The Value of a Data Governance Framework for State and Local Governments


The Value of a Data Governance Framework for State and Local Governments

State and local governments have access to more and more data each year, but the true value of data is only unlocked after its evolution from information to intelligence. This evolution can’t occur until data is integrated within a decision-making framework.  When actionable decisions are enabled from the intelligence derived as a result the data, then a fully-fledged value chain for the data assets has been established.

But these insights and the decisions they inform are only as valid and precise as the data on which they’re based. If the data is inaccurate, incomplete, inconsistent, or inaccessible – then any value that can be derived from it is constrained, and effective decision making is diminished. This is why a data governance framework is so critical.

What is a data governance framework?

A data governance framework brings together different IT and business stakeholders to define the rules that govern data management across the organization. It is the infrastructure that makes data governance possible and maintains data integrity, quality, and security.

A data governance framework touches every part of the data management process, including the technology and data architecture as well as the policies and processes that govern how data is created, used, retained, and secured. The framework also establishes measures for monitoring how data is managed and how regulatory compliance requirements are met.


Getting started with data governance framework

Any data governance framework implementation begins with an assessment of the organizations’ data maturity. This involves an understanding of what data assets exist, where data is located – on-premises and/or in the cloud – and what processes, procedures, and standards are in place for data handling, management, security, and so on.

Agencies must also consider the key drivers for data governance and the pain points they’re seeking to address. These typically center around four key areas:

  • A common data standard: All data that is collected, stored, and reported must “speak the same language.” Data governance can help achieve this by defining data rules definitions for each element, so they are consistent and data integrity is enforced.
  • Data interoperability: For effective data analysis, there must be seamless, secure, and controlled exchange of information between applications. As the data begins to flow across connected systems and stakeholders, a governance process is essential to keeping the data ownership and structure in place.
  • Data access and usage: As access to data increases and the volume of data requests grows, data governance can help ensure that data is used and shared appropriately.
  • Data security and privacy: A formal data governance framework enhances data security and privacy and helps agencies meet their compliance obligations.


Consider a data governance pilot

The most difficult part of building a data governance framework is getting started, especially when many organizations and datasets are involved. That’s why a data governance pilot is a great starting point. A data governance pilot applies the principles of data governance to a single dataset in order to

  • Quickly demonstrate the value of good data management
  • Identify a baseline for readiness status of available data assets
  • Foster a detailed understanding of the steps an organization needs to take to derive insights from their data assets


To maximize the value of a data governance pilot, choose a dataset directly related to an immediate challenge or need. During the pilot, you’ll not only establish a foundation for future policies and processes, but you’ll also uncover and socialize the level of effort needed to build the overall data governance framework.