The Challenges of Data-Driven Working

In September, the National Data Benchmark was published, a research report in collaboration with Data Expo in which more than 250 data professionals shared their experiences and insights about the state of affairs in the field of data.

Plenty of input for your humble data strategist to dive into and — of course — share a few strong opinions on. Because the National Data Benchmark covers a wide range of topics, that's rather a lot to discuss in a single blog. This will therefore undoubtedly not be the last time this research is cited, but for now let's dive into the first conclusion of the research: data-driven working and its challenges.

National Data Benchmark: data-driven working

Three-quarters of the organizations surveyed indicate that they expect data-driven working to generate added value, and that they have concrete plans to do more with it. That's the good news: we no longer need to explain that data-driven working is a good idea, and the fact that there's added value in smartly applying data has fortunately sunk in at most organizations. But whether that knowledge also leads to improvements in practice is the question; we humans are very good at knowing something is good for us, yet doing the opposite anyway. Now, starting to work data-driven is somewhat different from quitting smoking or going to bed on time. Without the direct pain of a poorly running process or disappointing results, very few companies will start tinkering with their existing way of doing things.

And that's exactly where the biggest challenge for the success of data-driven working lies: we only start where and when it hurts. The intended improvements of data-driven working often lie in improving processes that are already running poorly, or where control over the situation was already lacking. And while it certainly doesn't have to mean that things are then beyond saving, it's wiser to repair your roof while the sun is shining. In the same way, it's also a better idea to start your data plans when your company is doing well.

The challenges

Making your organization data-driven requires vision and leadership. And that's exactly what many companies lack: only 27% of the organizations surveyed in the National Data Benchmark indicate that they have a well-implemented data strategy. And 49.6% of participants report that setting up a good data strategy is the biggest challenge in becoming more data-driven. That makes the lack of a data strategy immediately the second most important challenge, narrowly surpassed only by ensuring data quality (50.8%).

I've written about data quality before, and I won't repeat my full argument here for sufficient-rather-than-complete data quality, but a clear data strategy can specifically help identify the data quality you need. By clearly stating in your data strategy what you're going to do with which data, it also becomes easier to identify where the highest level of data quality is necessary for your plans, but also where it simply has less impact.

And while you're at it drawing up a data strategy: also think about how you'd want your organization to handle that data. In third place on the list of biggest challenges for data-driven working is data literacy (44.1%). So it's important to think about who needs to work with your data and what they need in order to be able to do that. It's not just about being able to work with the necessary software or being able to find the right buttons to click. Precisely thinking, within your data strategy, about the purpose of your data products helps ensure that you don't just burden your organization with extra complexity, but actually help it make better decisions.

In all honesty, we should also mention that while the developer of a data product often finds that their audience should know more about data, it might also be worthwhile to think carefully about how your organization actually communicates with data. Of course, today we may expect many employees to have a certain skill in working with and interpreting data, but any manager who wants to make sure their employees make the right decisions based on data would do well to take their data visualizations to a much more professional level.

Conclusion

In summary, we can conclude that the three main challenges in becoming more data-driven can be addressed by thinking carefully in advance about the application of data within your organization:

• What improvement do you want to make with data?
• What data is needed for that?
• How important is the quality of that data?
• Who needs to work with the data, and what interaction with the data do we expect from them?

Answering the questions above is actually the main prerequisite for a successful data strategy. Unfortunately, a data strategy has increasingly become a vehicle for management consultants to make a name for themselves in the boardroom, causing the practical application of many data strategies to slowly fade from view. A good data strategy is simple, short, and the fastest route to removing the main challenges in the field of data-driven working.

How is the data strategy of your organization doing?

Written by Louis de Roo
Data Strategy Leader