Data Governance layers a management structure on top of data to assure data is identified, registered, categorized and continuously managed. Its function is to be the decision-making instance on data ownership, data maintenance throughout the data’s entire life cycle, data quality and data compliance. It also specifies and controls the rules concerning the usage, access, aggregation and flow of data.
Data Governance thus serves several purposes:
- Building a cross-entity data governance organization with clear roles and responsibilities (e.g. data owner), including external stakeholders (e.g. suppliers)
- Implementing this data governance, including awareness training and education of staff and role owners
- Identifying, classifying and registering data
- Defining the relevant data quality levels for each data class (e.g. no outdated data)
- Identifying the relevant compliance rules for a specific data set (e.g. retention times for financial records)
- Implementing concrete measures to establish compliance with the applicable regulations for a specific set of data (e.g. automatic alert if data reaches its retention period and must be deleted)
- Assuring appropriate data Access Rights Management (Identity and Access Management or IAM) by integrating data into the IAM environment
- Creating efficient processes assuring that data management can be performed most efficiently and effectively
Having good Data Governance means being able to self-clean the company’s data over time. You start with the highest priority areas and then work your way up step by step until you have achieved adequate data quality.
Data Governance brings substantial benefits to your company in different areas:
- Common business terminology helps users to quickly find relevant data for their purpose
- Users know to what degree they can trust the data available
- Access rights to data categories are connected to data criticality and compliance requirements
- Manual management is substituted by automation (categorization, access, deletion, etc.)
- Regulatory requirements are mapped to data categories, assuring data is treated accordingly
- Interaction between data pools are made easier
- Elimination of redundant, obsolete or trivial data simplifies the organization and infrastructure and eliminates unnecessary costs
- Compliance regulations and new data analytics projects can be implemented faster and easier
- It is impossible to reach perfection given the high number and complexity of the data!
- Data Governance usually requires many resources (both in staff and in finance) and existing organizational structures and competencies may have to be reorganized across the company