What is Data Governance? Use Cases, Best Practices & Tools [2023]

In today‘s data-driven business environment, every organization recognizes data as one of their most valuable assets. But without proper governance, uncontrolled data can become a liability.

Recent research shows that poor data quality costs companies an average of $15 million per year. And with data volumes doubling every two years, getting data governance right has never been more critical.

By 2022, 50% of companies expect to have a formal data governance program in place – up from just 20% in 2017. But what exactly is data governance and how can you implement it successfully?

In this comprehensive guide, we‘ll cover everything you need to launch an effective data governance strategy, including:

  • Core concepts and business drivers
  • Key program components
  • Best practices for optimal results
  • Common pitfalls to avoid
  • Leading technology solutions
  • The role of governance in GDPR compliance

Let‘s explore these topics in more detail so you can establish a solid data governance foundation at your organization.

What is Data Governance and Why it Matters

Data governance refers to the overall management, oversight, and accountability for data as a strategic business asset. It involves coordinated efforts across people, processes, and technology to establish standards, policies, procedures, and metrics that enable an organization to ensure the integrity, availability, and security of its data.

Without effective governance, companies struggle with:

  • Inconsistent data – With no standards, definitions, and processes governing data, inconsistencies emerge across systems and users. Conflicting metrics and reports undermine trust in data.
  • Security vulnerabilities – Data lacks appropriate security controls including classification, masking, backups, encryption, and access management. This leads to breach risks.
  • Compliance failures – Inability to adhere to regulations around data privacy, retention, lineage/provenance, and localization. Fines and reputation damage result.
  • Poor data quality – No oversight of problems like inaccuracies, incomplete information, duplication, and staleness degrades analytic value.
  • Excessive costs – With no central governance, unnecessary redundant data and infrastructure thrives. Cleaning up downstream costs more.

Data governance tackles these challenges through six key focus areas:

  • Policies – Strategic principles for valuating, managing, consuming data
  • Standards – Consistent definitions, formats, metrics, processes
  • Architecture – Technical infrastructure that supports governance
  • Quality – Measuring, monitoring and improving data quality
  • Metadata – Contextual data that describes meaning and relationships
  • Security – Controls that ensure availability, confidentiality, and integrity

Proper governance provides businesses with clean, consistent, and compliant data that can be analyzed to drive growth. Let‘s look at how to build a successful program.

Developing a Data Governance Strategy

The first step is defining the strategy and scope of your data governance initiative:

  • Obtain executive sponsorship – Present a business case to demonstrate how governance will impact revenue, customer satisfaction, risk, and efficiency. Ongoing executive support ensures funding and prioritization.
  • Establish a council – Assemble a cross-functional governance council with data owners, IT, compliance, and business units to guide the program.
  • Inventory data assets – Develop a full catalog of data sources, systems, usage, owners, security levels, and metadata organization-wide.
  • Define scope – Based on risk assessments and business impact, prioritize which data domains and use cases will be included in initial governance efforts.
  • Set measurable goals – Targets like improving data quality by X%, reducing compliance failures by Y%, or cutting data sourcing costs help drive strategy.
  • Develop a roadmap – Create a phased rollout plan focused on quick wins, which can then help expand the program for greater impact over time.

Key Tasks and Processes

After aligning on strategy, the data governance team carries out core tasks like:

  • Establishing policies – Codify end-to-end data lifecycle management policies, including acquisition, storage, maintenance, usage, retention, archival, and deletion protocols.
  • Defining standards – Create standards for data formats, definitions, business rules, taxonomies, and metadata to ensure consistency across systems.
  • Measuring and monitoring data quality – Leverage profiling tools and metrics to measure quality dimensions, then monitor if KPIs are being met.
  • Enforcing security protocols – Define security policies based on classification levels. Implement security controls like masking, encryption, access management, and auditing.
  • Managing metadata – Centralize storage and maintenance of metadata. Ensure relationships and lineage are mapped.
  • Overseeing regulatory compliance – Monitor data practices remain compliant with regulations. Facilitate audits through reporting.

Best Practices for Data Governance Success

To optimize the chances of a successful data governance program, leading practices include:

  • Start small, think big – Focus initial efforts on high-value data domains with clear pain points, but have a roadmap to expand.
  • Align metrics to business goals – Collaborate with stakeholders to identify KPIs that demonstrate reductions in risk, costs, and inefficiencies.
  • Foster a data-driven culture – Position governance as helping – not hindering – departments leverage data. Celebrate wins.
  • Maintain momentum – Treat governance as continuous, not a one-time project. Demonstrate value regularly to retain stakeholder commitment.
  • Integrate data quality – Unify governance efforts with existing data quality improvement initiatives for efficiency.
  • Leverage data catalogs – Utilize data catalogs to support metadata management, discovery, and lineage mapping requirements.
  • Automate policy enforcement – Rely on tools for lower-touch monitoring of policies so efforts can scale.
  • Secure adequate budget – Data governance requires sustained funding for technology, training, and resources. Overcome perceptions that it’s unnecessary.

