How valuable is a date?Sustainably mining and combining information.
Next MQ! Innovation Summit|November 8-9, 2018
Audi sets up new digital business models. Matthias Frank and Bernd Nägel, who both work in Finance, investigated the fundamentals for this in their workspace: How can you place a number on the value of data?
Matthias and Bernd,
you both work in Finance at Audi. What exactly are your subject areas there?
Within corporate financial planning, I head the Business Analytics, Volumes/Foreign Currency Valuation department. A great deal of financial data from the company merges together here. Our operational tasks are primarily forex planning and the valuation of the volume and sales plans. In parallel to that, we are dealing with the value of data and with the new IT solutions. We have begun working with Big Data tools and merging data that are located in various containers and are handled with various systems.
My department determines new business models from a financial perspective and at the same time looks for new approaches in digitalization. These may be services in an online shop or – my personal focus – new business models that use the data we collect with and in our cars. Of course, before we put them on the data highway we have to clarify how we will make them benefit us financially. We are well on our way to establishing these business segments.
What insights did you especially hope to glean from the workspace?
I am interested in a central question: How can we add value to the company’s master data and understand it as value? These are the kind of data that don’t really change over the long term, such as a supplier number or the structure of a market. There are certain fuzzy aspects in this area today and a need for harmonization. Does Mexico and its market ID belong to North or South America, for instance? Do the New Car Sales and the After Sales departments use the same customer IDs?
Only when we consistently and clearly define this master data, ideally across-the-board in the company, can we establish our new digital business model. At the MQ! I was also interested in finding out how other companies handle this topic.
My biggest concern was the opening to the outside. As automotive experts we can certainly produce some good ideas for structuring data business cases. But we have to take off our Audi glasses and look at what others are doing. What are Google, Facebook and Apple doing with their data? The whole world is using data to generate new services. We want to be part of this trend and use it for our benefit.
How tightly are you all networked now in your work?
We have in fact discussed data models and data value with several large companies, such as Microsoft, the Maersk container shipping company and Deutsche Bahn. In doing so we discovered that all of them are confronting the same challenges.
We experience this very similarly in our department, although we also communicate with start-ups on a case-by-case basis. Every large company is facing a transformation process that includes many new opportunities and challenges.
How did you approach this topic in the workspace?
We divided up the various data into six categories. For example, we put data about road conditions in one category. In another, we put driver comfort. And in yet another, the structure of a market. There were four target destinations for all data groups: corporate customers, end customers, administrative departments and internal customers at Audi. We divided our participants into small groups and asked them to select data from the categories, assign them to customer groups, and estimate the value of the data in euros.
And what was the result?
The results for the price tags varied widely. We drew some valuable and conclusions from them that were in part new to us. For example, we found confirmation of our assumption that the value of data depends not only on the customer segment, but rather also on the geographic location of the market. A participant from Canada found it extremely important to get information about road conditions on long drives he had to make – and he would be glad to pay for that. European participants, on the other hand, did not see a lot of value in data in this regard. And the participants from companies that work with master data rated this as extremely relevant and correspondingly valuable.
Did you learn more from the participants or did they learn more from you?
I experienced the workspace as a win-win situation. We obtained a lot of valuable feedback and suggestions – for example, that we could assign certain digitalization tasks to strategic partners, which they would be happy to take over because their culture is different. The group of participants was a good mix. There were start-up founders as well as established entrepreneurs. There were sales professionals as well as consultants, and even a professor. All trying to discover the same thing – the value of data. And as a result, the discussions were never finished after 90 minutes. On the contrary: they continued on. And we are happy about that.
We opened up many new doors for our work and opened up new perspectives. And after the MQ! we obtained many contact requests and offers of cooperation. The whole thing was just a lot of fun, starting with the pitch. Generating interest for a workspace in front of 400 visitors at a high-caliber event like that – it really was a kind of emotional rollercoaster.
And it paid off! We were able to capture a bit of the zeitgeist and bring it into the company. And we demonstrated that, where digitalization is concerned, Audi is very open to people from outside and can listen very well.
Head of Business Analytics, AUDI AG
Matthias Frank works as a Business Analysis Manager at Audi. With his background in entrepreneurial studies he is especially skilled in developing businesses. He told us about business models evolving from the increasing value of data.
Controlling Digitization, AUDI AG
Bernd Nägel is working in the financial side of the car industry. He is controlling business activities around the mapping company HERE at Audi and shared his insights on the analysis and rating of common customers, markets and live-data.