DATA RETENTION MANAGEMENT

Data Retention Management

Data Retention Management (DRM) from Mycelium Software enables organizations to save maintenance costs by providing a mechanism to reduce the size of their production databases. The purge feature within the DRM tool permanently removes unwanted records, thereby improving the performance of production systems.

Automated control of the growth of database tables, maintenance of high system performance, and mitigation of compliance risks, are the key benefits of leveraging automated Data Retention Management.

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Improved Maintenance of Production Databases

End-user performance depends heavily on the state of the data stored in the databases which support production environments. Production databases with a smaller footprint promote improved system performance and enhance resource productivity.

Data Retention Management provides exhaustive features to consistently purge, archive, or remove unnecessary data from the system. This results in a production environment which is easier to maintain and one which delivers an overall improved performance.

Secure Storage of Data

Data Retention Management lets organizations reduce the size of production databases. Purging of unnecessary data or relocation of less-frequently used data to non-production environments, are the two mechanisms to achieve the said task.

Transactions that include sensitive information (e.g. business or personal) can be purged depending on the scope of the transaction.

Reduce Risk and Ensure Compliance

Purging, archiving, and removing unnecessary data improve the production performance and minimize the costs involved with the maintenance of production environments.

Data Retention rules contain all the logic needed to remove data in a manner that is consistent with your operational requirements. These rules provide configurable components that ensure that your organization is in compliance with the necessary regulatory, legal and organizational requirements.

Continual Monitoring and Health Checks

Prior to performing the purge process in the production environment, Data Retention Management conducts a series of pre-purge activities which execute reconciliation scripts and generate validation reports. These reports capture the baseline figures on the status of the database, before any purge scripts are initiated.

A comprehensive reconciliation and recalculation of any financial related data is performed to ensure that pre-purge and post-purge balances are in agreement. Detailed dashboards packed with insightful metrics from the Data Retention Management tool, help to keep a constant check on the status of the production environments.

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Enhance the quality of data in production environments and attain high system performance with Data Retention Management.

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Benefits of Data Retention Management

High Performance

Data Retention Management can be used at any step of the product lifecycle, as it provides excellent throughput in selecting, purging, and archiving the qualified data.

DRM is suited for usage during the implementation phase and it may also be used to realize incremental improvements from periodic purge maintenance.

User Friendly & Intuitive Design

Data Retention Rules are configurable components that help to define filters which are in accordance with the retention policy. DRM rules are configurable, standard, consistent, repeatable, and extensible. The intuitively designed web client helps to generate these Data Retention Rules.

Functional (or business) users can come up with Data Retention Rules since these rules are not dependent on input from technical resources. Hence, a diverse set of team members can participate in the creation of rules for data retention.

Avoid Data Saturation

Data saturation could dampen the performance and quality of the production environments. Data Retention Management lets you reduce the size of production environments which helps to lower the cost of data maintenance.

Archive purged records in various formats

DRM does not rely on data partitioning. This tool uses row-level provisioning rules to execute the purge process. It can relocate the purged data in its native format, or the relocated records can undergo ETL, to accommodate aggregation in a data warehouse.