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 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:
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 that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it 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:
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.”
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:
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:
BARC notes that such programs always span the strategic, tactical, and operational levels in enterprises, and they must be treated as ongoing, iterative processes.
According to the Data Governance Institute, eight principles are at the center of all successful data governance and stewardship programs:
Data governance strategies must be adapted to best suit an organization’s processes, needs, and goals. Still, there are six core best practices worth following:
For more on doing data governance right, see “6 best practices for good data governance.”
Good data governance is no simple task. It requires teamwork, investment, and resources, as well as planning and monitoring. Some of the top challenges of a data governance program include:
For more on these difficulties and others, see “7 data governance mistakes to avoid.”
Data governance is an ongoing program rather than a technology solution, but there are tools with data governance features that can help support your 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:
| Data governance solution | Description and features |
| 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 is a system but there are some certifications that can help your organization gain an edge, including the following:
Each enterprise composes its data governance differently, but there are some commonalities.
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.
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:
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:
