Understand the four key elements of data governance readiness and how a strong data governance framework helps you define the structure for your data governance program.

Person managing product information

What is a data governance framework?

A data governance framework creates a single set of rules and processes for collecting, storing, and using data. By doing so, the framework makes it easier to streamline and scale core governance processes, enabling you to maintain compliance, democratize data, and support collaboration—no matter how rapidly your data volumes grow. (Need some background on data governance? Learn more about the definition of data governance and common data governance challenges).

With a data governance framework, you can ensure that your policies, rules, and definitions apply to all your data across your entire organization. You can deliver trusted data to a broad range of individuals in a variety of different roles, from business leaders to data stewards and developers. You can introduce self-service tools that enable even non-technical users to find and access the data they need for governance and analytics. And you can ensure that data is appropriately governed, transformed, and reliably delivered across all applications and analytics deployments in the cloud, on-premises, or both.

Download the data governance workbook, which provides a step-by-step guide to launching a data governance program.

Why do I need a data governance framework?

A data governance framework enables the business to define and document standards and norms, accountability, and ownership. In addition to setting out roles and responsibilities, this involves establishing key quality indicators (KQIs), key data elements (KDEs), key performance indicators (KPIs), data risk and privacy metrics, policies and processes, a shared business vocabulary and semantics, and data quality rules.

A data governance framework includes discovery of data to create a unified view across the enterprise. This includes not only the data itself, but data relationships and lineage, technical and enterprise metadata, data profiling, data certification, data classification, data engineering, and collaboration.

A data governance framework supports the execution of data governance by defining the essential process components of a data governance program, including implementing process changes to improve and manage data quality, managing data issues, identifying data owners, building a data catalog, creating reference data and master data, protecting data privacy, enforcing and monitoring data policies, driving data literacy, and provisioning and delivering data.

The business then uses the data governance framework to measure and monitor the results to optimize for trust, privacy, and protection. It tracks processes, data quality, and data proliferation; monitors data privacy and risk exposure; alerts you to anomalies; creates an audit trail; and facilitates issue management and workflow.

What are the pillars of data governance readiness?

The ultimate objective of data governance is to generate the greatest possible return on data, capturing critical opportunities to leverage data assets while avoiding the risks of exposing them. These are the critical factors to consider as you assess your data governance readiness and maturity:

  • People. People collaborate on determining the technology requirements, define the processes, and ultimately drive the data governance outcomes that support strategic drivers. Are your people committed to data governance? Have you formally defined their roles and responsibilities? Do they have the necessary skills? Have you developed a change management plan, including sponsors, to support organizational alignment and buy-in?
  • Processes. Data governance processes allow people to confirm that your data is formally managed across the enterprise, which ensures that your critical business processes draw on trusted data. Are your data definitions, rules, and goals realistic and appropriate? Are your business processes modernized and your business rules reviewed so that they can integrate data governance cleanly and deliver meaningful results?
  • Contributors: Business and IT subject matter experts who provide necessary context, including business leaders, process owners, and stewards who run the upstream and downstream processes impacted by your initiative, as well as IT architects, analysts, and systems experts
  • Technology. Technology includes the platforms, tools, and subject matter expertise necessary to enable a sound data governance process. Even if your existing systems are already governed to some extent, platform technology enablers such as data profiling, lineage, and metadata tools are critical to your ability to automate and scale your data governance processes and accelerate time to value.

How are GDPR and data governance readiness related?

The European Union General Data Protection Regulation (GDPR) requires companies to provide enhanced protection for the personal data of European consumers—including businesses that are based outside the EU. Data governance is how your company achieves that goal—it makes GDPR compliance feasible.

Data governance enables key compliance actions, including:

  • Defining regulated data

  • Determining how, why, and where your company uses regulated data

  • Managing consent and rights for use

  • Evaluating risk exposure on an ongoing basis so you can protect and purge data accordingly

Identifying key compliance and regulatory mandates like GDPR and the California Consumer Privacy Act (CCPA) is a critical part of every data governance assessment. Not understanding which industry regulations and regional laws apply to your business virtually guarantees non-compliance at some point, with all the business risks that implies. When you know what compliance requires of you, though, you can build a governance program to meet those needs. And when you already have capabilities like data discovery, data masking, data anonymization, and metadata management in place, your data governance program is also prepared to evolve to meet future regulations without major re-work.

What's more, your existing data governance framework will also scale to support other data governance initiatives, such as cleaning customer data for marketing, streamlining reporting for sales, or even launching enterprise-wide analytics.

Why do companies choose Informatica data governance?

The Informatica data governance platform is designed to deliver value today and adapt as your governance requirements change. You may deploy it at first to improve data quality in one business unit, then shift to support a company-wide customer experience program—all without compromising speed or effectiveness as you onboard new data and new users. With Informatica, you can also add new core capabilities as needed, expanding to support a variety of data management systems and tools.

Our technology platform is built to be modular, integrated, and highly interoperable. It needs only minimal coding to connect Informatica solutions to each other and to other applications. In addition, our CLAIRE™ engine applies artificial intelligence and machine learning to automate formerly manual processes like data discovery, cataloging, reporting, and even applying metadata so your team can spend more time on analysis and strategy.

Our data governance system can govern data across hybrid, cloud, and multi-cloud environments from a single location, with the scalability to handle extreme fluctuations in data and users. And the same centralized data governance console creates a single place to connect data lineage to business processes, document governance policies, and align workflows across business and IT so everyone is aware of how their use of data measures up to the standards and norms you've established.

Data governance success stories


Challenge: A traditional postal carrier turned global package distributor needed to monetize the value of its data, shorten its supply chain, and let its customers determine where, when, and through what distribution channel to do business.

Solution: The carrier centralized all of its data on a data management framework from Informatica that creates a single holistic view of customers and ensures it provides the right service level in the right place at the right time. The results include increased customer satisfaction, fewer steps in the supply chain, greater innovation, and increased revenues. Learn more about PostNL’s data governance success story.


Challenge: A life insurance and financial services provider wanted to understand its customers better to engage with them in more personalized ways, offer them new products and services, and reduce operational costs.

Solution: The firm developed an an enterprise-level data governance management framework including a collaborative business glossary, data lineage, and intelligent metadata, to track data throughout the organization and keep data quality high. Its deeper understanding of critical data ensures that its agents and employees have better information to optimize sales, decision-making, and costs. Learn more about AIA’s data governance success story.

More data governance readiness resources