Use of data analysis
For the study, we interviewed the Director-Major Shareholder (DGA) and/or CFO of sixteen Dutch transport and logistics companies with a turnover of up to 500 million euro. As these interviews were held before the coronacrisis, we were unable to include any possible consequences of this crisis in our study.
With the participating companies we discussed the origins and current use of data analysis, but also the ambitions for data analysis and what is needed to translate these ambitions into reality. We also looked at what works well and what could be better.
The interviews demonstrated that many companies are taking their first steps towards becoming a more data-driven company. In order to illustrate this, we used the so-called Gartner Analytics Model that describes four maturity levels that indicate the current maturity level of data analysis within the company. Most of the interviewed companies are currently on level 1 ('what happened') or level 2 ('why did it happen').
The interviews and an analysis of the data demonstrated that using data analysis is still in its infancy in the interviewed companies and that the potential is only realised on a very small scale. However, we observed that the companies have a clear ambition to move towards the next levels.
Vision and leadership
One key conclusion from our study is that vision and leadership from the company’s management are a prerequisite for developing data analysis within the organisation. Bottom-up development from ‘the shopfloor’ alone is not enough.
The use of data analysis often starts with small pilot projects and BI tools from the Planning and/or Finance departments. We noticed that these projects often lead to quick financial and other gains. This is partly due to more, better and up-to-date insights into relevant KPIs, which in turn enables improved and direct control. The top performers said that the investments in these projects pay for themselves and that they should have started much sooner.
We also noticed that a mix between experienced employees and graduates from intermediate and higher vocational education (for example in Econometrics or Business Intelligence) contributes to improving commitment to a successful application. An additional effect is that the company is seen as an attractive work environment for young talent.
We distilled a number of best practices from the companies that are already further ahead in applying data analysis. We combined those with our own knowledge and experience in this field and supplemented them with outcomes from the interviews.
An important insight is that the use data analysis by management is picked up in close cooperation with the employees involved. Data analysis is no longer about systems and IT, but often about a different way of working. Learning to trust data, combined with many years of experience and insight, means that your company can start to unlock its full potential.
It is also important to define unambiguous and detailed KPIs. They enable access to, modelling and visualisation of the right data and ensure the right areas are tweaked. Read the full report below.