Remove unneeded or redundant data

Removing unneeded or redundant data is crucial to minimizing the storage resources required to store your datasets. This practice not only helps in reducing overall cost but also plays a significant role in achieving sustainability by lowering the energy consumption associated with storing unneeded data.

Best Practices

Implement Regular Data Audits

• Conduct data audits regularly to identify and assess unneeded or redundant data. This is important as it helps maintain an efficient data environment and supports sustainability by reducing storage footprints.
• Establish criteria for data relevance and retention based on business and compliance requirements. Implement tools that automate data discovery and classification.
• Use data analytics to understand data usage patterns and identify under-utilized datasets that may be candidates for removal or archiving.

Adopt Lifecycle Policies for Data Management

• Create lifecycle management policies to automate the transition of data between different storage classes based on its age and usage frequency. This ensures efficient resource allocation and minimizes the environmental impact.
• Implement strategies to automatically move less frequently accessed data to lower-cost, lower-performance storage solutions, or archive it when it is no longer needed.
• Create deletion schedules for data that has surpassed its retention period to ensure timely removal of obsolete information.

Utilize Data Deduplication Techniques

• Implement data deduplication methods to eliminate redundant copies of data, thereby Streamlining storage requirements. This practice enhances efficiency and supports sustainability initiatives by minimizing resource use.
• Leverage cloud storage solutions that provide built-in deduplication capabilities to automatically handle duplicate data.
• Regularly review and refine deduplication processes to ensure effectiveness and identify any new redundant data that may arise.

Educate Teams on Data Management Best Practices

• Train teams on the importance of proper data management practices, highlighting the impacts of data retention on storage costs and sustainability.
• Foster a culture of accountability where data stewardship is prioritized, encouraging team members to regularly assess the necessity of data they manage.
• Provide resources and tools that support teams in efficiently managing their data lifecycles, and facilitate regular check-ins to discuss data usage and management challenges.

Questions to ask your team

• What processes do you have in place to regularly identify and remove unneeded or redundant data?
• How do you track the lifecycle of your data to determine when it can be archived or deleted?
• Are there automated tools implemented to assist in the identification and deletion of obsolete data?
• How often do you review your data storage to ensure it aligns with your current business needs?
• What policies do you have to manage data retention and deletion in compliance with relevant regulations?
• Do you have cross-functional teams involved in data management to ensure all aspects of data use are considered?

Who should be doing this?

Data Steward

• Assess and identify unneeded or redundant data in the storage systems.
• Establish and enforce data retention policies to guide data lifecycle management.
• Work with cross-functional teams to ensure understanding of data usage and value.
• Regularly review data management practices to align with sustainability goals.

Data Engineer

• Implement data management and storage solutions that minimize resource usage.
• Develop automated processes for data archival and deletion.
• Configure storage technologies for optimal efficiency based on data usage.
• Monitor data storage metrics to identify opportunities for optimization.

Business Analyst

• Analyze data requirements in relation to business value and sustainability goals.
• Collaborate with stakeholders to determine data relevance and retention needs.
• Identify potential redundancies in datasets and recommend removal strategies.
• Report on the impact of data management policies on sustainability metrics.

IT Operations Manager

• Oversee the implementation of data management policies across the organization.
• Ensure that data storage solutions align with sustainability objectives.
• Coordinate with the Data Steward and Data Engineer to enhance storage efficiency.
• Monitor compliance with data removal policies and report on outcomes.

Compliance Officer

• Ensure that data removal and management practices comply with legal and regulatory requirements.
• Review and audit data management policies for effectiveness and sustainability alignment.
• Provide guidance on best practices for data governance and compliance.
• Assist in training staff on data management policies related to sustainability.

What evidence shows this is happening in your organization?

• Data Cleanup Policy: Establish organizational rules and guidelines to identify, classify, and remove datasets that no longer provide business value or are redundant. This policy outlines roles and responsibilities, continuous maintenance procedures, and compliance requirements to ensure that obsolete data is regularly purged.
• Redundant Data Removal Checklist: Provide a step-by-step procedure for identifying, reviewing, and removing duplicated or outdated information across various systems. This checklist helps ensure adherence to compliance standards and the sustainability goal of minimizing storage usage.
• Data Removal Runbook: Detail the technical processes and automation scripts necessary for safe and efficient data elimination. This runbook includes instructions on scheduling tasks, verifying records before deletion, and monitoring outcomes to maintain efficient data storage practices.

Cloud Services

AWS

• Amazon S3: Amazon S3 allows you to store data in scalable storage with different classes. By introducing lifecycle policies, you can automatically transition less frequently accessed data to lower-cost storage classes or delete it altogether.
• AWS Glue: AWS Glue helps you discover and manage your data. It allows for efficient data cleaning and deduplication, thereby optimizing storage usage by removing redundancy.
• Amazon RDS: Amazon RDS provides automated backups and the ability to delete unnecessary snapshots, thus reducing the storage footprint while ensuring data integrity.
• Amazon Data Lifecycle Manager: This service automates the creation, retention, and deletion of EBS volumes, allowing you to manage storage efficiently as your data needs change.

Azure

• Azure Blob Storage: Azure Blob Storage offers lifecycle management policies that automate moving blobs to cooler storage or deleting them when no longer needed.
• Azure Purview: Azure Purview helps you catalog and manage your data, making it easier to identify and remove redundant or unneeded data across your resources.

Google Cloud Platform

• Google Cloud Storage: Google Cloud Storage enables lifecycle management to automatically delete or transition data to lower-cost storage tiers based on your usage patterns.
• BigQuery: BigQuery allows you to query large datasets efficiently and offers features to identify and remove redundant data through its auditing and querying capabilities.