digital spirit: How do the trend surveys differ from Trend Radar?
Christian Kolarsch: The trend surveys and Trend Radar are closely linked. We use Trend Radar to give you insight into major digitisation trends on a meta level. In the last edition, we looked at 15 major digital trends. The surveys dig deeper into selected trends.
digital spirit: The matrix you use to weight the trends’ significance for the Group: how does it work?
Markus Albrecht: We depict a global view of the market and the maturity of a technology on the x axis. The y axis shows our assessment of the impact on Deutsche Bahn’s market. It would not be easy to obtain this kind of assessment from any external analyst.
digital spirit: How do you collaborate with other departments for this purpose?
Markus Albrecht: When it comes to surveys such as “Data-driven business models“, it’s not only the technical aspects that are important and valuable. Of relevance are also social issues, the situation on the marketplace, and the preconditions for data-driven business models. To include the broadest possible breadth of expertise, we work closely with colleagues within the Group from diverse areas, such as market research, digitisation, and data analysts. The survey is also an expression of the close working relationship between CIO DB Group and DB Systel. All of this gave us a broader view of data-driven business models.
digital spirit: How do you chose topics for a survey?
Christian Kolarsch: From our perspective, an important selection criterion for a trend that is worth closer examination is that it significantly impacts many areas of the Group. The trend survey on data-driven business models is nearly 40 pages long. Clearly, we don’t have the resources to go into every topic at this depth.
digital spirit: Why do you see data-driven business models as vitally important for the Group?
Christian Kolarsch: When you think of data-driven business models, you mostly think of Facebook, Amazon and co. and it’s not easy at first to see their relevance for Deutsche Bahn. But if you look at various players in the travel and transportation market, or in logistics, such as Uber or Flixbus, then you soon realise that the relevance of data, and business models driven by data, to Deutsche Bahn cannot be underestimated. More and more businesses are entering our market who do not have large vehicle fleets or major infrastructure resources. We cannot afford to underestimate the importance of this topic, because it offers new market players huge opportunities, leading to greater competition.
digital spirit: Why did you also look at companies like MyHammer and Zalando that don’t operate in the travel and transportation sector?
Markus Albrecht: Firstly, the key components of a data-driven business model are data generated by customers, and the data that our own systems produce. Secondly, platforms are what links the data ecosystem and the customer ecosystem, which is the third component. And although some consumer platforms don’t seem at first glance to be relevant to Deutsche Bahn, they do make the immense potential of data-driven business models tangible. We want the survey to help us explore how the models employed by these platforms could be adapted to the needs of Deutsche Bahn.
digital spirit: What insights can be obtained from data for business models?
Markus Albrecht: An example is travel behaviour: When customers want to travel from A to B, the price is one factor: for instance, if a Flixbus is the cheaper option, that could be decisive. At the same time, it is becoming more and more important to customers how they will get from door to door – in other words, the end-to-end concept. Data analysis indicates a great desire for a personal multi-modal itinerary, and at what locations you can achieve the best results with special additions to Deutsche Bahn’s traditional offering.
Christian Kolarsch: We look at how the major platform providers operate, whose business is not necessarily travel-related. For example, we examine the elements of Amazon’s success – and we try and place them in our own context. Correspondingly, Deutsche Bahn is developing new approaches to mobility, which, like “ioki” don’t operate to a timetable, but on demand.
Data-driven business models
Data-driven business models are based on platforms that facilitate the exchange of goods and information between market participants, consolidate business relationships and generate brand-new business. In the digital age, data-driven business models aim to use rapidly growing data volumes to enhance existing ranges of products and services and exploit new markets.
digital spirit: Requirements are changing all the time. So is a survey an appropriate tool for actionable insight?
Christian Kolarsch: Our primary goal is to show where the opportunities and the risks lie. We also want to address social and economic questions through the survey – and stimulate fresh ideas. When it comes to data-driven business models, we can’t operate in a vacuum; we need to take account of political and economic constraints.
digital spirit: What insights were you able to gain with regard to Deutsche Bahn?
