What Is Data Governance?

Data governance is a set of principles, standards and practices that ensures your data is reliable and consistent. It also helps ensure that your data can be trusted to drive business initiatives, inform decisions and power digital transformations. A successful data governance program enables you to do these things in a way that is repeatable. Such a program can scale and adapt as data volumes — and sources — grow and technologies evolve. In short, good data governance means you can use your data with confidence, both now and in the future. Check out our blog if you’re wondering how data governance is different from data management.

Why Does Data Governance Matter?

Data governance helps ensure that your data is consistent, reliable, accurate and trusted to enable data-driven decision-making. Traditionally data governance has been focused on risk and compliance. But the focus of data governance has shifted over the last few years. The advent of digital transformation and the exponential increase in the volume of data, distribution of data, data-related regulations and the number of users who want to be empowered with trusted data have contributed to the change. Data governance has moved from a rigid, one-size-fits-all approach to a more agile version tied to delivering the data intelligence required for data-driven decisions.

See how intelligent data governance lets you bring together people, processes and systems to deliver strategic business outcomes.

Hurdles such as the abundance of data, users and regulations can seem insurmountable. But the single greatest issue comes down to trust assurance in the quality and protection of the data. Trust that users can and should have approved access to appropriate and reliable use of that data, and trust that all team members are empowered to confidently use this information to deliver value creation opportunities. Effective data governance can help deliver the trust required in data. On that foundation, data governance can democratize trusted data to empower data consumers of all skill levels in the organization to propel analytics, AI and data-driven digital transformation.

What Is a Data Governance Framework?

Data governance policies venn diagram showing how an organization can be aligned

Venn diagram showing how a data governance framework creates a single set of rules and processes for collecting, storing and using data.

Data governance requires your organization to understand and take stock of your data. Which regulatory and legal requirements apply to your data? Which business best practices are most appropriate for your organization?

Once you have that understanding, you need to establish rules and adopt automated and human processes to enforce those rules. The drivers of data governance are usually regulatory and legal requirements, but the organization determines which rules to include.

Governance often dictates policies such as storage for certain types of data. It also codifies data protection methods, such as encryption or password strength. Data governance can dictate how to back up data, decide who has access, and sets guidelines for when you should destroy archived data. You can also set governance objectives around issues such as data quality or silos that isolate certain data.

You often hear about data governance “frameworks.” A data governance framework consists of the data strategy policies that impact everyone in the organization:

  • People (roles)
  • Processes and procedures
  • Technologies

If data governance is the “what,” then a data governance framework is the “how,” and alignment to your strategy, the “why”.

Key Elements of Data Governance

A successful data governance program possesses four key ingredients:

  • Vision and business case
  • The right people
  • Intelligent data governance technology
  • Efficient processes

Vision and Business Case

The vision spells out your broad strategic objective for building a governance program. The business case clearly articulates the specific business opportunity. Put another way, your vision is your destination, and your business case is your vehicle for getting there.

A vision statement, although broad, should be actionable, not abstract. It should look three to five years ahead. Here’s an example of a solid vision for data governance:

“Create a better customer experience by reducing the time to resolve issues, delivering more relevant marketing materials and protecting sensitive customer data.”

You must also have an actionable business case. This will be more pragmatic and hands on. It should specify the actual people (roles), technologies and processes that you’ll need to support your data governance efforts. Your vision becomes the basis for the policies you intend to implement that align your organization to your business goals for data.

Data Governance Roles

To deliver a positive return on your data, you need to put the right people in place. These roles will support, sponsor, steward and operationalize your data.

First, you need an executive steering committee or data governance council. This group communicates, prioritizes, funds, resolves conflicts and makes decisions about data governance for your entire enterprise. The steering committee comprises the executive leaders of your organization. Sometimes they belong to the C-suite. They may also be vice presidents or directors accountable for the specific lines of business.

You might be lucky and find an executive sponsor immediately so that a steering committee gets off the ground right away. In many cases, you’ll need some grassroots efforts before senior leaders are willing to commit. UNC encountered this very situation. In that case, they figured out how to navigate the pushback by starting with small governance projects. They then built an understanding of their internal culture so they could confidently act when the time was right.

