Most companies consider data their most valuable asset, second only to their employees. They must use this precious resource effectively to run their business and compete in their market sector. Modern organizations should implement a data lifecycle management (DLM) approach to safeguard their valuable information and optimize its use.
What is Data Lifecycle Management?
Data lifecycle management is the process of managing data throughout its complete lifetime. DLM involves companies establishing a structured framework for the way data is handled across the organization. Teams create policies and procedures and leverage appropriate technologies to govern, protect, and optimize data storage throughout its lifecycle.
Data Lifecycle Stages
DLM is designed to address data in all stages of its lifecycle.
- Data creation and collection: Companies generate or collect data from processes and applications, including customer transactions, system logs, and IoT sensors. Data elements must be validated, classified, and assigned ownership to ensure they are handled and stored properly.
- Data storage: Teams must store data in reliable systems that provide the appropriate level of performance and accessibility. Storage environments include databases, backup repositories, and data warehouses. The data may need to be encrypted and protected with stringent access controls based on its sensitivity and organizational value.
- Usage and access: Once data is collected and stored, it is used throughout the organization by applications and individuals. Organizations may also share data with external partners or customers. Companies should implement role-based access controls (RBAC), strong authentication, and logging to monitor activity. Data should only be shared or distributed to authorized entities.
- Archival: Infrequently used data is typically migrated to less expensive long-term storage solutions. Teams archive information to reduce costs, meet compliance standards, and improve performance by minimizing data storage volume.
- Retention for compliance: Certain types of data may need to be retained to meet compliance requirements. Examples are healthcare or financial records and audit logs. Teams must be able to recover this data upon request to demonstrate compliance.
- Data deletion and disposal: Data must be securely deleted after it has reached the end of required retention periods or is no longer useful to the organization. Data deletion reduces security vulnerabilities and storage costs. Secure deletion methods include the physical destruction of storage devices or cryptographic erasure.
Objectives of a comprehensive DLM approach
Organizations meet multiple objectives with effective data lifecycle management.
- Controlling storage costs: DLM promotes storing data in multiple tiers based on its value and accessibility requirements. Companies may overspend by storing data longer than necessary or by using expensive, readily accessible solutions for rarely used information.
- Enhancing security: DLM reduces unnecessary or irrelevant data, minimizing the attack surface available to threat actors. The framework also minimizes insider threats by enforcing access controls at each stage of the data lifecycle.
- Raising data quality: DLM keeps data clean and relevant by removing outdated or duplicate information, raising the accuracy and quality of analytics and other data-centric processes.
- Improving performance: Teams can boost system performance by eliminating stale and duplicate data, speeding up processes such as database searches and backups.
- Supporting compliance: Companies processing regulated data must retain specific elements for extended periods to meet regulatory standards such as HIPAA or GDPR. DLM automates the process of assigning and implementing the appropriate retention periods for regulated data.
Key Components of an Effective DLM Strategy
Companies adopting a DLM strategy must take a methodical approach that ensures all data resources are handled correctly. Effective data lifecycle management requires the coordination and enforcement of well-designed data handling policies and complementary technical solutions. The following components are essential for establishing a viable DLM program.
Data classification
The first step in DLM is classifying all data that an organization generates or collects. Classification determines how the data will be stored, accessed, and secured. Higher-value, frequently used data resources require different storage solutions from archived transaction records.
The classification should be based on the data’s sensitivity and importance. Decision-makers and subject matter experts must carefully construct these categories to reflect their operational, security, and compliance requirements. Common categories may include:
- Public data that needs no special handling;
- Internal business data that must be protected;
- Confidential data requiring more stringent security and access controls;
- Restricted or regulated data with extremely limited access.
Data security controls
Teams must develop security controls based on data classification to protect data throughout its lifecycle. Not all data requires the same degree of protection. Essential data security controls include:
- Encrypting data at rest and in transit;
- Defining strict access control policies;
- Segmenting networks to minimize threats to sensitive data;
- Implementing monitoring and data loss prevention (DLP) platforms;
- Maintaining logs of data access activity;
- Ensuring data is backed up and recoverable.
Security controls may evolve as data ages, changing its importance or sensitivity.
Backup and disaster recovery
Organizations need strong, reliable backup and disaster recovery solutions to protect their data from cyberattacks, natural disasters, and hardware or software failures. Companies with regulated data often need to meet specific availability guidelines that require rapid recovery capabilities. Teams must take precautions that data lifecycles are not interrupted by extenuating circumstances and that they can maintain business continuity.
Automated policy enforcement
Modern DLM solutions often employ automation to reduce manual errors and improve the program’s consistency. Automation can be very effective for many aspects of DLM, including:
- Classifying and assigning data to appropriate storage tiers;
- Scheduling migration to different tiers based on retention policies;
- Enforcing data retention policies for regulatory compliance;
- Deleting expired data to free storage resources.
How VAST Supports Your DLM Strategy
VAST understands the importance of managing your data effectively for your business. We offer multiple services that directly address specific aspects of a DLM strategy. The following are some examples of our solutions that support DLM.
- Our team can assist with information governance and help you automate retention policies and workflows.
- Our managed data backup and disaster recovery services play an essential role in your DLM program.
- We can help you optimize storage management through our partnerships with leading vendors and extensive experience in implementing customized solutions.
Contact our team today and see how we can help you implement effective data lifecycle management.
