Table of Contents
What is a data governance framework?
Why do I need a data governance framework?
4 pillars of data governance readiness
How are regulatory compliance and data governance frameworks related?
Why do companies choose Informatica data governance solutions?
Data governance examples: success stories
More data governance resources
What is a Data Governance Framework?
A data governance framework creates a single set of rules and processes for collecting, storing and using data. Even with an ever-growing volume of data, a data governance framework makes it easier to:
- Streamline and scale core data governance
- Maintain policy and regulatory compliance
- Democratize data
- Support collaboration
A data governance framework helps ensure that your policies, rules and definitions apply to all the data in your organization. It helps you deliver trusted data to individuals in many roles, from business leaders to data stewards and developers.
A framework also allows you to introduce self-service tools. These tools can empower even non-technical users who want to find and access the data they need for data governance and data analytics. And a framework ensures that you can govern, transform and deliver data across all applications and analytics deployments, in the cloud and on-premises.
Why Do I Need a Data Governance Framework?
Businesses use a data governance framework to define and document standards and norms, accountability, ownership, and roles and responsibilities. A data governance framework also establishes:
- 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
- Data quality rules
A data governance framework includes data discovery to create a unified view across the enterprise. This includes not only the data itself, but also:
- Data relationships and data lineage
- Technical and enterprise metadata
- Data profiling
- Data certification
- Data classification
- Data engineering
Because it defines the essential process components of a data governance program, a data governance framework supports data governance for the organization. This includes implementing process changes that:
- Improve and manage data quality
- Address data issues
- Identify data owners
- Build a data catalog
- Create reference data and master data
- Protect data privacy
- Enforce and monitor data policies
- Drive data literacy
- Provision and deliver data
The business can then use the data governance framework to measure and monitor the results. This allows them to optimize for trust assurance, data privacy and data protection. A framework makes it easier to:
- Track processes, data quality and data proliferation
- Monitor data privacy and risk exposure
- Maintain awareness of data access and use anomalies
- Create an audit trail
- Facilitate policy and other remediation management and workflow.
What Are the Pillars of Data Governance Readiness?
The ultimate objective of your data governance strategy is to generate the greatest possible return on data. A framework helps you capture critical opportunities to leverage data assets. It also helps you avoid the risks related to inappropriate data use or exposure. These are the critical factors to consider as you assess your data governance readiness and maturity:
- People. Governance succeeds when there is collaboration and planning. People need to determine technology requirements and define the policies and processes that drive the data governance outcomes that support strategic objectives. Important points to consider include: Are your people committed to data governance? Have you formally defined their roles and responsibilities? Do they have the necessary skills along with adequate data literacy? And, have you developed a change management plan (which includes sponsors), that supports alignment and buy-in?
- Processes. These allow people to confirm that your data is managed throughout the enterprise. This ensures that your critical business processes draw on trusted data. Important points to consider include: Are your data definitions, rules and goals realistic and appropriate? Have you modernized your business processes? And, did you review your business rules to integrate data governance so you can deliver meaningful results?
- Contributors. These are the business and IT subject matter experts who provide necessary context. They’re the business leaders, process owners and stewards who run the upstream and downstream processes impacted by your initiative. Contributors can also include IT architects, analysts and systems experts. Important points to consider include: Can you identify your data governance stakeholders? And, do you know where contributors maintain expertise across the organization?
- Technology. These are the platforms, tools, and subject matter expertise that enable reliable data governance processes. Even when some governance is already in place, platform technology enablers can improve outcomes. Data profiling, lineage and metadata tools let you automate and scale your data governance processes and accelerate time to value.
Important points to consider include: Do you have a complete platform capable of scaling out data governance across your organization? And, have you identified any gaps — such as data quality, data privacy, data lineage or data sharing — so you have the tools you need?
How Are Regulatory Compliance and Data Governance Frameworks Related?
Your organization needs to be able to address regulatory compliance and industry mandates. Regulatory compliance can include regional mandates such as the European Union General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Examples of industry mandates include financial reporting and accounting transparency mandates like Sarbanes-Oxley (SOX), or healthcare data privacy mandates like the Health Insurance Portability and Accountability Act (HIPAA). Data governance is how your company achieves the goal of regulatory compliance — it makes implementing policies such as GDPR compliance feasible.
