In our first blog on data governance, we described what data governance is and the important role it plays in health care.
We will now explore the data governance framework.
What the data governance framework should look like
The data governance framework is the blueprint for the people and processes in your data governance program, and the software you choose for them to use.
- The governance framework should enable communication across stakeholders to promote data sharing and acceptance.
- A Data Strategy increases the value, availability, and reliability of data.
- Data Policies and Processes measure and monitor data quality, ensure adherence to the business rules, ensure compliance with laws and regulations, and protects the data assets.
- Data Standards promote data consistency across the enterprise.
- Governance Organization defines the cross-functional teams that own the program and establish the “rules, tools and schools”.
- Data Stewardship curates the data assets to insure quality and compliance.
A driver for most data governance initiatives is the basic need to improve communications. As the desire to leverage shared data increases across the enterprise, so does the need for communication and active stakeholder participation in the data management process.
Communication is an integral part of all governance programs (e.g., data strategy, policies and process, and standards and models). Clear communication channels give stakeholders a voice into the data management process and increases their acceptance and ownership of data governance. Success of a governance initiative can often be evaluated by the effectiveness of its communication channels. The newest generation of data governance tools are designed to create a social, crowdsourced experience to enhance communication, along with their more traditional workflow capabilities. An emphasis on data literacy is also crucial, especially in a time where there is an increased demand for health care organizations to provide self-service access to data and analytics.
A structured, on-going communication plan is an important component to any data governance framework, and is essential to keeping stakeholders aware of changes in the data and any outstanding issues in the governance process.
Data Standards – These standards promote data consistency. Data standards cover business terms, or critical data elements that might be found in a business glossary. They also cover the definitions of master data entities, such as patient, provider, or member. Data standards can be applied to common data models, data sets, and analytics products.
Data Strategy – This focuses on increasing the value, timeliness, and reliability of data assets. Data management and data quality efforts are achieved through the continuous evaluation of internal data and processes, and the availability of external data sources to augment and improve data quality, and completeness. Data governance efforts typically focus on increasing the quality of the metadata around the data assets so they can be found more quickly, trusted, and understood.
Data Policies and Processes – These ensure the integrity, consistency, and sharing of enterprise data resources. Roles and responsibilities are assigned to data assets, as well as policies designed to protect and enhance the data. A data sharing request process ensures that the right people are granted access to data when they need it, while remaining compliant with regulations and laws.
An accountability framework promotes the desired behavior in the maintenance and consumption of crucial data assets, while business rules and policies focus on improving the quality of the data. Metrics may also be put in place to monitor data quality and performance.
Finally, data policies and processes should be captured in a set of standard operating procedures, including Service Level Agreements.
Data Governance Organization – The Data Governance Organization is typically a cross-functional team that makes interdependent rules, resolves issues, and provides services to data stakeholders. These team members generally come from the different areas on the business side of operations ranging from executive stakeholders to front-line data analysts. They establish policy that IT and data groups follow as they establish architectures, implement best practices, and address requirements. They are the primary curators of the metadata gathered and vetted within the data governance program.
Data Stewardship – The Data Stewardship function operates under the auspices of the Data Governance Organization. The Data Stewards represent the concerns of others and curate the data assets themselves that do not belong to the data stewards themselves. Some data stewards may represent the needs of the entire organization, while others may represent a smaller group, business unit, department, or a set of data. In some organizations data stewards may even play both roles – stewarding both the metadata and the data.
The benefits of data governance
There are numerous benefits to having a data governance program. They include:
- Increased consistency and confidence in the data supporting decision making,
- Consistent and effective use of data across the enterprise,
- Enhanced data quality and accuracy,
- Data consistency through enforcing common business rules,
- Increased data standardization,
- Decreased risk of regulatory fines,
- Improved data security, and
- Improved timeliness of data and coordination.
Getting started with data governance
HighPoint uses a Data Governance Strategy, Roadmap, and Activation Methodology focusing on iterations of rapid prototyping and effective, yet lightweight, governance structures. This includes communications, data standards, data strategy, and policies and processes.
HighPoint’s data governance assessment and activation looks at an organizations ability to enhance, deploy, and operationalize a data governance framework. Part of the assessment includes reviewing processes and building a roadmap for the future growth of the governance program.
The assessment looks at the strategic data governance needs and expands on the working pilot framework against those needs to help operationalize data governance.
The HighPoint Data Governance Assessment and Activation process focuses on proven governance use cases, such as:
- Metrics governance
- Report catalog
- Data catalog
- Reference Data Management
- Governance for Master Data Management
- Metadata management for data integration
- Data quality issue management
The process is divided into two phases: A two to six-week phase for assessments and roadmap development, followed by an activation period where selected use cases are implemented.
This allows for the integration of the data governance framework and processes into daily activities, which operationalizes the software and ensures user engagement and adoption.