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Data Governance


Data governance comprises the entirety of processes, guidelines, standards, and technologies required to ensure the availability, usability, integrity, security, and effectiveness of data in an organization. The aim is to guarantee high data quality and responsible use of data.


With the increase in data volumes and the diversity of data sources, organizations have recognized the need to better manage their data assets. Data governance has emerged in this context to ensure that data is properly handled throughout its entire life cycle. This includes aspects such as data protection, data security and compliance with legal requirements.

Areas of application

Data governance is relevant across industries and is used in every company that uses data systematically. It is particularly critical in areas such as financial services, healthcare, and public administration, where the handling of sensitive data is strictly regulated. In industry, it supports, among other things, compliance with quality standards and the optimization of business processes through reliable data.


Effective data governance leads to improved data quality, which in turn enables more accurate analyses and better business decisions. It also protects companies from the risks of poor data management, such as financial losses, reputational damage, and legal sanctions.


Implementing effective data governance is often challenging, as it requires company-wide coordination and adjustment of corporate cultures. Resistance to change, complex data landscapes and the lack of clear responsibilities can make establishment difficult.


A specific example of the application of data governance in industry is the management of customer data in a B2B retailer portal. By following strict governance guidelines, companies can ensure that customer data is handled correctly, securely, and in accordance with regulatory requirements.


Data governance is crucial for ensuring data quality and security in companies. By establishing clear policies and processes, organizations can maximize the value of their data and minimize legal and operational risks.