So it's not surprising that some people tend to associate the term with negative connotations, along the lines of "Here come the bureaucrats!" At its core, it is actually about regulating fundamental issues in the handling of data in the company, e.g. 1:
- For which use cases do I need data?
- What quality of data must be available?
- Who owns the data?
- Who can access the data?
- How long is the data valid and when can it be deleted?
- How confidential is the data?
- Which technologies do I want to use to manage my data?
- ...
Recognizing potential through data governance
To answer these questions, it is not enough to set up a purely technical solution, such as a data lake, and then leave it to IT. Instead, this requires proactive management of data across the entire lifecycle and across the entire company. Rigid and isolated data silos can thus be broken up, allowing new cross-connections to grow within the company. And the potential this opens up is huge. For example, completely new data-driven products and services can be developed, as can currently be seen in the field of artificial intelligence. At the same time, costs, complexity and regulatory risks are reduced.
Data governance is an important factor for data-driven digital transformation and therefore also for every data-driven company. This transformation brings disruptive changes for many organizations, as can currently be seen in the field of autonomous driving, for example.
Breaking up old structures and accompanying change
Compared to the big tech companies (Apple, Amazon, Google, etc.), which have always grown up with data, this change is much more difficult for more traditional companies, such as those in the manufacturing or automotive industries. This is often due to historically evolved structures that first have to adapt, but also to the fact that in many cases a great deal of persuasion is still required from a large number of stakeholders. In addition, data governance is unfortunately not available off the shelf: Every company is unique and therefore needs a customized framework with corresponding roles, processes and standards. But it also needs clearly defined goals that are to be achieved with the implementation. This is only possible if the management is behind a corresponding initiative and acts as a sponsor with a clear vision and strategy, thus taking the company and its employees along with it.
Data governance = data quality = product quality
With the transformation to a data-driven company, data is increasingly becoming the focus of many companies' business models. Data and the intelligent products, processes and services based on it are thus increasingly becoming the unique selling point that companies use to differentiate themselves. However, this differentiation can only succeed if the product quality is high, i.e. if the associated applications function reliably, cost-effectively and securely. After all, nobody wants to get into a robo-taxi that regularly breaks down or can be remotely controlled by a hacker. Product quality, in turn, can only be guaranteed through high data quality. Due to the complex framework conditions in which many companies operate today, this can only be guaranteed on the basis of a well-implemented data governance initiative.
To summarize: Data governance is therefore the foundation for data-driven digital transformation.
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1 If you are looking for a more detailed introduction to data governance, you will find it here: https://www.alexanderthamm.com/de/blog/data-governance-grundlagen-herausforderungen-und-loesungen-im-bereich-data-management/
