What is data governance?
Data management involves establishing the necessary procedures, standards, processes and tools to create consistent and secure data management and sharing. It is aimed to bring everyone in the organization on the same page.
“With bad data, we keeping making bad decisions. We just don’t realize they’re bad decisions until later.”– Scott Taylor, The Data Whisperer
Why is data governance important?
Improve decision making! Without proper data governance, there is no way you can consistently trust data, access data, develop valuable insights and confidently make business decisions. If you want your business data to satisfy criteria of availability, consistency, usability, and security, you need a data governance strategy.
Reduce complexity! By introducing standards and processes, you reduce complexity, redundancies and inefficiencies of data management.
Improve collaboration! There is a shared and consistent understanding of data and data definitions across the organization. Data governance reduces the need for data silos and enables collaboration across departments, leading to broader insights.
Data as an asset! Data governance adds context to an organization's data. It should be seen as an entity that has value, and that can determine the success of your business. Data monetization starts with having data that is properly stored, maintained, and made accessible in an optimal way.
What are the components of a data governance strategy?
- Identification: identify how the data is created, gathered, modified, stored and maintained throughout the organization. Identify the different data domains and the organizational structure. Identify data and business owners, dictionaries, catalogs, data quality measures, applications, etc.
- Standardized processes: enforce data management standards and processes for data gathering, storage and data transformation.
- Data quality management
- (Master) data management (~ single source of the truth)
- Rules and regulations
- Service level agreements
- Security and authorization
- Tools & Technologies: establish tools, architectures and technical roadmap to enable realization and compliance of data management standards. The tools and technologies must provide support to the entire data lifecycle, from data acquisition and integration to insight and information delivery.
- Organization: the roles and responsibilities, organizational structure and ownership for data management. Define the organizational model for data governance by setting up a necessary chain of decision making bodies, key objectives, deliverables, milestones, critical issues and monitoring processes.
- Governance controls and mechanisms: Identifies key activities to be performed and measures progress towards meeting objectives. These controls also monitor and determine how well governance and its processes are being adopted. Data Governance is not a one-off project, it requires continuous improvements and must be seen as a common practice. Controls must also be applied to all levels and must ensure a governance driven by business needs.
How can dataroots help you?
At dataroots we believe that the people are the center of business- and data-driven decisions. Together with you, we assess the current situation, and design and implement a tailor-made data governance strategy.