Following leading practices helps data governance programs avoid common downfalls. So what pitfalls should you keep top of mind?

Common Data Governance Pitfalls to Avoid

While data governance offers tremendous upside, organizations often undermine efforts by:

  • Not obtaining ongoing executive support – Without executive champions, governance becomes underfunded and underprioritized over time.
  • Focusing on technology alone – Tools enable governance, but strategies must still be business-driven. Don’t let it become an IT project.
  • Allowing data silos – Governance must span the organization – disjointed initiatives create data inconsistencies.
  • Lacking enforcement – Rules without enforcement are recommendations. Disciplined policy execution is critical.
  • Using meaningless metrics – vanity metrics like "% errors reduced" lack context. Metrics must demonstrate business impact.
  • Assuming static compliance – Regulations evolve continuously. Governance programs must keep pace to avoid fines.
  • Neglecting changemanagement – Position governance as an enabler, not overhead. Communicate wins frequently.
  • Underinvesting in training – With limited data literacy, governance efforts flounder. User enablement is key.

By being aware of these pitfalls, you can proactively build programs to avoid them. Now let’s explore the technologies that support your governance initiatives.

Data Governance Software Solutions

Data governance relies on specialized tools to automate policy monitoring, metadata management, data mapping, issue resolution, and more. When evaluating solutions look for capabilities like:

Metadata management – Discover, catalog, and link technical, business, and operational metadata to provide context and definitions for data assets.

Data mapping – Visualize data flows and lineage across source systems, databases, transforms, models, dashboards, and reports to enable impact analysis.

Reference data – Centrally define domain value sets, taxonomies, hierarchies, data relationships, and business rules to ensure consistency.

Data quality – Assess, monitor, and improve data accuracy, completeness, conformity, duplication, integrity, and timeliness.

Data security – Apply security policies, access controls, activity logging, auditing, and alerts to harden data protection.

Collaboration – Provide tools for stewardship roles to discuss and resolve data issues across teams.

Workflow – Automate governance processes like policy attestation, training, issue escalation, and remediation for efficiency.

Leading data governance platforms include:

Collibra – End-to-end capabilities including catalog, lineage, workflow, security, and extensive integration capabilities.

Informatica – Established data management provider with integrated governance offerings spanning quality, security, and metadata.

IBM Information Governance Catalog – Metadata management with data discovery, mapping,issue resolution, and integrated AI-powered recommendations.

Alation – Unified data catalog, governance, quality, and stewardship on a modern SaaS platform.

SAS Data Governance – Combines data quality, metadata management, and data relationship visualizations for 360-degree oversight.

Erwin Data Governance – Specialized data mapping, lineage, and model impact analysis combined with a business glossary and collaboration.

When choosing tools, ensure you can easily integrate capabilities with your existing data infrastructure. Work with providers who understand your use case and can configure software specifically for the needs of your organization.

Enabling GDPR Compliance Through Data Governance

For European companies handling personal data of EU citizens, the EU’s General Data Protection Regulation (GDPR) imposes strict requirements around transparency and control of data.

Non-compliance results in fines up to 4% of global annual revenue, making robust data governance critical for GDPR adherence. Specifically, governance helps by:

  • Tracing data lineage – Understand downstream systems containing personal data and how it transforms, as required for recordkeeping.
  • Assigning data stewards – Ensure defined owners can handle data subject requests around access, edits, and deletion.
  • Enabling transparency – Provide visibility into data collection, infrastructure, policies, and controls per GDPR “right to understand.”
  • Monitoring compliance – Continuously audit that security controls, retention limits, breach protocols, and more adhere to GDPR.
  • Managing subject rights – Locate and take action on specific data to enable subject rights like data portability, erasure, and rectification.

For companies handling EU citizen data, investing in data governance pays dividends in simplified GDPR compliance, reduced risk, and avoided fines.

Key Takeaways

Here are the key points to remember about successful data governance programs:

  • Data governance manages data as an enterprise asset across its lifecycle to reduce risk and cost while enabling analytics.
  • Documenting data flows, assigning data stewards, monitoring quality, applying security controls, and enabling traceability are core governance processes.
  • Stakeholder buy-in, integrated metrics, enforced policies, continued training, and the right technology stack enable governance success.
  • Avoid common pitfalls like focusing solely on technology, allowing silos, or neglecting continuous improvement to ensure long-term viability.
  • Leading governance tools provide critical capabilities around metadata, mapping, quality, security, and collaboration to automate oversight.
  • For GDPR, governance provides the transparency and traceability regulators require, avoiding significant fines.

As data‘s role continues to grow in driving competitive advantage, investing in its governance pays dividends. By following the guidance outlined here, you can implement a successful data governance initiative tailored to your organization‘s needs. Reach out if we can help advise your efforts.

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