Markus Albrecht: Deutsche Bahn is viewed as a responsible partner in terms of handling data. Customers tend to be more willing to provide information and data about themselves when they know that we will treat it confidentially. We possess an immense treasure trove of data – and so, compared with many national and international companies and start-ups, we are in a good position to launch new business models that capitalise on it.
digital spirit: Does that mean the survey mainly pinpoints the opportunities of digitisation?
Christian Kolarsch: Not only the opportunities, because there are also risks, particularly when we look into the future. In the survey, we come to the conclusion that we at Deutsche Bahn need to become data-driven and to establish corresponding business models. Otherwise there will be a growing risk of disruption, as we need to compete with entirely new players. Companies like Flixbus, for example, have mastered the travel value chain without owning a single vehicle. There is a growing risk that major digital businesses like Alphabet/Google will leverage data-driven business models to occupy the gap between our customers and ourselves — which means we lose business.
If we want to make a success of data-driven business models, we need to separate the data wheat from the chaff and achieve high overall information quality.
digital spirit: What is more important: data as a resource or data as something that needs protecting?
Christian Kolarsch: Both – they are mutually dependent. In the survey on data-driven business models, we home in on the treasure trove of data at the Group’s disposal. At Deutsche Bahn, we have a wealth of data from diverse operational units. Much of it came about for a completely different purpose than to generate data-driven business. If we want to make a success of data-driven business models, we need to separate the data wheat from the chaff and achieve high overall information quality. That is one aspect of our survey entitled “Information Veracity“.
digital spirit: Can you explain that in greater detail?
Christian Kolarsch: We are increasingly facing data and information overload. The major challenge is to focus in on the right data and information. Our everyday activities are based on the information we process. So the question is: how can I find reliable data and high-quality data? When I think about everything that’s available to me via social media channels or the internet, plus all the additional vast amounts of data that will be generated by the Internet of Things and will require processing in future, the issue of Information Veracity is going to be increasingly important.
In general terms, veracity means the validity and/or the trustworthiness of data. Normally, the plausibility of data and findings depend on the quality of the input data and the analytical methods applied.
digital spirit: What does that mean in the context of Deutsche Bahn?
Christian Kolarsch: Consider predictive maintenance approaches: vehicle components are no longer replaced at intervals, but when they are worn. Specific indicators determined by means of IoT technologies, reveal, for example, that a part needs to be replaced. The corresponding algorithms must function correctly and the data collected must be correct. Otherwise, bad data could cause damage to components – cause them to be replaced much too soon. In either case, the desired effects of predictive maintenance are not achieved.
digital spirit: You sketched three fictitious scenarios for the survey on information veracity. Why?
Christian Kolarsch: When we started on the survey, many people had never heard the term “information veracity” before. That’s why we wanted to make the issue more tangible, and developed examples of threat scenarios from different areas. From a technical perspective, we chose the subject of software manipulation, and transposed it into a Deutsche Bahn context. From a social perspective, we explored the significance of fake news or deliberate fabrications. The business scenario we chose related to calculating profitability and business cases.
High data quality and reliability helps us to improve our internal processes and procedures.
digital spirit: What insights were you able to obtain?
Christian Kolarsch: Above all, that in the long-term it is a matter of survival for companies to take a responsible approach to handling the data that they collect or that is entrusted to them . It is a key issue for Deutsche Bahn, too — particularly for Deutsche Bahn. Our survey on data-driven business models shows that the way we handle data is one of our major strengths. And the survey on information veracity reinforces the importance of not putting that into jeopardy.
digital spirit: So the two surveys are closely related?
Markus Albrecht: The two topics have a great deal in common, but each is also useful in its own right. While data-driven business models focus more on the external customer, the issues of data quality and reliability are highly beneficial from an internal perspective, too. Without accurate, high-quality data, we can’t hope to develop strong data-driven business models. But that doesn’t mean that the reverse is true: that we don’t need high-quality data unless we have data-driven business models. On the contrary: high data quality and reliability also helps us to improve our internal processes and procedures.