Brilliant Orange vs die Mannschaft: A Biased Dutch-German View on AI and machine learning

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By Sebastian Olbrich and Tom Spaans First results from upcoming Insights and Data Consulting (IDC) survey Working with data and leveraging its value throughout the business is a key part of a company’s digital transformation. As Short-term projects prove, the value of data needs to be supported by a sound strategic vision to gain sufficient momentum […]

By Sebastian Olbrich and Tom Spaans

First results from upcoming Insights and Data Consulting (IDC) survey

Working with data and leveraging its value throughout the business is a key part of a company’s digital transformation. As Short-term projects prove, the value of data needs to be supported by a sound strategic vision to gain sufficient momentum for a culture shift towards data-driven decision making. Hence, organizations build up corresponding capabilities to work with data which can be categorized into the following three pillars: Data Management & Technology, Analytics capabilities & Assets and Operating model & Governance (see figure 1).

Capgemini Consulting conducted a study to gain insights into the importance of these capabilities of organizations in general and the current state-of-play of each capability in the investigated client organizations. By assessing how mature and balanced these capabilities are, our clients are provided with transparency over their status-quo and can benchmark themselves against other organizations. Such a clear picture allows companies to develop a roadmap and to position innovative projects in the fields of machine learning and artificial intelligence.

The study summarized over 100 interviews across all industries independent of size, ownership and legal structures or revenue. The study targeted central Europe, focusing mainly Germany and Netherlands. Being fully aware that the results might factor in a non-homogenous client distribution and that every business sector might not comprise of similarly distributed clients, our IDC Consultants Sebastian Olbrich and Tom Spaans could not help but focus on the commonalities and differences across central Europe.

A Dutch-German biased perspective on the results

Tom: “Let us start our analysis by focusing on the perceived maturity of each pillar per region (Figure 2). I can’t help but notice a significant difference between Germany and the other central European countries. Do you feel the focus of German companies does not lie in analytics, are they simply behind or might the results express a new humility in the German self-perception?”

Sebastian: “The results in Figure 2 depict a relative score which is the difference between as-is and target state. If you go to the absolutes, you will see that the status-quo is about the same. From this it is easy to conclude that our ambitions are just higher.”

Tom: “Ambitions? The Germans are behind in all comparisons, not just Benelux, but also France, Switzerland and Austria. Furthermore, they have seemingly not taken advantage of the full range of possible answers. For analysis purposes a strong fluctuation within the answers is a lot more interesting to analyse.”

Sebastian: “Well, your 1974 football team was also way stronger than ours and, Beckenbauer was nowhere near Cruyff in terms of technical skills. Yet, our 1974 ‘rumple-squad’ had the capability of orchestrating what they had to offer. In a similar way, we want to think of the data-driven processes as well thought out. As you can see the three pillars are equally well developed resulting in no weak links in our business data processes.”

Tom: “As much as I agree with your rumple-analysis, this year is bad timing to talk about football in Netherlands, so let’s get back to the study. To me it is not obvious that progress is always held back by the weakest pillar. It is likely that an advanced data management organization is a requirement for mature analytics capabilities, but this case might not be true vice-versa. What we can say, but is not captured in this figure, is that size matters. The data shows that the status-quo depends on the size of the company in the Benelux region. Usually companies, smaller in terms of headcount seem to perceive their capabilities as more mature. However, the three pillars for smaller companies are less balanced than bigger ones.”

Sebastian: “The numbers suggest that the picture is same in Germany. The data also shows the same order: analytical capabilities and assets are developed best, followed by Data Management and Technology and capabilities concerning Operating Model and Governance come in last.”

Tom: “If we look at the first pillar more closely (Figure 3), we observe that all regions invested in Data Privacy and Security. I assume this is due to recent data leaks and the upcoming General Data Privacy Regulation (GDPR) within the European Union. The Dutch seem to follow tradition by focusing on the value of assets. This happens to be the region where the gap between Benelux and Germany seems biggest. Is there nothing valuable in German data?”

