What are the common challenges and risks of data retention policies and practices in your industry or domain?

1 Regulatory compliance

One of the main challenges of data retention is to comply with the relevant laws and regulations that govern how long and under what conditions data can be kept. Different jurisdictions and sectors may have different requirements and standards for data protection, privacy, security, and access. For example, the General Data Protection Regulation (GDPR) in the European Union imposes strict rules on how personal data can be processed and retained, while the Health Insurance Portability and Accountability Act (HIPAA) in the United States regulates how health information can be handled and disclosed. Data architects need to be aware of the applicable regulations and ensure that the data retention policies and practices are aligned with them.

2 Data quality

Another challenge of data retention is to maintain the quality and integrity of the data over time. Data quality refers to the accuracy, completeness, consistency, timeliness, and relevance of the data. Data quality can deteriorate due to various factors, such as errors, duplication, corruption, obsolescence, or incompleteness. Poor data quality can affect the reliability and validity of the data analysis and decision making, as well as the trust and satisfaction of the data users and customers. Data architects need to implement data quality management processes and tools, such as data validation, cleansing, deduplication, enrichment, and monitoring, to ensure that the data retained is fit for purpose.

4 Data storage

A fourth challenge of data retention is to manage the storage and infrastructure of the data. Data storage refers to the physical or virtual space and resources needed to store and access the data. Data storage can be costly and complex, depending on the volume, velocity, variety, and value of the data. Data storage can also affect the performance and availability of the data services and applications. Data architects need to optimize and scale the data storage architecture and technology, such as cloud, on-premise, hybrid, relational, or non-relational, to meet the data retention needs and demands.

5 Data governance

A fifth challenge of data retention is to establish and enforce the data governance framework and policies. Data governance refers to the set of roles, responsibilities, rules, standards, and processes that define and regulate how data is collected, stored, used, shared, and retained. Data governance aims to ensure that the data is aligned with the strategic goals and values of the organization, as well as the expectations and interests of the data stakeholders. Data architects need to collaborate and communicate with the data governance bodies and committees, such as data owners, custodians, stewards, and users, to define and implement the data retention policies and practices.

6 Data value

A sixth challenge of data retention is to evaluate and maximize the value and utility of the data. Data value refers to the benefits and outcomes that can be derived from the data analysis and utilization. Data value can vary depending on the context, purpose, and perspective of the data users and customers. Data value can also change over time, as new insights, opportunities, or risks emerge from the data. Data architects need to assess and monitor the data value and usage, and determine when and how to archive, delete, or retain the data.

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?