The high-level people on your executive steering committee play important roles, but they’re not the only ones. An effective data governance program should also include the following people:

  • Executive sponsor. This is the C-level executive whose responsibilities span several silos. These silos can include functional, line-of-business, application, geographic and others. Identifying this person early is crucial to success. This person allocates resources, determines staffing and funding, identifies high-priority business issues and fosters cross-functional collaboration.
  • Data stewards. Data stewards are the business and IT subject matter experts (SMEs). They’re the ones who translate how your data governance framework affects your organization’s business processes, decisions and interactions. Business stewards must be IT savvy. Likewise, IT stewards must understand the business. Experienced business analysts who can act as communication bridges between business and IT can make the best business stewards. Data and enterprise architects and senior business systems analysts make strong IT stewards.
  • Data governance leader. This person coordinates tasks for data stewards and helps communicate decisions made by stewards. They also drive ongoing data audits and metrics that assess program success and ROI. And they can be the primary point of escalation to the executive sponsor and steering committee.

Data Governance Tools and Technology

Four Characteristics of an Intelligent Data Governance Solution

Intelligent data governance characteristics include automation, scale, extensibility and agility

Intelligent data governance characteristics include automation, scale, extensibility and agility

Connect data through people, processes and technology to drive enterprise transformation and achieve better business outcomes.

In the context of data governance, “technology” means automation. Many technology solutions and platforms can help you automate data governance. To choose the right one, consider the full lifecycle of critical data, from creation to archival.

You should also focus on intelligent automation. Intelligent automation possesses four key qualities:

  1. Automation
  2. Scale
  3. Extensibility
  4. Agility

Data Governance Policies and Procedures

Business policies and standards are critical for any data governance program. It’s important to agree on policies that can apply throughout the enterprise. Typical policies include:

  • Data accountability and ownership
  • Organizational roles and responsibilities
  • Data capture and validation standards
  • Information security and data privacy guidelines
  • Data access and usage data retention
  • Data masking
  • Data archiving policies

The culture at each organization is different. There isn’t a right or wrong set of policies to consider. As you map out your data governance program, watch out for any potential perception of “red tape.” Instead, today’s successful data governance programs work together and focus on improved collaboration. Decide together on what’s best for the organization while also understanding that enforcement doesn’t have to feel restrictive. By making this pivot, you will shift your data governance program from being policy centric to value centric.

Data Governance Best Practices: 5 Steps to Begin Your Data Governance Journey

If you are just beginning to explore data governance, here’s a simple five-step roadmap to help you succeed.

Step 1: Select a project

Your first data governance initiative is critical. Get it right and you’ll have the opportunity to expand into an enterprise-wide program.

Selecting the right project is key. If this is your first attempt at data governance, you must be able to show demonstrable value to the business. And that means you must deliver a hard return on investment (ROI) — or at the very least return on effort — in a reasonable timeframe. If possible, make it a project that will excite senior management. That means being able to provide metrics that show tactical success as well as progress on longer-term goals.

Step 2: Set your goals

What do you want to achieve? This is not a rhetorical question. More governance programs fail because goals are too vague or expectations differ. Here are some examples of the most common data governance goals:

  • Improve efficiency of critical processes that have suffered in the past from low-quality data
  • Better, more effective compliance with regulations (this can include risk reduction and penalty avoidance)
  • Consistent use of trusted data across the enterprise to drive every tactical and strategic decision

Step 3: Get the right people and organize them appropriately

Data governance programs involve a lot of people. Even if your actual data governance team is small, your project will impact large numbers of employees, customers, partners — in short, anyone who depends on your data. Many of these people will have opinions, and some will voice them loudly. Don’t be fearful of this. Embrace their passion, but make sure to organize it.

Use a responsibility assignment matrix like RACI (which represents roles for responsible, accountable, consulted and informed). This ensures that the right people provide input — and approvals — at the right time, and that everyone understands their individual responsibilities.

  • The responsible person is likely to be an experienced project manager. This person manages schedules, assigns resources and builds the case.
  • The accountable person takes ownership of the major decisions and the results of the program. This is likely to be an executive-level person who owns the resources and who has veto power.
  • The consulted people are the business and IT subject matter experts. They are the ones who will help you provide the necessary context to achieve your goals.
  • The informed are the people who will be affected by your data governance effort. They don’t have a direct say in the direction of your initiative — and this is something you’ll need to make clear from the start.

