Data Management Glossary
Data Lifecycle Management
Data Lifecycle Management (DLM) is the process of managing data throughout its entire lifecycle – from creation or acquisition to its deletion or archiving. As the name suggests, Data Lifecycle Management involves various stages and activities to ensure that data is effectively and securely managed throughout its existence. With unprecedented data growth in the enterprise, particularly of unstructured data, data hoarding has become a significant challenge to address. The right approach to unstructured data management and the recognition that all data cannot be treated the same has led to an increased focus on data governance and data lifecycle management, which typically includes:
- Data Creation/Acquisition: This is the initial stage where data is generated or acquired by an organization through various sources such as data entry, sensor devices, APIs, data feeds, or third-party vendors.
- Data Storage: After data is created or acquired, it needs to be stored in appropriate data repositories, such as databases, data warehouses, data lakes, or cloud storage systems. The storage infrastructure must be designed to accommodate the volume, velocity, and variety of the data being managed.
- Data Processing and Analysis: Once the data is stored, it can be processed, transformed, and analyzed to derive insights and valuable information. This stage involves data cleansing, data integration, aggregation, and applying analytical techniques to extract meaningful patterns and trends. (Related areas: Data science, data lakes, data preparation, data warehousing.)
- Data Usage and Presentation: After the data has been analyzed, it is utilized to make informed decisions, generate reports, create dashboards, or feed into applications for various business purposes. Increasingly feeding AI and ML is a use case here.
- Data Archiving: As data ages or becomes less frequently used, it may be moved from active storage to long-term archival storage for compliance purposes or to free up resources on primary storage systems. (See hot data, cold data.)
- Data Retention and Deletion: Organizations need to establish data retention policies that dictate how long data should be kept based on regulatory requirements or business needs. At the end of its useful life, data should be securely and permanently deleted to avoid any data privacy or security risks. (See Data Hoarding)
- Data Security: Throughout the entire data lifecycle, data security measures must be implemented to protect data from unauthorized access, breaches, or other cybersecurity threats. (See Data Protection.)
- Data Governance and Compliance: Data governance policies and procedures are put in place to ensure data quality, integrity, and compliance with relevant regulations and standards.
- Data Backup and Disaster Recovery: Regular data backups and disaster recovery plans are essential to safeguard against data loss due to hardware failures, natural disasters, or cyber incidents.
The right data lifecycle management (see also Information Lifecycle Management) strategy can help organizations maximize the value of their data, reduce data storage costs, ensure data integrity, comply with regulations, and maintain good data hygiene practices. It is particularly crucial in the context of artificial intelligence (AI), big data, data privacy, and data protection considerations.