Venn diagram showing how a data governance framework creates a single set of rules and processes for collecting, storing and using data.
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:
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:
- Focus on flexibility, agility and interoperability so you can grow and scale as business needs evolve
- Have capabilities to address all critical needs such as data cataloging, data stewardship and governance, data quality, and data sharing and democratization
- Automate to accelerate processes, workflows, data discovery and reporting
- Consider the cloud for scalability gains
- Build a metadata repository
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
- Data Governance Framework: Pillars for Success
- Data Governance Challenges and Best Practices
- Webinar Series: Data Empowerment Experts
- Data Governance vs. Data Management: What’s the Difference?
Data governance means setting internal standards—data policies—that apply to how data is gathered, stored, processed, and disposed of. It governs who can access what kinds of data and what kinds of data are under governance.What are the 4 pillars of data governance? ›
- Identify distinct use cases. ...
- Quantify value. ...
- Improve data capabilities. ...
- Develop a scalable delivery model.
Data governance (DG) is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused.What are the 3 key roles of data governance? ›
All the other data governance roles — data admins, data stewards, and data custodians — exist to help data users with data-driven decision-making.What are types of data governance? ›
- De-centralized Execution – Single Business Unit. ...
- De-Centralized Execution – Multiple Business Units. ...
- Centralized Governance – Single or Multiple Business Units. ...
- Centralized Data Governance & Decentralized Execution.
An example of data governance is when an organization adopts a data governance initiative in order to: define data models, distribute roles and responsibilities regarding the use of data, retention of old and new data — particularly sensitive data — create data standards, implement protection and establish security in ...What are the three 3 data principles? ›
Principles of Transparency, Legitimate Purpose and Proportionality. The processing of personal data shall be allowed subject to adherence to the principles of transparency, legitimate purpose, and proportionality.What is data governance tools? ›
Data governance is the collection of processes, policies, roles, metrics, and standards that ensures an effective and efficient use of information. This also helps establish data management processes that keep your data secured, private, accurate, and usable throughout the data life cycle.What skills are needed for data governance? ›
Knowledge, Skills, and Abilities
Strong understanding of databases and data structures. Strong analytical and time management skills. Excellent written and verbal communication skills. Intermediate facilitation skills with the ability to drive issues to closure.
Following standardized rules and regulations: A data governance process should follow standardized rules and regulations to avoid risks and noncompliance. Organizations should define proper rules and guidelines for things such as data access, data definition, privacy policies and security standards.
Typical universal goals of a Data Governance Program:
Train management and staff to adopt common approaches to data issues. Build standard, repeatable processes. Reduce costs and increase effectiveness through coordination of efforts. Ensure transparency of processes.
- Identify and Prioritize Existing Data. ...
- Choose a Metadata Storage Option. ...
- Prepare and Transform the Metadata. ...
- Build a Governance Model. ...
- Establish a Process for Distribution. ...
- Identify Potential Risks. ...
- Constantly Adapt Your Data Governance Framework.
How did you come to be in the Data Governance arena? Give me your Data Governance elevator pitch. How do you measure the success of Data Governance initiatives? Give me an example of how you went about implementing governance before and what you would do differently this time.What are the 7 types of data? ›
Most modern computer languages recognize five basic categories of data types: Integral, Floating Point, Character, Character String, and composite types, with various specific subtypes defined within each broad category.Which is the best data governance tool? ›
- IBM Data Governance.
According to the ICO's website, The GDPR was developed based upon seven principles: 1) lawfulness, fairness and transparency; 2) purpose limitation; 3) data minimization; 4) accuracy; 5) storage limitation; 6) integrity and confidentiality (security); and 7) accountability.What are the 7 key principles? ›
- Lawfulness, fairness and transparency.
- Purpose limitation.
- Data minimisation.
- Storage limitation.
- Integrity and confidentiality (security)
The GDPR has a chapter on the rights of data subjects (individuals) which includes the right of access, the right to rectification, the right to erasure, the right to restrict processing, the right to data portability, the right to object and the right not to be subject to a decision based solely on automated ...
Data Governance Goals:
Foster an organized system to manage data effectively and ensure clean, consistent data. Ensure use of standard, repeatable processes for data entry and reporting. Support a culture of informed decision making based on clean, consistent and understandable data.
Data governance is a strategy that incorporates truth, integrity, and transparency as the foundation of a data management policy that encompasses stakeholders, technologies, and existing policies involved in data management and security.What is the biggest challenge with data governance? ›
Common Challenges of Data Governance include: Lack of Data Leadership. Understanding Business Value of Data Governance. Recognizing the Need / Pain Caused by Data.What is the difference between information governance and data governance? ›
Data governance focuses on technical data infrastructure, while information governance focuses on business processes surrounding data and physical information. Common data governance tools include applications, databases, stream processing, MDM, security and disaster recovery.What are the six governing principles? ›
They are popular sovereignty, limited government, separation of powers, federalism, checks and balances, republicanism, and individual rights.What are the main principles of GDPR? ›
- Lawfulness, fairness and transparency.
- Purpose limitation.
- Data minimisation.
- Storage limitation.
- Integrity and confidentiality (security)
Data governance challenges arise when enterprises lack the ability to monitor and control how data is used, or to provide insights after noncompliance has occurred.What governance means? ›
Governance has been defined to refer to structures and processes that are designed to ensure accountability, transparency, responsiveness, rule of law, stability, equity and inclusiveness, empowerment, and broad-based participation.What are the 6 types of governance? ›
The project reports aggregate and individual indicators for more than 200 countries for six dimensions of governance: voice and accountability, political stability and lack of violence, government effectiveness, regulatory quality, rule of law, control of corruption.What are the three types of governance? ›
Governance as leadership comprises 3 modes of governance, namely the fiduciary mode, the strategic mode and the generative mode.
The five principles of corporate governance are responsibility, accountability, awareness, impartiality and transparency.What is the difference between data management and data governance? ›
In the simplest terms, data governance establishes policies and procedures around data, while data management enacts those policies and procedures to compile and use that data for decision-making.