Why data governance is worthwhile (after all)

With the huge hype surrounding artificial intelligence (AI), another topic has gained momentum - data governance. This is because the possibilities offered by AI have made the efficient use of data even more important for corporate management.

‍Asimple example: the performance of a chatbot depends first and foremost on the accuracy and timeliness of the training data. Problems with data quality, incomplete data security, lack of compliance or distorted data are therefore all the more serious in this environment. They can result in serious mistakes, data breaches, discrimination and the outflow of company data.

To avoid this, data governance creates the framework for transparent, secure and lean processes with clear responsibilities. Despite these advantages, many companies are (still) hesitant to introduce a holistic data governance framework.

The measures appear too extensive and the benefits are not immediately visible. We therefore recommend initially linking data governance with business objectives and prioritizing the management of critical data with the involvement of all organizational levels in order to quickly create added value and enthusiasm for the topic.

Essential elements of a data governance framework:

• Data quality management: ensuring that data is accurate, complete and reliable
• Data security and ethics: protection of data against unauthorized access and compliance with data protection regulations
• User and content management: Assignment of responsibilities for the management of databases
• Lifecycle management: managing the lifecycle of data from creation to deletion
• Data architecture and modeling: Definition of standards for data structures and formats

With clear guidelines and methods for these areas, you not only benefit from greater transparency and clear responsibilities. Standardized processes also help you avoid redundancies and errors, thus ensuring higher data quality and integrity. By adhering to clear compliance rules, you finally receive reliable data for your decision-making.

‍Overcoming hurdles

However, in order to benefit from the advantages, a number of challenges, such as the complexity of the data landscape or rapidly changing technologies, must first be overcome when launching the data governance initiative.

There are various approaches and methods for this:  

‍Hub-and-spoke(centralization)

• ‍Responsibilities: Clear distribution of roles in hub and spokes
• ‍Processes: Hub as a central point of contact that coordinates and implements AI activities with specialist departments ‍
• Governance: Hub defines guidelines and control mechanisms, while the spokes implement and adapt them locally ‍
• IT infrastructure: Hub provides central data platform that all Spokes can use

‍DataMesh (decentralization) ‍

• Division of the data pool into domains: Domain teams take responsibility for the data that falls under their respective domain ‍
• Data-as-a-product concept: application of "product thinking" to analytical data. Data products with clearly defined properties are provided by the domain ‍
• Self-service data platforms: Provided by centralized teams, the platform enables domains to create, distribute and receive data products ‍
• Federated Governance: Defines the standards for quality, safety and interoperability in accordance with external regulations

However, there may also be organizational resistance to change and new responsibilities. For a successful implementation, a targeted change management process that involves all managers and stakeholders, sets clear goals for the introduction of data governance and draws up a corresponding communication plan is therefore highly recommended.

Perfect start for your governance framework

‍Weare happy to support you with our expertise in the development of your individual framework, in which all user roles and processes for effective data management are defined. Feel free to contact us!

Further insights into data governance can be found here:

‍