Data Governance

Data governance is exercising the appropriate authority and control over a data asset to ensure its integrity, quality, security, and availability to all qualified users. Proper governance offers many benefits including increased awareness of data holdings, improved organizational efficiency and productivity, more citizen-centered services, and better informed decision-making and evidence-based policymaking resulting in Actionable Intelligence. Governance increases the value of Commonwealth data assets by guiding and enabling its evolution from information to intelligence while promoting data discovery, exploration, integration, and sharing through the implementation of enterprise standards, policies, guidelines, and best practices.

On January 8, 2020, Governor Northam signed an Executive order that established a data governance framework making Virginia a leader in data-driven policy, evidence-based decision-making, and outcome-based performance management. The primary goal of the framework is to help the Commonwealth make better informed decisions based on the data it collects and manages. The data governance framework includes the Virginia Data Commission, Executive Data Board, and a Data Governance Council.

The Commission's responsibilities include:
● Providing recommendations to the Governor on data governance, quality, sharing, analytics, reporting, intelligence and performance management.
● Advising and assisting the Chief Data Officer (CDO) to set, plan, prioritize, and review data and outcome performance goals and objectives.

The Board is responsible for:
● Translating commonwealth data-driven policy goals into agency performance targets.
● Allocating agency resources.
● Reporting to the Commission any recommendations.

Lastly, the Data Governance Council:
● Liaises between state agency operations and the CDO.
● Advises the CDO on technology, policy and governance strategies.
● Administers data governance policies, standards, and best practices as set by
the Executive Data Board.
● Implements data sharing and analytics projects.

The overall benefits of implementing the Virginia Data Commission will be to improve operational efficiency, increase delivery of customer centered services, and promote better outcomes for constituents. This framework supports the administration’s mission to deliver evidence based solutions that benefit all members of the Commonwealth.

Data Value Chain

The data value chain is the evolution of data from information to intelligence within an organization.​  It describes the various forms data can take as organizational units transform it to fit their needs.  Some have described “data” as the new “oil”, but that’s based on a flawed premise.  Oil decreases in value once it’s used, data does not.  Data increases in value the more it’s used and should be considered a non-depleting resource.  Leveraging the data value chain supports a virtuous cycle of continuous improvement when knowledge gaps and data errors are identified and corrected. 

The figure below describes the process associated with the data value chain.  Data is collected by an organization for a specific, usually operational, purpose.  There’s inherent value in the data or the bits and bytes stored within a database or information system due to the cost associated with its collection, storage, and management.  However, it isn’t until the data is interpreted that it doesn’t become information.  The interpretation of data is usually within a specific context or domain like transportation, education, health, environment, etc.  That is to say, the person interpreting the data is usually looking at it from a particular, very specific, perspective.  This perspective provides the constraints under which the interpretation applies and the information generated is limited (in most cases) to that domain. 

Domain specialists and experts assimilate the information identifying the patterns, trends, and mechanisms associated with the occurrence of the real-world events the data represents creating knowledge.  Knowledge can be integrated into the decision-making process of the organization creating intelligence.  Intelligence supports better informed decision-making and evidence-based policymaking resulting in actions making government agencies more efficient, providing better services, and improving the lives of citizens across the Commonwealth.

Data Strategy

Our data strategy has four primary components: Governance, Architecture, Management, and Intelligence.  Governance, as mentioned above, is the foundational component that drives the data architecture.  While governance is the people-based processes exercising authority and control, architecture is the blueprint or model of how data is collected, stored, processed, integrated, and analyzed.  It is the design process guiding the implementation of appropriate data management.  Management is where the “rubber hits the road”.  This is the component where networks are created, systems are developed, and standards are implemented.  The data management process supports data quality assessments, exploratory data analysis, data analytics, machine learning, and business or mission intelligence.  Without appropriate and consistent data management, data analysts, specialists, and scientists would have to individually document, curate, transfer, manipulate, and transform data prior to conducting any form of analysis.  You just can’t skip data management.  It’s an inherent part of every data analytics project and it is best done as an enterprise service and not an ad-hoc or project-based endeavor.  Completing the feedback loop, results generated by data analytics or intelligence projects inform the data governance layer by identifying flaws in the architecture, gaps in management, and/or data errors in the analytics making the organization smarter.     

Underlying the 4 components are 6 principles that guide the implementation of the data strategy.  These principles dictate that data is:

  1. used to support mission goals​;
  2. interpreted, analyzed, and assimilated to support actionable decisions​;
  3. standardized to promote interoperability and integration​;
  4. managed to maintain quality, integrity, and reliability​;
  5. accessible with appropriate security controls​; and
  6. disseminated to promote reuse​

Within the Commonwealth, Data Governance is the primary domain of the Chief Data Officer, while the Architecture, Management, and Intelligence components fall within the Chief Information Officer’s responsibilities.  The CDO supports the CIO as necessary in matters related to the creation, storage, and dissemination of data.  We recognize that good analytics depends on governed data and data governance is a team sport. 

Data Governance Executive Order