at We’ve helped organisations with their data strategy and then helped them execute it by ensuring the whole organisation is brought into being data-driven.

Leave a Reply

Your email address will not be published. Required fields are marked *