The Challenges of Data-Driven Working

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

Plenty of input for your undersigned data strategist to dive into and, of course, to voice a few strong opinions about. Because the National Data Benchmark covers a wide range of topics, that is 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 study: data-driven working and its challenges.

National Data Benchmark: Data-driven working

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

That is also 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 is lacking anyway. And although things need not be hopelessly lost by any means, it is wiser to fix your roof while the sun is shining. Likewise, it is a better idea to start your data plans when your company is doing well.

The challenges

After all, it takes vision and leadership to make your organization data-driven. And that is exactly what many companies lack: only 27% of the surveyed organizations in the National Data Benchmark say 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 the second most important challenge, narrowly surpassed only by safeguarding data quality (50.8%).

I have written about data quality before, and I will not repeat my entire plea here for sufficient rather than complete data quality, but a clear data strategy can help identify the data quality you need. By stating clearly in your data strategy what you are going to do with which data, it is also easier to identify where top-level data quality is necessary for your plans, but also where it simply has less impact.

And while you are at it drawing up a data strategy: also think about how you would like your organization to deal with that data. In third place in the list of the most important challenges for data-driven working is data literacy (44.1%). So it is important to consider who needs to work with your data and what they need to be able to do so. Being able to work with the required software or finding the right buttons to click is not the only thing that matters. Thinking in your data strategy about the purpose of your data products helps ensure that you do not 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 feels that their audience should know more about data, it may also pay off to think carefully about how you as an organization actually communicate with data. Of course, today we can expect many employees to have a certain skill in working with and interpreting data, but a manager who wants to make sure their employees make the right decisions based on data would do well to raise 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 how data will be applied 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 will need to work with the data and what interaction with the data do we expect from them?

In fact, answering the questions above is the main prerequisite for a successful data strategy. Unfortunately, a data strategy has increasingly become a vehicle for management consultants to profile 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 data-driven working.

How is your organization's data strategy doing?

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