A data governance framework enables regulatory compliance with policy mandates, including:
- Defining and classifying regulated data in scope of regulations
- Determining how, why and where your company accesses, moves and uses regulated data
- Managing appropriate rights for use within your organizations, including data subject consent
- Evaluating risk exposure so you can remediate with protection and disposition data accordingly
Identifying key compliance and regulatory mandates is a critical part of every data governance readiness assessment. You risk non-compliance when you are not aware of the industry regulations and regional laws that apply to your business. Non-compliance exposes your company to consequences such as fines, penalties and remediation costs.
When you know what the regulatory compliance requirements are, you can build a data governance program to meet those needs. And by adding capabilities such as data discovery, data masking for anonymization, and metadata management, you can ensure that your governance program can evolve.
A better understanding of your data and improving data literacy delivers other benefits. Your governance program will scale to support other data governance initiatives, such as:
- Cleaning customer data for marketing
- Streamlining reporting for sales
- Launching enterprise-wide analytics
Why Do Companies Choose Informatica Data Governance?
We want our customers to be successful, today and in the future. So we designed the Informatica Intelligent Data Management Cloud™ (IDMC) to deliver value today and to adapt as your data governance requirements change. Today’s business objective may be data quality. Two months from now, you may want to support a company-wide customer experience program. And later next year, you may want to accelerate trusted data sharing with a data marketplace.
With every project, you’ll be able to onboard new data and new users without compromising speed or effectiveness. Our Informatica Processing Unit (IPU) consumption-based pricing makes it easy to add new core capabilities as you need them, expanding to support a variety of data management systems and tools.
Our technology platform is built to be modular, integrated and interoperable. Informatica cloud-native solutions easily connect to each other and to other applications. Want your team to spend more time on analysis and strategy? Our CLAIRE™ engine applies artificial intelligence and machine learning to automate formerly manual processes. Automating processes such as data discovery or cataloging means your team spends less time curating data or searching for data sources.
Our IDMC data governance and cataloging services govern data across cloud, hybrid and multi-cloud environments from a single interface. They also scale to handle rapid growth in data and users. And the same centralized data governance console creates a single place where everyone can measure their use of data against the standards and norms that you’ve established:
- Connect data lineage to business processes
- Document governance policies
- Align workflows across business and IT
Data Governance Examples: Success Stories
Celcom Transforms Data into Opportunity and Meaningful Customer Experiences
The Challenge. The leading telecommunications provider in Malaysia, the company is undergoing a massive transformation from traditional mobile provider to converged digital operator — ushering in a new era of connectivity. They wanted to:
- Strengthen information governance
- Improve data quality through automation to reclaim employee time for more strategic tasks
- Make smarter decisions and ensure regulatory compliance with faster reporting
The Solution. Used Informatica data governance tools as a foundation for enterprise-wide governance. This data management framework creates a single holistic view of customers. Enabling enterprise-wide data discovery allows them to assign ownership, KPI, policy and process workflow. Celcom is now able to speed up data deduplication up to 30x faster, allowing rapid decision making with governed data. Read more about their data governance success story.
AIA Singapore Gains a Deeper Understanding of What Customers Want
The Challenge. This life insurance and financial services provider wanted to better understand their customers. They wanted to engage with them in more personalized ways, offer them new products and services and reduce operational costs.
The Solution. They developed an enterprise-level data governance management framework. This new framework included a collaborative business glossary, data lineage, and intelligent metadata. With this new framework, they can now track data throughout the organization and keep data quality high. This new visibility helps agents and employees with decision making. And that has led to optimized sales and costs. Learn more about AIA’s data governance success story.
More Data Governance Readiness Resources
- Read more about what data governance is and why it matters.
- Learn how you can successfully navigate common data governance challenges.
- The Data Governance Program Workbook: A step-by-step guide to launching a data governance program.
- 5 Best Practices for Unified Enterprise Data Governance on Azure: Share the right data with the right people using cloud data governance.
- Reimagine Data Governance: The framework you need to transform your business.