What is data governance? A best practices framework for managing data assets

Data governance definition

Data governance is a system for defining who within an organization has authority and control over data assets and how those data assets may be used. It encompasses the people, processes, and technologies required to manage and protect data assets.

The Data Governance Institute defines data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”

The Data Management Association (DAMA) International defines data governance as the “planning, oversight, and control over management of data and the use of data and data-related sources.”

Data governance vs. data management

Data governance is just one part of the overall discipline of data management, though an important one. Whereas data governance is about the roles, responsibilities, and processes for ensuring accountability for and ownership of data assets, DAMA defines data management as “an overarching term that describes the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data.

While data management has become a common term for the discipline, it is sometimes referred to as data resource management or enterprise information management. Gartner describes EIM as “an integrative discipline for structuring, describing, and governing information assets across organizational and technical boundaries to improve efficiency, promote transparency, and enable business insight.”

Data governance framework

Data governance may best be thought of as a function that supports an organization’s overarching data management strategy. A data governance framework provides your organization with a holistic approach to collecting, managing, securing, and storing data. To help understand what a data governance framework should cover, DAMA envisions data management as a wheel, with data governance as the hub from which the following 10 data management knowledge areas radiate:

Data architecture:The overall structure of data and data-related resources as an integral part of the enterprise architecture
Data modeling and design:Analysis, design, building, testing, and maintenance
Data storage and operations:Structured physical data assets storage deployment and management
Data security:Ensuring privacy, confidentiality, and appropriate access
Data integration and interoperability:Acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization, and operational support
Documents and content:Storing, protecting, indexing, and enabling access to data found in unstructured sources and making this data available for integration and interoperability with structured data
Reference and master data:Managing shared data to reduce redundancy and ensure better data quality through standardized definition and use of data values
Data warehousing and business intelligence (BI):Managing analytical data processing and enabling access to decision support data for reporting and analysis
Metadata:Collecting, categorizing, maintaining, integrating, controlling, managing, and delivering metadata
Data quality:Defining, monitoring, maintaining data integrity, and improving data quality

When establishing a strategy for data governance, each of the above facets of data collection, management, archiving and use should be considered.

BARC warns that data governance is not a “big bang initiative.” As a highly complex, ongoing program, data governance runs the risk of participants losing trust and interest over time. To counter that, BARC recommends starting with a manageable or application-specific prototype project and then expanding data governance across the company based on lessons learned.

BARC recommends the following steps for implementing data governance:

Define goals and understand benefits
Analyze current state and delta analysis
Derive a roadmap
Convince stakeholders and budget project
Develop and plan the data governance program
Implement the data governance program
Monitor and control

Goals of data governance

The goal of data governance is to establish the methods, set of responsibilities, and processes to standardize, integrate, protect, and store corporate data. According to the Business Application Research Center (BARC), an organization’s key goals should be to:

Minimize risks
Establish internal rules for data use
Implement compliance requirements
Improve internal and external communication
Increase the value of data
Facilitate the administration of the above
Reduce costs
Help to ensure the continued existence of the company through risk management and optimization

BARC notes that data governance programs always span the strategic, tactical, and operational levels in enterprises, and they must be treated as ongoing, iterative processes.

Benefits of data governance

Most companies already have some form of data governance for individual applications, business units, or functions, even if the processes and responsibilities are informal. As a practice, data governance is about establishing systematic, formal control over these processes and responsibilities.

Doing so can help companies remain responsive, especially as they grow to a size in which it is no longer efficient for individuals to perform cross-functional tasks. Several of the overall benefits of data management can only be realized after the enterprise has established systematic data governance. Some of these benefits include:

Better, more comprehensive decision support as a result of consistent, uniform data across the organization
Clear rules for changing processes and data that help the business and IT become more agile and scalable
Reduced costs in other areas of data management through the provision of central control mechanisms
Increased efficiency through the ability to reuse processes and data
Improved confidence in data quality and documentation of data processes
Improved compliance with data regulations

Data governance principles

According to the Data Governance Institute, eight principles are at the center of all successful data governance and stewardship programs:

