Tobias is a PhD student at the Business Engineering Institute St. Gallen. Already during his studies at the University of Konstanz, University of Hamburg and Copenhagen Business School, among others, he was enthusiastic about business informatics. After practical insights into the automotive industry as well as auditing, he wrote his master's thesis on offensive data strategies in cooperation with Fraunhofer ISST. In his upcoming PhD, Tobias will focus on the topics of data strategies and artificial intelligence.

Data Lineage: A Path to the Data-Driven Enterprise?

In recent years, the call for data-driven decisions and processes has grown rapidly in companies across every industry. Data-driven companies such as Apple, Alphabet, and Microsoft are now among the most valuable companies in the world. But in order to make data-driven decisions, different challenges concerning a company and its data (management) need to be solved. These include the challenge of keeping an overview of their own data stocks. An overview of one’s own data is playing an increasingly important role in the financial industry, too. Banks must be able to disclose their data to the regulator at any time, and regular reporting is becoming the norm. In this context, the principle of “data lineage” has become established. Based on the approach of metadata management, this concept enables the structuring and visualization of one’s data inventories and sources. The following article explains the most important basics of the topic “data lineage” and shows possible areas of application in the financial industry.

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Synthetic Data – The Future of Data-Driven Financial Services?

One of the biggest challenges in this context is the handling of bank-specific and personal data and its processing by artificial intelligence (AI). The basis of data-driven services is a high-quality and up-to-date database. But not all companies have a large enough database to train an algorithm, and the sharing and basic use of some data is strictly limited – sometimes even within the company. To counteract these problems, the concept of synthetic data has become established.

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Gaia-X – A Revolution for the Financial Industry?

Cloud solutions are faster, more flexible and less expensive than on-premise solutions. However, the best-known cloud infrastructures – Amazon Web Services (AWS), Google Cloud, Microsoft Azure and IBM’s Red Hat – all have one thing in common: they are headquartered in the USA and are therefore subject to the (decidedly lax) US data protection “law”. This makes using the services difficult from a data protection perspective, especially for financial institutions. The European Gaia-X project could be a real alternative.

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