Data Governance – Definition, Challenges & Best Practices
Most companies already have some form of data governance for individual applications or business departments, although it is not necessarily comprehensively institutionalized. The systematic introduction of data governance is therefore often an evolution from informal rules to formal control.
Formal data governance is normally implemented once a company has reached a size at which cross-functional tasks can no longer be implemented efficiently.
Data governance is a prerequisite for numerous tasks or projects and has many clear benefits:
- Consistent, uniform data and processes across the organization are a prerequisite for better and more comprehensive decision support;
- Increasing the scalability of the IT landscape at a technical, business and organizational level through clear rules for changing processes and data;
- Central control mechanisms offer potential to optimize the cost of data management (increasingly important in the age of exploding data sets);
- Increased efficiency through the use of synergies (e.g. by reusing processes and data);
- Higher confidence in data through quality-assured and certified data as well as complete documentation of data processes;
- Achieving compliance guidelines, such as Basel III and Solvency II;
- Security for internal and external data by monitoring and reviewing privacy policies;
- Increased process efficiency by reducing long coordination processes (e.g. through clear requirements management);
- Clear and transparent communication through standardization. This is the prerequisite for enterprise-wide data-centric initiatives;
- Further, specific benefits result from the specific nature of each data governance program.
More than ever, data governance is vital for companies to remain responsive. It is also important to open up new and innovative fields of business, for example by big data analyses, which do not permit the persistence of backward thinking and overhauled structures.