In today’s digital age, businesses have access to an unprecedented amount of data. This data can be a valuable asset, providing insights and opportunities for growth. However, it also comes with its own set of challenges, particularly when it comes to data deletion. As organizations navigate the ever-evolving data landscape, they must carefully consider what data to keep, how to use it, and when to delete it.
The growth of generative AI has contributed to a doubling of unstructured data, creating a need for businesses to take a critical look at their data retention and deletion strategies. Alongside this growth, the cost of data breaches and privacy violations is skyrocketing. Criminals are targeting highly sensitive databases, resulting in significant financial losses for companies. In fact, IBM found that the average cost of a breach in 2023 was $4.45 million, a 15% increase from the previous year.
To effectively manage data and mitigate risks, organizations must establish policies to delete obsolete data. While there may be concerns about deleting data that could be valuable in the future, the longer data is stored, the higher the risk of a breach or violations of privacy laws. It is essential for companies to assess how they are using their data and weigh the benefits against the potential risks.
Deleting obsolete data serves multiple purposes. Not only does it reduce legal liability by complying with data protection laws, but it also helps reduce storage costs. However, identifying which data is considered obsolete can be challenging. To address this, organizations should start by creating a comprehensive data map that outlines the sources and types of incoming data, where it is stored, and how it is processed. This map provides a foundation for understanding how data flows through the company’s systems and helps identify which data is most valuable and which can be safely deleted.
When deciding which data to retain or delete, organizations should consider legal obligations, such as financial record retention requirements and sector-specific regulations. They should also evaluate the potential risks and benefits of retaining specific data. For example, outdated data may not accurately reflect current market conditions or consumer behaviors. By focusing on more recent data sources, companies can obtain more accurate insights.
Deletion can be done manually based on predefined retention periods, but automating the process with a purge policy improves reliability. Another option is to deidentify the data by removing identifiable personal information. However, this approach has its challenges, as truly deidentified data may not retain much value for analysis or AI models.
One common pitfall in addressing obsolete data is rushing the process and skipping important conversations. It is crucial to involve multiple groups, including legal, privacy, security, and business leaders, in determining what data is essential to keep. Shortening retention periods over time is easier than trying to recover deleted data.
Navigating the complexities of data deletion requires a strategic and informed approach. Organizations must understand the legal, cybersecurity, and financial implications and develop a robust data retention strategy that not only complies with regulations but also safeguards their digital assets. By carefully managing their data deletion practices, businesses can harness the power of AI while avoiding potential legal issues and security breaches.
