Data governance
Understand and govern all enterprise data to mitigate risk and accelerate insights
Understand and govern all enterprise data to mitigate risk and accelerate insights
Data governance consists of policies, processes and an organizational structure to support enterprise data management. The structure of a data governance program provides understanding, security and trust around an organization’s data among its stakeholders, especially as companies scale and accumulate more data sources and assets. With the exponential accrual of new data, companies need to determine the appropriate big data environments for storage and access purposes, such as data lakes, and they need to design a data architecture to govern those sources and integrate and make them available across the organization. This data integration becomes increasingly important as it impacts the workflows and decision-making of various teams.
Data governance is essential to an organization’s overall strategy for data management and as part of a complete DataOps practice. It helps you to know what data you have, where that data resides and how that data can be used. Data governance lays the foundation for business-ready data through the adherence to defined rules and processes to accelerate analytics and growth initiatives.
A data governance platform with an integrated data catalog can help your organization find, curate, analyze, prepare and share data to support your AI initiatives. IBM data governance solutions help to ensure that the data pipeline is ready to help catalog, protect and govern sensitive data and to trace data lineage.
Achieve data governance goals with solutions to meet a variety of data management needs.
Use machine learning to curate metadata, manage assets and share knowledge.
Use clean, current information to drive big data projects and applications.
Assess the value and risk of personal data. Help secure PII, PCI and PHI.
Activate business-ready data for AI and analytics with a data catalog that’s backed by active metadata and policy management. Help your colleagues find data to curate, categorize, govern, analyze and use.
A few roles are key to the practice of data governance. Three roles ensure that standards are created and maintained over time, aiding in data compliance, security, data quality and automation goals.
Chief data officer
Executive sponsors, such as chief data officers, signal the importance of a data governance program to the organization through its prioritization. These individuals are instrumental in the development of a cross-functional council, which usually sources members from various business units to represent the needs and concerns of different disciplines or product portfolios. This committee serves as a forum to communicate new data governance initiatives and assign responsibilities to achieve agreed upon timelines and outcomes.
Data owners
These individuals are responsible for the state of the data. They are usually designated by the type of data that they manage, such as customer or financial data, and their role seeks to maintain data accuracy and usability. Common tasks include troubleshooting data issues, approving data definitions, and providing data recommendations, particularly as it relates to any regulatory requirements.
Data stewards
These individuals are subject matter experts (SMEs) around their data domain, influencing data policies and championing data governance across the organization. Since they can communicate the importance of specific data points for business processes or decisions, they can also impact the structures of database tables to ensure that the right data is surfaced for reporting purposes. Overall, though, data stewardship helps keep stakeholders accountable for their role in maintaining data quality.
Data governance practices have increased in adoption over the years, especially with the growth in digital transformation projects. For data governance initiatives to achieve successful outcomes, they should include a number of components, such as:
Data standards
Data dictionaries, taxonomies and business glossaries should be developed to provide clarity around business and data definitions. This documentation reduces confusion in conversations, particularly ones involving metrics and reporting. It also gives stakeholders visibility into the data architecture, enabling teams to innovate on their own to automate processes for their discipline.
Data processes and organizational structure
Data governance processes provide transparency to end users around how data is processed within an organization. This can be inclusive of data refresh cadences, PII restrictions, regulatory data policies or even something as simple as data access. This type of documentation also supports organizational structure by clarifying the responsibilities of different roles as it pertains to the management and maintenance of data.
Technology
Different data governance tools, such as metadata management platforms, support the processes and standards around data. These tools can store and secure information about the data that an organization manages. This can include documentation on business definitions, data logs, data owners, database information (such as database and tables names, server locations, data types, etc.). It can also feed into self-service data analytics tools, allowing analysts to query and visualize different data sets for reporting or innovation projects.
The IBM solution empowers us to keep our clients’ personal data safe, protecting the company’s reputation and preserving our customers’ trust.
Han van der Vinden
Test Manager
CZ
A flexible multicloud data platform that integrates your data, whether on premises or on cloud, and helps to keep it more secure at its source.
A highly scalable data integration tool used to design, develop and run jobs that move and transform data, deployable on premises and on any cloud.
Software that delivers self-service access to data. Begin data analysis more quickly with automated transformation.