Data Governance Overview

What is data governance?

There are a few ways you can define data governance. Data governance can be processes and policies that support the availability, usability, integrity, security, and scalability of data in an enterprise. It’s also a framework to ensure everyone has the appropriate information to make the correct call on how to handle data.

As organizations begin to realize the value of their data, they are going to seek to extract information from the data supply chain, and proper data governance (coupled with appropriate metadata management) can ensure transparency in that supply chain. Everyone that interacts with data at any point shares a stake in organizational data, and more frequently citizen developers are adding to data before passing it along to colleagues, creating silos of necessary data that is vulnerable to the changed within an organization.

As systems proliferate our daily operations, data becomes more critical and ubiquitous. Data is an asset and critical to functioning effectively and managing risks. As the organization expands, there is an exponential increase in the amount of organizational data. Each system added brings a hefty load of interrelationships that will need to be managed in order to ensure smooth operation.

Good data governance consists of support, tools, and training to handle data properly.

How is data governance performed?

There are several ways organizations seek to govern data, either through formalized data governance or through informal channels. As governance programs mature, they will expand to include the following disciplines.

Policies & Procedures

• Act as guidelines for processes.
• Formalize behavior in order to clarify expectations on anything within data governance disciplines
• Demonstrate support of senior leadership.

Security

• Data governance seeks to strike the balance between data availability and data security.
• Privacy, compliance, and security programs around data are often outgrowths of a comprehensive Governance, Risk, and Compliance (GRC) program that joins business and IT interests in their policies.

Risk Management

• Blends many of the other data governance disciplines.
• Addresses issues across many domains within healthcare, from regulatory and compliance to finance.
• Assesses risk, helps define risk and defines controls to mitigate risk.

Data Management

• Integrative discipline covering many facets of many areas within an organization.
• Data management is a discipline in and of itself but has many ties to Data Governance.
• Focuses on quality, architecture, standardization, and conversion of data to information.
• Data Governance helps drive the direction of data management procedures and focus.

Compliance

• Compliance programs integrate policies and procedures with training and auditing to ensure organizational adherence.

Data Integration

• Data integration efforts, such as data warehousing and master data management is often included within Data Governance plans to support centralized reporting.
• Centralized data stores help support easier security methods
• Centralized data stores also support common terminology and consistency of reports and dashboards across the organization.
• Master data management is key.
• Focuses on analytics and business intelligence issues assist in driving standardization for not only data but procedures and policies.
• Integrated information delivery systems assist in security by centralizing data stores and reducing security risks related to multiple reporting systems.

Decision Support Feedback loops

• All of the disciplines to this point support management decisions on organizational goals.
• Promotes data usage across the organization.
• Assists in highlighting cross-functional issues within an organization.
• Continuously feeds new derived data into the system.