Data Governance Definition
Data governance is the implementation of policies and procedures to ensure data availability, integrity, data security, and usability. Enterprise data governance breaks down big data silos across an organization to ensure consistency across departments for data compatibility, accuracy in business intelligence, reporting, and analytics. The main function of a data governance model in an organization is to ensure that the data being used is high quality, trustworthy, used appropriately, and accessed by the right people.
Data Governance FAQs
What is Data Governance?
What does data governance mean? Data governance, one of the components of a data management strategy, is the process of creating and implementing a set of mutually agreed-upon rules and standards by which all data across an organization is controlled. Data governance policy dictates the manner in which data is collected, stored, processed, and disposed of; who can access which kinds of data; what kinds of data are under governance; and how to comply with external standards set by industry associations, government agencies, and other stakeholders.
What is a data governance framework? A data governance framework consists of the policies, rules, processes, roles and responsibilities delegations, organizational structures, tools, and technologies that are put in place as part of a data governance roadmap to ensure privacy and compliance in an organization’s data management plan. An effective data governance plan depends on a few key principles that call for organizational buy-in from top to bottom. The data governance principles include Transparency, Accountability, Integrity, and Collaboration:
- Transparency: Build trust with employees and customers by being clear about what data is being collected, its purpose, where it’s stored, and who has access.
- Accountability: Clearly define the different kinds of data, the responsibilities for data, and who is responsible for the data. Assign data stewardship, decision-maker, and data manager roles.
- Integrity: Ensure data is accurate, relevant, timely, and compliant with policies and regulations.
- Collaboration: Developing a data governance strategy should be a cooperative practice. Develop best practices by gaining insight from discussions with different parts of the organization. Document and share the data governance framework internally so it’s clear to all involved how it will work upfront.
How Does Data Governance Work?
Data Governance Teams
The first step in building a data governance team is appointing a Chief Data Officer (CDO), or dedicated data governance manager, who is responsible for leading the development of the program’s structure, securing approval and funding for the program, and monitoring progress; as well as staffing the data governance team, identifying data stewards, and formalizing the governance committee.
The governance committee typically consists of data owners and other executives who create and dictate implementation of policy and standards decisions related to data access governance (DAG) and usage. Data stewards oversee datasets assigned to them and enforce the aforementioned policies and standards with end users. Data quality analysts, engineers, modelers, and architects, as well as business users and analytics teams are also involved. Once the hierarchy of participants has been established, and data literacy is made a top priority for all involved, determine how the success of data governance models will be measured. Then the actual process of governing data can begin.
How to Implement Data Governance
Once an organization has established ownership of its different datasets and a structure is in place, the teams can start collaborating on governance processes, data governance standards, rules for compliance with internal and external regulations, consistent data usage controls across applications, data source and storage documentation, and strategies for protecting data from attacks and misuse.
Some other data governance examples include:
- Data Mapping: Mapping out and classifying the different data groups in an organization will help create a better understanding of what kind of data is being controlled, and data flows. This is important to know because different types of data require different governing policies.
- Data Cataloging: A data catalog utilizes metadata management to create an organized inventory of available datasets. Data governance and security rules can be programmed into catalogs and automatically applied to the indexing process.
- Technical Glossary: Establishing a common vocabulary for different business and technical concepts will drive more effective governance strategies.
- Data Cleansing: A core component of effectively governing data is data quality. Data accuracy, completeness, and consistency can be improved with data cleansing, which identifies and fixes duplicates, errors, and inconsistencies.
- Master Data Management (MDM): MDM creates a master set of data related to products, services, customers, and competitors in order to establish consistency across different systems within the organization.
Data Governance Best Practices
While each organization uses different data and will have different governance needs, there are a few best practices that can improve any governance initiative:
- Communication: Governance is a collaborative process. Frequent and consistent communication from Day 1 is essential to ensuring that the right people know what they’re supposed to be doing, showing stakeholders the impact of the program, and encouraging broad participation. Internal agreements on data accountability and decision rights should be agreed upon.
