Data Management Glossary
Data Management Policy
What is a Data Management Policy?
A data management policy addresses the operating policy that focuses on the management and governance of data assets, and is a cornerstone of governing enterprise data assets. This policy should be managed by a team within the organization that identifies how the policy is accessed and used, who enforces the data management policy, and how it is communicated to employees.
It is recommended that an effective data management policy team include top executives to lead in order for governance and accountability to be enforced. In many organizations, the Chief Information Officer (CIO) and other senior management can demonstrate their understanding of the importance of data management by either authoring or supporting directives that will be used to govern and enforce data standards.
Considerations for a data management policy
- Enterprise data is not owned by any individual or business unit, but is owned by the enterprise
- Enterprise data must be safe
- Enterprise data must be accessible to individuals within the organization
- Metadata should be developed and utilized for all structured and unstructured data
- Data owners should be accountable for enterprise data
- Users should not have to worry about where data lives
- Data should be accessible to users no matter where it resides
Ultimately, a data management policy should guide your organization’s philosophy toward managing data as a valued enterprise asset. With automation, IT can “set and forget” the policy to ensure continuous adherence to policies. For instance, a policy could dictate that all data over one year of age is tiered to cold storage, that research data from a department is moved to secondary storage upon completion of the project, or that all ex-employee data is deleted 30 days after the employee’s last day.
Watch the video: Intelligent Data Management: Policy-Based Automation
Developing an unstructured data management policy
It is important to develop enterprise-wide data management policies using a flexible governance framework that can adapt to unique business scenarios and requirements. Identify the right technologies following a proof of concept approach that supports specific risk management and compliance use cases. Tool proliferation is always a problem so look to consolidate and set standards that address end-to-end scenarios. Unstructured data management policies must address data storage, data migration, data tiering, data replication, data archiving and data lifecycle management of unstructured data (block, file, and object data stores) in addition to the semi-structured and structured data lakes, data warehouses and other so-called big-data repositories.
Read the VentureBeat article: How to create data management policies for unstructured data.
Data Management Policy FAQs:
Why is a policy-based approach to unstructured data management no longer optional for enterprise IT?
Unstructured data now represents 80-90% of all enterprise data according to Gartner, and it is growing three times faster than structured data at annual rates of 55-65%. By the time most organizations recognize the scale of their unstructured data problem, manual approaches to managing it are already unworkable. No IT team can manually classify, tier, migrate, or govern billions of files spread across multi-vendor NAS and cloud storage environments.
A policy-based approach replaces manual, reactive data management with automated, continuous enforcement. Instead of individual decisions about where specific files belong, policies define the rules once and Komprise applies them at scale across the entire data estate. A policy that says all files not accessed in 12 months are tiered to lower-cost object storage runs continuously without IT intervention. A policy that removes ex-employee data 30 days after their last day runs automatically without a manual audit. A policy that identifies and routes sensitive data for compliance review fires every time new data matching the criteria arrives. At petabyte scale and with data volumes tripling between 2023 and 2026, policy-based automation is the only viable way to keep unstructured data managed, governed, and cost-controlled.
Why is unstructured data the primary fuel for enterprise AI, and what does that mean for data management policy?
Industry analysts have confirmed that most generative AI use cases require organizations to extract, qualify, and govern large volumes of unstructured data including documents, emails, and multimedia content. This is not a future trend, it is the current reality for enterprises deploying RAG pipelines, AI agents, and multimodal models today. Unstructured data contains the institutional knowledge, clinical context, research history, and operational intelligence that AI systems need to generate accurate, relevant, and trustworthy outputs.
The consequence for data management policy is significant. A data management policy that only governs structured databases and warehouses is already governing a minority of enterprise data and a fraction of AI-relevant content. Any organization building an AI strategy without an unstructured data management policy to match it is building on an ungoverned foundation.
Gartner forecasts that by 2027, IT spending on multistructured data management will represent 40% of total IT spending on data management technologies and services, and that the share of AI spending dedicated to data readiness will grow sevenfold between 2025 and 2029. Organizations that build policy-based unstructured data management capabilities now are positioning themselves to execute AI initiatives faster, at lower cost, and with higher quality outputs than those that treat unstructured data governance as an afterthought.
How does Komprise implement data management policies for unstructured data in practice?
Komprise Intelligent Data Management delivers policy-based automation across the full unstructured data lifecycle through four connected capabilities.
First, Komprise Analysis and Deep Analytics provide the foundation. Before any policy can be effective, IT needs to know what data exists, where it lives, who owns it, how old it is, and how it is being accessed. Deep Analytics searches the Global Metadatabase using standard metadata and custom tags to find precisely the data that policies should act on, with exclusion filters to handle edge cases.
Second, data mobility policies handle the core lifecycle actions. Policies can be based on data attributes such as last accessed time, file type, age, owner, or size, or on the results of a Deep Analytics query. Komprise can tier data using Transparent Move Technology, which stores data in native format with no rehydration penalty. It can migrate data using the Komprise engine proven at up to 27x faster than standard transfer methods. It can copy data to a target destination or confine data to an approved location based on policy triggers.
Third, Komprise Smart Data Workflows extend policy into AI and compliance use cases. Workflows can automatically detect and exclude sensitive data such as PII before it enters an AI pipeline, route approved datasets to AI platforms on a schedule, or apply retention tags that feed downstream governance actions.
Fourth, all policy actions and metadata activity are tracked in the Global Metadatabase, providing a continuously updated, auditable record of what data exists, where it has been, and what policies have acted on it. This makes policy enforcement both automatic and provable for regulatory requirements.
What is a Data Management Policy?
A data management policy addresses the operating policy that focuses on the management and governance of data assets. The data management policy should contain all the guidelines and information necessary for governing enterprise data assets and should address the management of structured, semi-structured and unstructured data.
What does a Data Management Policy contain?
A comprehensive Data Management Policy should contain the following:
- An inventory of the organization’s data assets
- A strategy of effective management of the organization’s data assets
- An appropriate level of security and protection for the data including details of which roles can access with data elements
- Categorization of the different sensitivity and confidentiality levels of the data
- The objectives for measuring expectations and success
- Details of the laws and regulations that must be adhered to regarding the data program