Policy-based data management is data management based on metrics such as data growth rates, data locations and file types, which data users regularly access and which they do not, which data has protection or not, and more.
The trend to place strict policies on the preservation and dissemination of data has been escalating in recent years. This allows rules to be defined for each property required for preservation and dissemination that ensure compliance over time. For instance, to ensure accurate, reliable, and authentic data, a policy-based data management system should generate a list of rules to be enforced, define the storage locations, storage procedures that generate archival information packages, and manage replication.
Policy-based data management is becoming critical as the amount of data continues to grow while IT budgets remain flat. By automating movement of data to cheaper storage such as the cloud or private object storage, IT organizations can rein in data sprawl and cut costs.
Other things to consider are how to secure data from loss and degradation by assigning an owner to each file, defining access controls, verifying the number of replicas to ensure integrity of the data, as well as tracking the chain of custody. In addition, rules help to ensure compliance with legal obligations, ethical responsibilities, generating reports, tracking staff expertise, and tracking management approval and enforcement of the rules.
As data footprint grows, managing billions and billions of files manually becomes untenable. Using analytics to define governing policies for when data should move, to where and having data management solutions that automate based on these policies becomes critical. Policy-based data management systems rely on consensus. Validation of these policies is typically done through automatic execution – these should be periodically evaluated to ensure continued integrity of your data.