Data Management Glossary
Data Governance
What is data governance?
Data governance refers to the management of the availability, security, usability, and integrity of data used in an enterprise. Data governance in an organization typically includes a governing council, a defined set of procedures, and a plan to execute those procedures.
Data governance is not about allowing access to a few privileged users; instead, it should allow broad groups of users access with appropriate controls. Business and IT users have different needs; business users need secure access to shared data and IT needs to set policies around security and business practices. When done right, data governance allows any user access to data anytime, so the organization can run more efficiently, and users can manage their workload in a self-service manner.
3 things to consider when developing a data governance strategy:
Selecting a Data Governance Team
- Balance IT and business leaders to get a broad view of the data and service needs
- Start small – choose a small group to review existing data analytics
Data Quality Strategy
- Audit existing data to discover data types and how they are used
- Define a process for new data sources to ensure quality and availability standards are met
Data Security
- Make sure data is classified so data requiring protection for legal or regulatory reasons meets those requirements
- Implement policies that allow for different levels of access based on user privileges
Komprise is not a data governance solution but we are part of an overall governance strategy as it relates to unstructured data management. With the Deep Analytics user profile, you can provide secure data access to specific users to search and tag file and object data so that it can then be incorporated into smart data migration and data mobility use cases, including Smart Data Workflows.
Data Governance FAQs
Why is unstructured data the hardest part of enterprise data governance?
Most enterprise data governance programs were built for structured data in databases and data warehouses. They define ownership, access controls, lineage, and retention for SQL tables and BI datasets effectively. But unstructured data, which now represents 80-90% of all enterprise data according to Gartner, operates entirely outside this framework. There is no schema to enforce, no rows to audit, and no built-in lineage tracking for files, documents, images, and research archives scattered across multi-vendor NAS environments and cloud storage.
Governing unstructured data requires a fundamentally different approach. You need to know what files exist, where they live, who owns them, when they were last accessed, whether they contain sensitive content, and whether they are being managed according to policy — across billions of files simultaneously. Without a platform specifically designed for unstructured data governance, organizations are making compliance assertions about file data they cannot fully see or verify.
Komprise addresses this through the Global Metadatabase, which indexes all file and object data across the entire storage estate, and Komprise Smart Data Workflows, which can be created to detect and classify sensitive data across file and object storage. This gives governance and compliance teams the visibility and automation they need to govern unstructured data with the same confidence they apply to structured databases.
See also: Unstructured Data Governance
How does data governance for unstructured data support AI compliance?
As enterprises deploy AI programs that rely on unstructured data for training, RAG pipelines, and inferencing, data governance becomes a direct prerequisite for AI compliance rather than a separate IT function. Gartner predicts that by 2028, 50% of organizations will implement a zero-trust posture for data governance due to the proliferation of unverified AI-generated data. This means every dataset entering an AI pipeline must be verified, classified, and authorized before use.
Komprise enforces AI data governance at the point where data enters a workflow rather than after the fact. Komprise Smart Data Workflows can be created to detect and classify sensitive data including PII and content matching regex-based classification patterns before data reaches any AI model or agent. The Global Metadatabase maintains an auditable record of what data was accessed, when, under which policy, and whether it was authorized for AI use. For organizations subject to HIPAA, GDPR, SOX, or FINRA requirements, this means AI programs can proceed with confidence that governed data never enters an unauthorized pipeline.
See also: AI Data Governance
How does Komprise support data governance for regulated industries?
Regulated industries face specific governance requirements that go beyond general data management best practice. Healthcare organizations must comply with HIPAA requirements covering data access, retention, and the governance of protected health information. Financial services firms face FINRA, SEC, and SOX requirements covering record retention, audit trails, and data access controls. Life sciences organizations must demonstrate chain of custody for research data and clinical records. Legal services firms must govern privileged communications and matter files with strict access controls.
Komprise supports regulated industry governance through several connected capabilities. Komprise scans and indexes all file and object data across the storage estate, building a complete, continuously updated picture of what data exists and where it lives. Komprise Deep Analytics searches this index using metadata and custom tag criteria to identify data matching specific governance criteria, such as files classified as containing regulated content, files stored in the wrong location, or files exceeding defined retention periods. KAPPA data services can extract domain-specific metadata from file content, such as clinical parameters from medical imaging files or project codes from research archives, enriching the Global Metadatabase with the business context compliance teams need. All data management actions and workflow executions are tracked in the Global Metadatabase, providing the auditable trail that regulators and legal teams require.
Read the solution brief: Komprise for Compliance
What is the role of self-service data access in a governed data governance framework?
Data governance that requires IT involvement for every data access request becomes a bottleneck rather than an enabler. Business users, researchers, and departmental teams increasingly need to find, tag, and work with their own data without opening IT tickets for each request. At the same time, IT and governance teams need to ensure that self-service access does not compromise data security or expose sensitive content.
Komprise addresses this with a governed self-service model. A Komprise administrator can create Deep Analytics user profiles that give authorized line-of-business users read-only access to search and query only the directories and shares they are permitted to access. These users can discover data, run queries using metadata and tag criteria, use the Directory Explorer to navigate directly to known locations, and apply tags to prepare data for downstream workflows. They cannot move data. Data movement and mobility actions remain under IT administrator control and are executed through policy-based data management and Komprise Smart Data Workflows.
This model separates data discovery, which business users can do independently, from data mobility, which IT governs centrally. It scales self-service access to hundreds of users across departments while maintaining a clear governance boundary that compliance and security teams can audit and enforce.
How does data governance connect to data lifecycle management for unstructured data?
Data governance defines the rules. Data lifecycle management enforces them continuously over time. Without lifecycle management, governance policies become aspirational statements rather than operational controls. A policy that says sensitive data should not be retained beyond a defined period means nothing if there is no automated mechanism to identify data that has exceeded that period and act on it.
Komprise connects governance and lifecycle management through the same platform. Governance criteria defined through classification, tagging, and policy rules in Komprise are stored as metadata in the Global Metadatabase. Lifecycle policies that act on those criteria run continuously on a schedule, identifying data that matches governance thresholds and executing the appropriate action: moving data to a compliant storage location, applying retention tags, restricting access, or routing data through Komprise Smart Data Workflows for sensitive data detection and classification. The Global Metadatabase maintains a complete audit trail of every action taken, giving governance and compliance teams the documentation they need to demonstrate that policies are being enforced rather than simply defined.
This connection between governance policy definition and automated lifecycle enforcement is what distinguishes active unstructured data governance from a passive data catalog exercise.
See also: Data Lifecycle Management
See also: Data Management Policy