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What are the best practices for managing data retention across multiple systems?

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Data retention is the process of keeping data for a specified period of time, based on its value, purpose, and regulatory requirements. Data retention can be challenging when you have multiple systems that store, process, and share data, such as databases, applications, cloud services, and archives. How do you ensure that you retain the right data for the right amount of time, without compromising data quality, security, and compliance? In this article, we will discuss some of the best practices for managing data retention across multiple systems.

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Murali Krishna

Director, Data Solutions and Products, Data and AI Platform @VISA

1 Assess your data sources

The first step is to identify and inventory all the data sources that you have across your systems, and classify them according to their type, format, sensitivity, and ownership. You should also document the data flows and dependencies between your systems, and how they affect the data lifecycle. This will help you understand the scope and complexity of your data retention needs, and prioritize the most critical and sensitive data sources.

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2 Define your data retention policies

The next step is to define your data retention policies, which are the rules and guidelines that specify how long and where to retain your data. Your data retention policies should be based on your business objectives, legal obligations, and ethical standards. You should also consider the costs and benefits of retaining data, such as storage space, performance, usability, and value. Your data retention policies should be clear, consistent, and aligned with your data governance framework.

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Murali Krishna

Director, Data Solutions and Products, Data and AI Platform @VISA
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Who defines the data retention policies is the key. There are multiple stakeholders and having right data steward/owner review the retention policies is critical to the process. Further, having an audit of changes to the retention policies also gives insight on the nature of data itself.

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3 Implement your data retention processes

The third step is to implement your data retention processes, which are the actions and procedures that execute your data retention policies. Your data retention processes should be automated, scalable, and auditable, to ensure that your data is retained accurately, efficiently, and securely. You should also monitor and measure your data retention processes, to verify that they are compliant with your policies and regulations. You should also review and update your data retention processes regularly, to adapt to changing business and technical needs.

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4 Optimize your data retention architecture

The fourth step is to optimize your data retention architecture, which is the design and structure of your data storage and management systems. Your data retention architecture should be optimized for performance, availability, scalability, and security, to support your data retention processes and policies. You should also leverage the best practices and tools for your specific data types and formats, such as compression, encryption, deduplication, tiering, archiving, and backup.

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5 Communicate your data retention strategy

The fifth step is to communicate your data retention strategy, which is the plan and vision that guides your data retention efforts. Your data retention strategy should be communicated to all the stakeholders involved in your data lifecycle, such as data owners, users, providers, and regulators. You should also educate and train your data team and staff on the importance and benefits of data retention, and how to follow your data retention policies and processes. You should also solicit feedback and suggestions from your data community, to improve your data retention strategy.

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Murali Krishna

Director, Data Solutions and Products, Data and AI Platform @VISA
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This is very critical in the entire life cycle of data as broken or inefficient communication would create debts and silos on data that are difficult to fix. Clear plans are to be put in place for regular communication about the data retention strategy to avoid any unforeseen issues and to gather feedback from the users

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6 Evaluate your data retention outcomes

The sixth step is to evaluate your data retention outcomes, which are the results and impacts of your data retention activities. Your data retention outcomes should be evaluated against your data governance goals and metrics, such as data quality, security, compliance, and value. You should also identify and address any gaps, issues, or risks that arise from your data retention practices, and implement corrective and preventive actions. You should also celebrate and share your data retention successes, and recognize the contributions of your data team and staff.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Murali Krishna

Director, Data Solutions and Products, Data and AI Platform @VISA
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Have a active working group that has relevant skills and understands as many data assets to address any issues that could arise from data retention policies. This working group should monitor/review various policies that are created at least on critical data sets

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What are the best practices for managing data retention across multiple systems?

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3

4

5

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7

1 Assess your data sources

2 Define your data retention policies

3 Implement your data retention processes

4 Optimize your data retention architecture

5 Communicate your data retention strategy

6 Evaluate your data retention outcomes

7 Here’s what else to consider

Data Governance

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What are the best practices for managing data retention across multiple systems?

1

2

3

4

5

6

7

1 Assess your data sources

2 Define your data retention policies

3 Implement your data retention processes

4 Optimize your data retention architecture

5 Communicate your data retention strategy

6 Evaluate your data retention outcomes

7 Here’s what else to consider

Data Governance

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