The Swiss magazine "Computerworld" reports on the importance of a corporate data strategy
In her presentation at the TDWI Conference 2019 in Zurich, Prof. Dr. Christine Legner pointed out how companies can develop a suitable and sustainable data strategy. This process and the importance of such a strategy in a digitally transformed future are the subject of a current article in the Swiss technical journal "Computerworld".
Here you can read the English translation of the original German article (source: Jens Stark/NMGZ).
Diverse aspects of data processing and management were among the topics discussed during this year's TDWI Switzerland conference in Zurich. In her keynote on data strategy, Christine Legner from the University of Lausanne showed how companies can develop one.
Today, companies accumulate masses of data or even actively collect it. In each case, most companies do not have a concrete plan for how to use all of this information for the improvement of their own business or even how they could use it to expand into new areas. A data strategy is, therefore, more sought after than ever before. Indeed, with new analysis methods based on machine learning and artificial intelligence on the horizon, a data strategy can even be decisive for the survival of one's own company in the future.
How companies can save themselves in the digitally transformed future with such a data strategy was exhibited by Christine Legner, professor in the HEC faculty (Faculté des hautes études commerciales) of the University of Lausanne and academic head of the Competence Center Corporate Data Quality (CC CDQ), during the TDWI Switzerland conference (Transforming Data with Intelligence). In her keynote address in Zurich on Monday, she emphasized the urgency of data strategies for companies, while also exhibiting routes companies could take to obtain one. To emphasize the urgency, Legner quoted the authors Leandro DalleMule and Thomas Davenport who postulated in a report on this topic in the Harvard Business Review that "Companies that have not yet built a data strategy and a strong data-management function need to catch up very fast or start planning for their exit.“
Data Strategy in a State of Change
As Legner reported from research, many companies have been working on developing a data strategy for some time now. However, a change is emerging in this regard. "There are completely new requirements due to new trends like digitalization and digital transformation", she said. Even if companies began with their data strategies ten years ago, their current data strategy is "significantly different" than before. Legner draws on an examination by the CC CDQ in which 16 European companies were closely surveyed during a workshop. According to this study, more than 50 percent of the companies are currently reworking their data strategy.
What Belongs in a Data Strategy?
But what should actually be included in a data strategy? One problem on the route to answering this question, according to Legner, is that there are different people and stakeholders within a company, who all have a somewhat different vision of what belongs in a data strategy. In particular, the professor mentioned the business users on the business intelligence team who demand democratization of the data, that is, who want to expand the circle of users who have access to the company's data. On the other hand, there are also classic data managers who insist on data quality and are very careful with new data projects because they do not want to neglect the company's core mission.
Finally, there are data scientists who are on the hunt for fresh use cases; even with newborn AI technologies, for example. Opposing these are classic IT administrators who "prefer not to even have such tools demanded by the data scientists on their laptops", as Legner visually describes the conflict. "They all have their own, albeit justified viewpoint, on what a data strategy should contain", summarizes the scientist.
Therefore, a data strategy must offer room to categorize the topics raised by the various company representatives, prioritize them and exhibit independence. "This way, the topic as a whole, that is, the transformation to a data-driven company, can gain more attention with management and with that also help achieve more efficacy", she hopes.
Legner then presented what a data strategy can achieve according to the findings of the CC CDQ. One one hand, it must answer the question of how a company can use data to generate more value from it, execute it and subsume the entirety of it under the term "data monetization." On the other hand, the question must be answered as to how the company collects, saves, processes and manages data in order to generate value. This aspect is known as the "data basis."
Furthermore, according to Legner, a data strategy must match the companies maturity in regards to data storage. A company in Industry 4.0 can hardly develop new strategies, if the data foundation in the form of sensory data is too fragmented. Here, a data strategy must first be created to stabilize the foundation of the data.
A Blueprint for Data Strategies
To be helpful to companies on the road to their own data strategy, the economic scientists at the University of Lausanne have created a sort of blueprint, or template. This "Canvas" is based on current templates such as the "Business Model Canvas" from Alexander Osterwalder from the HEC Lausanne. The purpose is to break down the complex procedure into understandable elements, to bring different perspectives together and to steer discussions and suggestions into structured lanes. This is how the "CDQ Data Strategy Canvas" was developed. This defines elements like the hangers for the data strategy, the vision, the mission, the extent of the purpose and the value for the company. An important element of the data strategy is the establishment of certain data-related, supported organizational and technical skills within the company.
"It is also important in this context to support a certain rethinking in the way data is handled," said Legner, who spoke of a company's data management "Code of Conduct," as postulated in the canvas, in which the information should be thoroughly established. Finally, the advisable definition of a roadmap is one that outlines a timeline for how the data strategy should be implemented.
Mountain Climbing as a Metaphor
To solidify her theoretical concepts, Legner featured a number of examples of companies who visually developed their data strategies. In this context, the example of the German Telekom can be highlighted. The telecommunications giant uses the metaphor of mountain climbing to visualize its data strategy. The summit symbolizes the goal of becoming a data-driven company. Achieving this goal requires the cooperation of the rope team, the expertise of a mountain guide and also concrete tools like guideposts and maps to find the way to the goal. The German Telekom created a concrete "Chief Data Office" that serves as a so-called mountain climbing office to plan, coordinate and take the first steps toward reaching the summit.
Christine Legner from the University of Lausanne presented the paths toward a data strategy for companies during the TDWI Switzerland conference in Zurich. Read the original article on computerworld.ch (in German)