data governance and Information governance can help enterprises frame the technical and business discussions required to meet governance, risk management and compliance needs while increasing the value of operationalizing data.
Both terms include the word governance. Other aspects are implied in both disciplines, including risk management, compliance, efficiency, alignment and simplicity. As businesses become more digital, a thoughtful approach to addressing the lifecycle and workflows of data governance and Information governance grows in importance.
The two disciplines approach related problems at different conceptual levels. Data governance focuses on various technical considerations of the data itself. Information governance focuses on the implications relating to the meaning of the data in relation to enterprise goals, business users, regulators, legal teams and customers.
Effective data governance frames the technical discussion on collecting the information; appropriately naming, storing and tracking it; creating value from it; and protecting and deleting it. It is essential because it provides a way of framing conversations for automating the best practices for managing every step of the data lifecycle. This can help plan a data architecture that simplifies the handoff between these various processes while maintaining desired service levels. Putting this into practice often requires a team effort spanning many different roles and areas of expertise. A chief data officer (CDO) may work with a variety of others to understand the requirements and intersection points of processes spanning a variety of data tools. Organizations can benefit from a clear data governance leader or committee who can oversee the entire process, identify gaps and improve the overall value. Data collection requires input from business units to understand how data is collected by various business applications, SaaS applications, third-party data sources and others. The responsibilities of these roles can include the following: Database analysts (DBA) and other data management professionals develop appropriate naming conventions and schemas to describe the data. Data engineers help identify best practices to store and stage data across various databases, data warehouses, data lakes or long-term archives. They also help set up and manage Master Data Management (MDM) tools to track data properties, data lineage and quality. Data engineers and data scientists find ways to weave this data into various analytics tools, decision engines and business apps using the appropriate data science and machine learning tools. Security and resilience experts create and vet the infrastructure and processes to protect the data from leaks, theft, corruption and ransomware. A data protection officer (DPO) oversees best practices and tools to reliably delete information when no longer required or in response to a data elimination request. Several roles within a business contribute to a well-rounded data governance team.
An Information governance initiative focuses on what needs to be done to improve business results, manage risks and comply with regulations.