Sebastian: “Even worse – given the low level of data integration it seems people do not know where to look. Currently, a lot of our clients invest in data governance and infrastructure projects. Having said that, the observation might be due to a difference in prevalent industries between the regions. Benelux is strong in banking and trade; two industries that are way ahead in the digital transformation which focuses mainly on client data. The big German automotive and manufacturing companies still employ a strategy which is resource- or asset based.”

Tom: “Still hard to believe that German engineers do not focus on value management…”

Sebastian: “Many still seek value in operations and hence focus on management of their products and internal operations. As digital leaders successfully demonstrate, we must put the user experience at the centre of all activities. Consequently, digital transformation demands nothing less of the German blue chips than a shift in management paradigm i.e. from an engineering or product centric view to a market-centric view. As you can imagine, such an endeavour would take a slightly longer time than digitizing existing processes.”

Tom: “If we focus on the second pillar, the analytics capabilities and assets, the first thing I notice is the analytics asset library. Depending on the region, the maturity is either amongst the best areas or the worst.”

Sebastian: “Seems funny at first, but I suspect the reason to be connected to the lack of maturity. For many of our clients the analytical capabilities usually depend on a few key resources. Even for consultancies like us, struggle to recruit talent and fluctuation is considerable. That could also explain why so many companies rely on software vendors (Figure 4).”

Tom: “Also, the gap seems to widen: The more you rely on software vendors, the bigger your asset library is, but the less likely you are able to operationalize and scale individual cases.”

Sebastian: “Happy you pointed that out. The numbers suggest that the German market seems to have understood that problem – at least the gap is smallest in Germany between vendor selection, asset library and operationalization.”

Tom: “That’s only one point of view, the agile one! From a process point of view, it might be deemed more efficient to have new ways of working by front-running on the vendor selection and operationalization. “

Sebastian: “Which leads us to the third pillar of operating models and governance.”

Tom: “Looking at Figure 5, I cannot help but notice two things: a) Benelux is quite balanced here as well. b) While the Dutch seem to focus on roles and responsibilities, the Germans focus on ownership. Do you think there is a cultural reason for this?”

Sebastian: “Obviously it is important to be aware of ownership and lineage to be able to understand the data. Data is moving from ‘golden sources’, which are single sources of truth, to multiple versions of truth, depending on the use case. Ownership becomes important when multiple truths exist. I have recently been hearing people talk about ‘multiple data lake’ approaches.”

Tom: “Thought water sports are more of our domain of expertise. But seriously, there is also a difference in KPI strategy when we look at the order in each region. Germany has put strong efforts in place, whereas the coastal countries seem to have KPIs at the bottom of their priority list.”

Sebastian: “How noble of you not to mention the Germans still rank last when looking at the absolute numbers.”

Tom: “I don’t want to sound repetitive…”

Sebastian: “Well, for a sound explanation of the relative numbers, you need to wait until I finish the interviews with German companies for the final report which will be published early next year. My hunch is that the capabilities are connected. It makes sense to focus on KPIs when data ownership is well defined.

Tom: “Industrialization of analytics is a topic not to be underestimated. As soon as the focus in Germany moves towards this topic, you will realize that KPIs and ownership are less clear cut when the use-case shifts over time. Models developed for a specific purpose will become widely used, which means rigorous testing of these models, and the responsibilities of the analytics departments will become more important.”

Sebastian: “Similarly, industrialization of solutions and management of analytical service portfolio also go hand in hand as cases mature and move towards robotics and machine learning.”

In this post we have only scratched the surface of the conducted survey – taking a biased Dutch-German view. If you would like to know more detailed insights, stay in touch for the upcoming joint DACH / NL Insights and Data Consulting (IDC) study.

About the authors

Dr. Sebastian Olbrich is Principal with Capgemini, Insights & Data Consulting (IDC) practice. He brings more than 15 years of experience to his clients, which are mainly global blue chips. He is also head of chair for Information Systems and Digital Business at the European Business School (EBS) in Wiesbaden, Germany and authored more than 50 academic articles.


Tom Spaans is a Senior Consultant with Capgemini Consulting, focusing on predictive modelling, advanced statistics and data-driven decisions. Tom has over 5 years of experience which he uses to support clients, focusing mainly on financial services.


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