Data retention has always been an important component of data management processes and the data lifecycle, from data creation and collection to archiving and data sanitization. Data retention policies vary significantly from industry to industry due to regulatory compliance, legal requirements, industry standards and business needs. But these policies are critical for organizations that must comply with the GDPR, the California Privacy Rights Act (CPRA), and similar laws enacted in Virginia, Colorado and Connecticut, among others.
As data moves through its lifecycle, organizations should aim to balance the value of the data because when data ages, it can become redundant, obsolete and trivial (ROT) meaning it is no longer valuable and can be eradicated permanently through data sanitization which helps minimize the risks associated with holding onto unnecessary or outdated data and the potential for “data assets” to become a liability.
Retention Processes Improve Data Quality
Data retention policies ensure that only relevant data is kept, reducing storage costs and optimizing resources. For companies in highly regulated industries, such as finance, data retention policies ensure financial data remains accurate, compliant with various regulations, as well as legal and tax regulations. For example, records related to financial transactions, such as invoices, receipts and bank statements, must be retained for seven years, while payroll records must be kept for 7-10 years, depending on regional labor laws. After the required retention period ends, data must be securely sanitized (unless it is under legal hold).
Data retention policies also help organizations maintain high-quality data by defining how long data should be stored and when it should be archived or deleted. This supports key data retention goals to support cleanliness, relevance and reliability of data systems by:
- Improving Accuracy: Errors abound in old and/or duplicated data. Retention policies help by regularly cleaning the database, keeping only current, accurate records.
- Removing Outdated Data: Automated data sanitization deletes old data to prevent clutter and reduce the chance of using irrelevant or obsolete information in analysis or decision-making.
- Ensures Consistency: By enforcing structured timelines for keeping data, retention policies help maintain consistency in the type and timeframe of data available, which improves comparisons and trend analysis.
- Ensuring Compliance and Audit Readiness: Data quality isn’t just about accuracy—it’s also about being trustworthy and audit-ready. Executing data retention best practices helps organizations meet regulatory standards, boosting credibility and reducing the potential for legal fines.
Rethinking Data Retention for the AI Era
In 2006, British mathematician Clive Humby coined the phrase, “Data is the new oil.” Today, many would argue that data is even more valuable than oil because AI has changed the game and the importance of data. Organizations no longer store data just for regulatory compliance or business continuity; it is now seen as a strategic asset used to drive critical business decisions, train and fine-tune LLM models, and monitor AI systems, securely and ethically.
As AI transforms the fundamentals of data management, it is also driving change in data retention policies. Data is seen as fuel to train machine learning models, improve personalization and automation, build recommendation engines and analytics dashboards, and feed real-time, prediction and GenAI systems.
AI’s demand for quality data is changing retention strategies, making both structured and unstructured data, including emails, videos, chat logs, sensor data and clickstreams, equally important. Data deletion and subsequent sanitization are more of a strategic decision as a result.
Data retention does face some challenges in today’s AI-powered enterprise. For example, there are some ethical considerations: Just because data is valuable doesn’t mean organizations should keep it. Outdated, biased, or incomplete data can taint future model outputs. Organizations will also need to scale data governance to manage the increase in data that AI systems will generate, which could range from tens of gigabytes to a few terabytes in a small company, to petabytes in some large enterprises.
Data Retention Strategies Reimagined
Traditional data retention policies were primarily designed to ensure regulatory compliance, protect privacy, manage storage costs and maintain data quality. They focused on structured data with clearly defined lifecycles, such as deleting financial records after seven years or archiving inactive customer information. These policies helped organizations be more strategic about removing outdated or irrelevant data, improving system efficiency, and reducing security risks associated with storing unnecessary or sensitive information.
In the age of AI, however, data takes on new strategic value with large volumes of diverse, historical and even unstructured data now critical for training and refining AI models. This shift requires retention policies to evolve to balance the need to preserve useful data for machine learning while still meeting compliance mandates and regulatory standards.