All data governance participants must have integrity in their dealings with each other. They must be truthful and forthcoming in discussing the drivers, constraints, options, and impacts for data-related decisions.
Data governance and stewardship processes require transparency. It must be clear to all participants and auditors how and when data-related decisions and controls were introduced into the processes.
Data-related decisions, processes, and controls subject to data governance must be auditable. They must be accompanied by documentation to support compliance-based and operational auditing requirements.
Data governance must define who is accountable for cross-functional data-related decisions, processes, and controls.
Data governance must define who is accountable for stewardship activities that are the responsibilities of individual contributors and groups of data stewards.
Checks-and-balances. Data governance will define accountabilities in a manner that introduces checks-and-balances between business and technology teams, and between those who create/collect information, those who manage it, those who use it, and those who introduce standards and compliance requirements.
Data governance will introduce and support standardization of enterprise data.
Change management. Data governance will support proactive and reactive change management activities for reference data values and the structure/use of master data and metadata.

Data governance roles

Each enterprise composes its data governance differently, but there are some commonalities.

Steering committee

Because data governance spans the enterprise, it generally starts with a steering committee comprising senior management, often C-level individuals or vice presidents accountable for lines of business. Morgan Templar, author of Get Governed: Building World Class Data Governance Programs, says steering committee members’ responsibilities include setting the overall governance strategy with specific outcomes, championing the work of data stewards, and holding the governance organization accountable to timelines and outcomes.

Data owner

Templar says data owners are individuals responsible for ensuring that information within a specific data domain is governed across systems and lines of business. They are generally members of the steering committee, though may not be voting members. Data owners are responsible for:

Approving data glossaries and other data definitions
Ensuring the accuracy of information across the enterprise
Direct data quality activities
Reviewing and approving master data management approaches, outcomes, and activities
Working with other data owners to resolve data issues
Second-level review for issues identified by data stewards
Providing the steering committee with input on software solutions, policies, or regulatory requirements of their data domain

Data steward

Data stewards are accountable for the day-to-day management of data. They are subject matter experts (SMEs) who understand and communicate the meaning and use of information, Templar says, and they work with other data stewards across the organization as the governing body for most data decisions. Data stewards are responsible for:

Being SMEs for their data domain
Identifying data issues and working with other data stewards to resolve them
Acting as a member of the data steward council
Proposing, discussing, and voting on data policies and committee activities
Reporting to the data owner and other stakeholders within a data domain
Working cross-functionally across lines of business to ensure their domain’s data is managed and understood

Data governance tools

Data governance is an ongoing program rather than a technology solution, but there are tools that can help support that program. The tool that suits your enterprise will depend on your needs, data volume, and budget. According to IT Central Station, some of the more popular data governance solutions include:

SAS Data Management:Built on the SAS platform, SAS Data Management provides a role-based GUI for managing processes and includes an integrated business glossary, SAS and third-party metadata management, and lineage visualization.
Collibra Governance:Collibra is an enterprise-wide data governance solution that automates many governance and stewardship tasks. It includes a policy manager, data helpdesk, data dictionary, and business glossary.
Informatica Axon:Informatica Axon is a collection hub and data marketplace for supporting data governance programs. Key features include a collaborative business glossary, the ability to visualize data lineage, and generate data quality measurements based on business definitions.
Varonis Data Governance Suite:Varonis’s solution automates data protection and management tasks leveraging a scalable Metadata Framework that enables organizations to manage data access, view audit trails of every file and email event, identify data ownership across different business units, and find and classify sensitive data and documents.
Alation:Alation is an enterprise data catalog that automatically indexes data by source. One of its key capabilities, TrustCheck, provides real-time data governance “guardrails” to workflows. Meant specifically to support self-service analytics, TrustCheck attaches guidelines and rules to data assets.
SAP Data Hub:SAP Data Hub is a data orchestration solution intended to help you discover, refine, enrich, and govern all types, varieties, and volumes of data across your data landscape. It helps organizations to establish security settings and identity control policies for users, groups, and roles, and to streamline best practices and processes for policy management and security logging.
IBM Data Governance:IBM Data Governance leverages machine learning to collect and curate data assets. The integrated data catalog helps enterprises find, curate, analyze, prepare, and share data.
Unifi Data Platform:The Unifi Data Platform is a suite of self-service data discovery and preparation tools. It enables automatic data profiling and tagging, semantic search, and Natural Language Query. Knowledge Graph provides insight into the relationships between data and sources.