Data Management Glossary
ROT Data
ROT data is Redundant, Obsolete, and Trivial data, which includes data stored within an organization that no longer has value or relevance. This type of data can clutter systems, increase storage costs, and pose compliance or security risks. File data (see File) is where a lot of ROT lives in the enterprise, which is why file data management is a growing category of software solutions. High Performance Computing (HPC), research labs and engineering teams are a common culprit for ROT.
Types of ROT Data
Redundant Data:
- Duplicate files or records (e.g., multiple copies of the same document).
- Overlapping datasets that provide no additional insights.
Obsolete Data:
- Outdated information (e.g., old project files, expired contracts).
- Legacy system data that is no longer used or supported.
Trivial Data:
- Non-business-related content (e.g., personal files, memes, or irrelevant emails).
- Temporary files or drafts that are no longer needed.
Challenges of ROT Data
- Storing ROT data increases hardware, cloud storage, and maintenance expenses unnecessarily.
- Visibility into cold data and building a plan to tier, archive, migrate this inactive data to lower cost storage is part of an overall unstructured data management strategy.
Performance Issues:
- Excessive data can slow down system performance and make finding relevant information harder.
Compliance Risks:
- Retaining outdated or unnecessary data can result in non-compliance with regulations like GDPR or HIPAA.
- See Data Governance.
Security Risks:
- ROT data increases the attack surface for cyber threats or leaks of sensitive information.
- Learn more about ransomware data protection at 80% less cost: Cybersecurity and ransomware data protection.
Managing ROT Data
Data Audit:
- Conduct regular data audits to identify redundant, obsolete, and trivial files.
- Having visibility into your file and object data across storage silos is a start. Learn more about Komprise Analysis.
Data Classification:
- Use automated tools to classify and tag data for better visibility and management.
- Read the blog post: Why unstructured data classification matters.
Retention Policies:
- Implement policies to define the lifecycle of data, specifying when data should be archived or deleted.
Data Cleanup Tools:
- For structured and semi-structured data, use data management software or scripts to de-duplicate and remove ROT data.
User Education:
- Train employees on proper data storage and retention practices to minimize ROT data creation.
- Establish reporting strategies to ensure departmental research teamshave visibility into their data storage costs.
- Read: The Rise of Data Services for Unstructured Data Management.
Archiving Solutions:
- Archive important historical data and securely delete (or confine) the rest to free up space.
- The term data archiving is often used interchangeably with data tiering. The point is to ensure ROT data is identified and there is a plan to ensure teams are working with the right data at the right time and inactive data is moved to lower cost storage (or removed).
By actively managing ROT data, organizations can improve efficiency, reduce costs, and enhance data governance. Also see Zombie Data.