Data governance defines roles, responsibilities, and processes for ensuring accountability for and ownership of data assets across the enterprise. Credit: Getty Images 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 it 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 it 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 (EIM). 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. Such a framework provides your organization with a holistic approach to collecting, managing, securing, and storing data. To help understand what a 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 architectureData modeling and design: Analysis, design, building, testing, and maintenanceData storage and operations: Structured physical data assets storage deployment and managementData security: Ensuring privacy, confidentiality, and appropriate accessData integration and interoperability: Acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization, and operational supportDocuments 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 dataReference and master data: Managing shared data to reduce redundancy and ensure better data quality through standardized definition and use of data valuesData warehousing and business intelligence (BI): Managing analytical data processing and enabling access to decision support data for reporting and analysisMetadata: Collecting, categorizing, maintaining, integrating, controlling, managing, and delivering metadataData quality: Defining, monitoring, maintaining data integrity, and improving data quality When establishing a strategy, each of the above facets of data collection, management, archiving, and use should be considered. The Business Application Research Center (BARC) warns it 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 across the company based on lessons learned. BARC recommends the following steps for implementation: Define goals and understand benefitsAnalyze current state and delta analysisDerive a roadmapConvince stakeholders and budget projectDevelop and plan the data governance programImplement the data governance programMonitor and control Goals of data governance The goal is to establish the methods, set of responsibilities, and processes to standardize, integrate, protect, and store corporate data. According to BARC, an organization’s key goals should be to: Minimize risksEstablish internal rules for data useImplement compliance requirementsImprove internal and external communicationIncrease the value of dataFacilitate the administration of the aboveReduce costsHelp to ensure the continued existence of the company through risk management and optimization BARC notes that such 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 governance for individual applications, business units, or functions, even if the processes and responsibilities are informal. As a practice, it 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 stemming from consistent, uniform data across the organizationClear rules for changing processes and data that help the business and IT become more agile and scalableReduced costs in other areas of data management through the provision of central control mechanismsIncreased efficiency through the ability to reuse processes and dataImproved confidence in data quality and documentation of data processesImproved 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 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.They must define who is accountable for cross-functional data-related decisions, processes, and controls.It must define who is accountable for stewardship activities that are the responsibilities of individual contributors and groups of data stewards.Programs must 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.The program must introduce and support standardization of enterprise data.Programs must support proactive and reactive change management activities for reference data values and the structure/use of master data and metadata. For more on doing data governance right, see “6 best practices for good data governance.” Data governance roles Each enterprise composes its data governance differently, but there are some commonalities. Steering committee Governance programs span the enterprise, generally starting 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 definitionsEnsuring the accuracy of information across the enterpriseDirect data quality activitiesReviewing and approving master data management approaches, outcomes, and activitiesWorking with other data owners to resolve data issuesSecond-level review for issues identified by data stewardsProviding 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 domainIdentifying data issues and working with other data stewards to resolve themActing as a member of the data steward councilProposing, discussing, and voting on data policies and committee activitiesReporting to the data owner and other stakeholders within a data domainWorking 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 PeerSpot, some of the more popular solutions include: Collibra Governance: Collibra is an enterprise-wide solution that automates many governance and stewardship tasks. It includes a policy manager, data helpdesk, data dictionary, and business glossary.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.erwin Data Intelligence (DI) for Data Governance: erwin DI combines data catalog and data literacy capabilities to provide awareness of and access to available data assets. It provides guidance on the use of those data assets and ensures data policies and best practices are followed.Informatica Axon: Informatica Axon is a collection hub and data marketplace for supporting programs. Key features include a collaborative business glossary, the ability to visualize data lineage, and generate data quality measurements based on business definitions.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.Alation: Alation is an enterprise data catalog that automatically indexes data by source. One of its key capabilities, TrustCheck, provides real-time “guardrails” to workflows. Meant specifically to support self-service analytics, TrustCheck attaches guidelines and rules to data assets.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.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. Data governance certifications Data governance is a system but there are some certifications that can help your organization gain an edge, including the following: DAMA Certified Data Management Professional (CDMP)Data Governance and Stewardship Professional (DGSP)edX Enterprise Data ManagementSAP Certified Application Associate – SAP Master Data Governance More on data analytics: 7 data governance mistakes to avoid6 best practices for good data governance12 myths of data analytics debunked The secrets of highly successful data analytics teams Developing data science skills in-house: Real-world lessons Related content News Amazon to lay off 9,000 more workers, including some at AWS The latest round of Amazon layoffs will impact AWS, Twitch, advertising and PXT, CEO Andy Jassy said. By Jon Gold Mar 20, 2023 3 mins Technology Industry Cloud Computing BrandPost What’s next for network operations Broadcom: 2023 Tech Trends That Transform IT By Serge Lucio, Vice President and General Manager, Agile Operations Division Mar 20, 2023 8 mins IT Leadership Networking BrandPost Digital transformation obstacles: Stubborn challenges, what to do about them Value Stream Management is an increasingly essential approach to strategic transformation initiatives. To help teams more fully capitalize on the opportunities it presents, Broadcom is holding its third annual VSM Summit. By Marla Schimke, Head of Product and Growth Marketing, Broadcom's Enterprise Software Division Mar 20, 2023 3 mins Devops Software Development Feature CEO directives: Top 5 initiatives for IT leaders As organizations change course with economic gyrations, collaboration between IT and business becomes priority No. 1 for CEOs. By Stacy Collett Mar 20, 2023 7 mins IT Leadership Podcasts Videos Resources Events SUBSCRIBE TO OUR NEWSLETTER From our editors straight to your inbox Get started by entering your email address below. Please enter a valid email address Subscribe