The data lifecycle is the sequence of stages that data goes through from its creation to its eventual deletion. The different stages of the data lifecycle can vary depending on the organization, but they generally include:

• Data creation: This is the stage where data is first generated or collected. Data can be created in a variety of ways, such as through customer transactions, sensor readings, or surveys.
• Data storage: Once data has been created, it needs to be stored in a secure location. The type of storage used will depend on the volume and type of data, as well as the organization’s security requirements.
• Data processing: Data may need to be processed before it can be used. This can involve cleaning, transforming, or aggregating the data.
• Data analysis: Data is analyzed to extract insights and make decisions. This can be done using a variety of tools and techniques, such as statistical analysis, machine learning, and artificial intelligence.
• Data archiving: Data that is no longer needed for active use may be archived for future reference. Archived data is typically stored in a less expensive and less accessible location than active data.
• Data deletion: Data that is no longer needed is deleted. This is typically done in accordance with data retention policies.

Data lifecycle management (DLM) is the process of overseeing the data lifecycle from creation to deletion. DLM helps organizations to ensure that their data is secure, compliant, and accessible when needed.

Here are some of the benefits of DLM:

• Improved data security: DLM can help organizations to protect their data from unauthorized access, use, or disclosure.
• Increased data compliance: DLM can help organizations to comply with data privacy regulations, such as the General Data Protection Regulation (GDPR).
• Enhanced data availability: DLM can help organizations to ensure that their data is accessible when it is needed, even if it is stored in multiple locations.
• Improved data quality: DLM can help organizations to improve the quality of their data by ensuring that it is accurate, complete, and consistent.
• Reduced data costs: DLM can help organizations to reduce their data costs by optimizing their data storage and processing.

Overall, DLM is an important process for organizations that want to manage their data effectively and protect their data assets.