- Metrics: Set clear goals that are specific and measurable. You cannot measure the results of a data governance program without metrics. Establish a baseline, and collect measurements frequently and consistently.
- Education: Continuous education and data governance training for stakeholders and team members will improve data literacy and familiarity with business terminology, which will in turn improve the effectiveness of governance initiatives.
- Security: Prioritize data security governance and risk management as core values. Data protection and data privacy must be built into data policies and standards. Have full oversight of where data is stored, who is accessing the data, and for what purpose.
- Trust-Based Model: Document the lineage and curation of the data assets and the access controls related to the data.
Data Access Governance
Access and security are core components of all data governance models. Between massive databases, shared network drives, collaboration systems, and applications, modern businesses have more entry points than ever to protect from increasingly sophisticated cyberattacks. Strong data access governance across an organization’s IT landscape is crucial to protecting data from breaches. While application data can be protected through role-based access control, managing access to unstructured data, such as documents and spreadsheets, is more challenging.
What is data access governance? A strong data access governance strategy is a type of information security that involves: clearly defining the organization’s permissions management process, discovering and classifying sensitive on-premise and cloud data, identifying stale files that need purging or archiving, limiting each user’s access to only the data that their job requires, monitoring daily user activity around sensitive or business-critical files, and setting up automated alerts that indicate when user access rights to sensitive files have been changed or someone alters sensitive data.
Modern data governance tools for data security and access controls provide centralized, secure access management and real-time, continuous monitoring for structured and semi-structured data. This enables governance teams to quickly and easily audit who has access to which data and how data is classified, prevent data compromise and protect customer data, and meet regulatory requirements with strong yet simple privacy controls.
Data Governance vs Data Management
Data management consists of the disciplines related to ingesting, organizing, and maintaining data, and is concerned with securing and enhancing the value of data collected by an organization. Governing data is the establishment and oversight of strategies and procedures related to how data is accessed, and is concerned with managing the integrity, security, and usability of the data.
While governance puts all of the policies and procedures in place, data management is the technical implementation of all these pieces. Data management enables the execution of the policies that data governance creates. If governance of data is the blueprint, data management is the construction. Governing data is just one activity in the data management process, which consists of elements such as ETL, data preparation, pipelines, catalogs, warehouses, architecture, and security.
Why is Data Governance Important?
So, why data governance? Most enterprises have data silos containing inconsistent data that is inaccessible to users throughout the organization. Aligning data collection, creation, classification, formatting, and usage across the entire organization is a highly challenging task. Big data governance addresses these challenges by giving businesses a clearly defined process for classifying, managing, and controlling data and how it is used in the organization. Putting governance procedures in place for enforcing internal data standards and policies helps ensure that data is accurate, consistent, and used appropriately.
The benefits of data governance include: more accurate analytics; stronger regulatory compliance; improved data quality and accuracy; decreased data management costs; better data access for data scientists, analysts, and business users; better business decision-making; competitive advantages; maximized revenue and profits; improved business planning; overall great efficiency; and an enhanced business reputation.
Does Cyral Offer a Data Governance Solution?
Cyral’s data access governance solutions provide centralized, secure access management and real-time monitoring for structured and semi-structured data that is found in SQL and NoSQL databases and data lakes. Cyral’s innovative, stateless interception technology enables real-time logs, data governance metrics, and traces for all data activity without impact to performance and scalability, providing unprecedented visibility of who is accessing which data and when.
Cyral’s data governance software can be deployed in front of existing data sources without any user workflow changes or data modeling overhead. Fine-grained access control with your existing IdP makes it easy to identify users behind shared service accounts and enable policies by user identities and SSO groups. Cyral’s cloud data governance lets you easily provision and manage access to cloud object storage using your existing identity provider.
Improve your data mesh governance, data access governance and data security governance with Cyral’s data governance platform today. Find out more about Cyral’s Data Access Governance solution.