The 3 main goals of data lifecycle management

The 3 main goals of data lifecycle management is profiled by BTW Media because published evidence links it to internet infrastructure, governance, operational dependencies, or market visibility.

• Data Lifecycle Management (DLM) is a comprehensive approach to managing data from the point of its creation through to its final disposition.
• Managing the data lifecycle helps ensure that data remains accurate, reliable, and of high quality throughout its existence.
• Effective data lifecycle management ensures regulatory compliance and risk mitigation in data privacy, security, and governance.

OUR TAKE
Data lifecycle management ensures data quality, compliance, and cost optimisation, leading to more accurate insights, reduced risks, and improved operational efficiency for organisations. One of the concept that organisations should improve awareness of.
–Miurio Huang, BTW reporter

Data Lifecycle Management (DLM) is a comprehensive approach to managing data from the point of its creation through to its final disposition. It involves a series of policies, processes, and technologies designed to ensure that data is effectively managed and utilised throughout its lifecycle. DLM is crucial for organisations to maintain data integrity, security, and efficiency.

1. Ensure data’s integrity and availability

Managing the data lifecycle helps ensure that data remains accurate, reliable, and of high quality throughout its existence. By implementing processes for data validation, cleansing, and enrichment at various stages, organisations can avoid data degradation or inconsistencies that could lead to incorrect analysis or decision-making.

While achieving the process of data validation and verification, organisations regularly check data for errors or inconsistencies and correct them, which include reconciliation processes, data audits, and integrity checks. This can involve using constraints, checks, and validation rules during data entry. These procedures helps ensure that the data is correct and meaningful when it is entered into the system. Meanwhile, Organisations can through removing duplicate, incomplete, or erroneous data and regularly backing up data to improve data quality, to clean data and to protect against data loss due to hardware failures, software issues, or other disruptions, Implementing robust recovery procedures can restore data from backups in the event of data corruption or loss.

Also read: Spanish police investigate if hackers stole millions of drivers’ data

2. Ensuring data’s security and compliance

Effective data lifecycle management ensures regulatory compliance and risk mitigation in data privacy, security, and governance. It involves controlling access to sensitive data through measures like access controls, encryption, and data masking. Additionally, establishing policies for data retention and secure disposal helps organisations comply with regulations and minimise unauthorised access to outdated data. Strong auditing and monitoring measures, including tracking data access and changes, enable compliance with regulations such as GDPR, HIPAA, or SOX while identifying and addressing security threats to enhance data security and compliance efforts.

Also read: Trend micro unveils AI tools to safeguard data at Computex

3. Cost optimisation

Managing the data lifecycle allows organisations to optimise storage resources and reduce unnecessary data storage costs. By identifying and eliminating redundant, obsolete, or trivial (ROT) data, organisations can free up storage space, reduce backup and archival costs, and improve overall storage efficiency. Additionally, by aligning data storage and retention policies with business needs, organisations can avoid unnecessary expenses associated with storing