Practical tips for data-driven working

In his daily work as Data Strategy Leader, Louis de Roo has the privilege of looking along with many different companies and organizations. Every day he learns more about what does and doesn't work when putting data-driven working into practice. That is also why the list below is by no means complete or could ever be. But 'start small' is exactly one of the tips he can offer. The list below will therefore be expanded regularly in the future. If you miss a topic, don't hesitate to leave a comment. Louis will try to address it in a later addition!

Start small

When designing and thinking through your first data product, the temptation is great to put all your wishes and creativity into your first attempt. But that is exactly where many companies get stuck: with sky-high ambitions and expectations for that first dashboard, it is almost impossible to set it up successfully while your organization is still busy learning which processes, systems and tools you need to bring together to arrive at a dashboard. So start with a small scope, to give yourself time to get to know the process. That way you avoid having to win a championship straight away without any training!

Learn together

It's a cliché, but in data-driven working the journey is certainly as important as the destination. Not only for the support for your solution, but also for learning to work with data within the organization, it is important to involve all stakeholders in reaching the goal from the start. That way you prevent working with data from becoming an exclusive party for the data department, and processes and responsibilities can grow along with the organization's data ambitions.

For whom

Looking at the same data is not the same as looking at the same dashboard. Where the board needs an overview, that can feel too distant or impractical to employees in an operational process. So ask yourself who is served by a dashboard 'for everyone'. There is nothing wrong with a dashboard of their own for everyone, as long as the source data is the same.

Precision

We often show data in detail 'because we can', but in most fields we don't base our decisions on differences after the decimal point. So consider which level of detail is relevant for the decision you are facilitating, and leave out the rest. That way you avoid discussions about rounding differences and searches for the right figures.

What is true

As soon as two systems display the same measurement, a difference will arise. Even the tightest system integration makes a rounding difference somewhere. And while most organizations can easily agree on which measurement to use for a certain KPI, the big point of contention is often what the definition of that measurement should be. By clearly recording from the start which data from which source with which processing you use, you not only prevent discussion, but also lay an important foundation for good data governance!

Quality

The most commonly heard reason not to work with data is quality: 'there are errors in this dataset' is often a reason for disqualifying the data, and with it the data solution. The question is to what extent this is justified: every dataset contains errors, sometimes from operational processes, sometimes from technical integration, sometimes from manual entry. And for some uses of data those errors are unacceptable, for example for a sworn auditor's statement or for chemical calculations. For most uses, however, a single error in the data doesn't matter and doesn't change the picture at all. Don't confuse 'as good as needed' with perfection!

What would you like to know more about?

This blog is regularly supplemented with practical tips for data-driven working. Do you have an urgent question or a topic that is truly missing from this list? Get in touch with us.