Data Governance: A Business Strategy.
High-value information is scattered across your organization, making it difficult for you to govern your data. Connect all of your disparate applications to manage your data to make smart decisions and ensure regulatory compliance.
Assess current state and define target state organization, roles, processes for governance. Evaluate technology and tool options. Define architecture guidelines and roadmap.
Execution and Enablement
Develop policies and processes for data governance and analytics governance. Deploy and configure technology and tools. Map personnel to key roles and provide coaching and guidance for execution.
Data governance provides businesses with concrete answers to how their enterprise can define and prioritize the financial benefits of data while mitigating the business risks of poor data.
Foundational Data Governance consists of an organization’s internal policies and processes that enable organizational alignment, human capital performance, and the elevation of data as a strategic asset.
- Organization, roles and responsibilities -data stewards, custodians, governing bodies, accountabilities, decision authorities and a community-of-practice
- Awareness, communication and transparency
- Enablement of data quality, business data glossary, reference data workflow management, collaboration and governance
- Continuous measurement and improvement of data quality metrics for data definition, structure, validity, timeliness and completeness.
- Policies, processes, standards, procedures, SLAs, frameworks and best practices
- Definition of metadata and data lineage to track the origins of data and its movement through the system and data classification
Data Governance 101
What is Data Governance?
A management function with direct accountability, which establishes and enforces a set of processes to enable the elevation of data as a strategic asset.
By accounting for people, processes, data and technology, organizations are able to create trusted, information-based assets.
Why Data Governance is Needed?
Problems to be Addressed:
- Many Versions of the Truth
- Poor Data Quality
- Need to Democratize Analytics
- Analytic and Reporting Errors
- Regulatory Compliance
What are the top 3 key drivers?
- Increase Revenue –Ready access to information that supports competitive advantage
- Manage Cost –Reduce process inefficiencies and time spent searching and collecting data
- Manage Risk –Identification and avoidance of privacy, compliance and security risks
What are the Frameworks?
- Foundational Data Governance
- Analytics Governance
- Applied Analytics Governance (Model Risk Management)
- Information Lifecycle Management