Step 4: Create your processes

Your data governance teams need clearly defined, repeatable processes that are designed for the reality of the task ahead. There are four core processes that support every data governance program:

  • Discover: Identify and understand the data being governed
  • Define: Document data definitions, policies, standards and processes. Assign ownership (a critical, often-overlooked step) and define your key metrics and KPIs
  • Apply: Operationalize data governance policies, business rules and stewardship
  • Measure and monitor: Measure the value of your data governance efforts and monitor compliance with your policies

Step 5: Choose your technology

Data governance initiatives are always evolving. New internal data projects as well as regulations (and new risks) constantly appear. You need a technological platform that delivers value today but can also adapt and evolve as your requirements change. Here are some key considerations when considering your data governance technology:

To learn more about the must-have elements of a modern, intelligent data governance solution, download the eBook “How to Use Data Intelligence to Drive Better Business Decisions.”

Data Governance Examples: Customer Success Stories

AIA Singapore improves sales, cuts costs through greater data governance

AIA Singapore offers insurance products and medical protection to individuals and businesses in Singapore.

Goal: AIA sought a deeper understanding of its business by identifying customer and financial data based on lineage and intelligent metadata. The objective: improve data quality to increase sales, improve decision-making, and cut costs.

Solution: AIA used Informatica’s data governance solution to create a data governance framework, which automatically scanned and indexed metadata from core systems using Informatica’s data catalog solution.

Result: AIA achieved a deeper understanding of customer data by tracking data movement and transformations. The Informatica solutions also maintain data quality, enabling AIA to optimize sales, decision-making, and costs.

McGraw-Hill Education Boosts Digital Market Share with Enhanced Data Governance

McGraw-Hill Education is one of the "big three" educational publishers providing educational content, software and services for pre-K through postgraduate education.

Goal: McGraw-Hill Education wanted to grow revenues in an increasingly digital educational marketplace. To achieve this, it needed to improve business intelligence reporting.

Solution: By deploying Informatica’s data governance solution, McGraw-Hill Education developed a data governance management framework. It used Informatica’s data quality solution for data profiling and to track data quality.

Results: Today, McGraw-Hill Education is seeing strong digital growth in the higher education market and increased profitability. The organization has improved its decision-making by achieving a better understanding of sales trends through trusted data.

For first-hand insights into building successful data governance programs, there’s no better source than your peers. Learning from people who have charted their own governance journey gives you not only best practices, but a chance to learn from their missteps and build on their experiences. The Informatica Data Empowerment Experts Series is a monthly webinar series that does just that: Bringing together people from a variety of industries to share the stories and real-world lessons they learned while empowering their organizations with clean, well-governed data. Register now for the next webinar or catch up on past sessions on demand at www.informatica.com/dataexperts.

What Are the Benefits of Data Governance?

If not done the right way, data governance may be perceived as just more red tape and corporate controls. That’s why it’s important to take your first successes — the ones that drive collaboration and new business opportunities — and evangelize them. A little internal marketing goes a long way to publicize the value you’re bringing to the organization.

And by encouraging people to understand and even participate in data governance activities, you’ll help them see it less as a rigid sort of control and more as an exercise in driving business value creation and collaboration for advantages that will benefit everyone and generate positive business outcomes, such as:

  • More efficient, transparent and reliable business reporting that draws on un-siloed and trusted data
  • Better collaboration between business and IT, thanks to shared responsibility for improving data quality and appropriate use that builds trust
  • Shared understanding of business terms and policies impacting data leading to better data intelligence for data-driven decisions 
  • More accurate analytics and AI driven by democratized access to trusted data
  • More empowered and productive data users by enabling self-service access to trusted data
  • The ability to enable compliance with data-centric policies and regulations

An intelligent approach to modern data governance that includes the right people, processes and technology is key to the success of your organization’s digital transformation journey. Whether you’re pursuing greater customer centricity, better analytics or improved regulatory compliance, an enterprise data governance program can ensure that the data driving your initiatives is trustworthy, high-quality, available and accessible to everyone who needs it.

Data Governance Resources