Data lifecycle management (DLM) is an approach to managing data throughout its lifecycle, from data entry to data destruction. Data is separated into phases based on different criteria, and it moves through these stages as it completes different tasks or meets certain requirements. A good DLM process provides structure and organization to a business’s data, which in turn enables key goals within the process, such as data security and data availability.
These goals are critical for business success and increase in importance with time. DLM policies and processes allow businesses to prepare for the devastating consequences should an organization experience data breaches, data loss, or system failure. A good DLM strategy prioritizes data protection and disaster recovery, especially as more malicious actors enter the marketplace with the rapid growth of data. This way, an effective data recovery plan is already in place in the event of a disaster, curtailing some of the devastating effects to a brand’s bottom line and overall reputation.
Information lifecycle management (ILM) is often used interchangeably with data lifecycle management, and while it is also part of a data management practice, it is distinct from DLM.
Data lifecycle management oversees file-level data; that is, it manages files based on type, size, and age. ILM, on the other hand, manages the individual pieces of data within a file, ensuring data accuracy and timely refreshes. This is inclusive of user information, such as e-mail addresses or account balances.
Scale AI workloads, for all your data, anywhere
A data lifecycle consists of a series of phases over the course its useful life. Each phase is governed by a set of policies that maximizes the data’s value during each stage of the lifecycle. DLM becomes increasingly important as the volume of data that is incorporated into business workstreams grows.
Phase 1: Data creation
A new data lifecycle starts with data collection, but the sources of data are abundant. They can vary from web and mobile applications, internet of things (IoT) devices, forms, surveys, and more. While data can be generated in a variety of ways, the collection of all available data isn’t necessary for the success of your business. The incorporation of new data should be always be evaluated based on its quality and relevancy to your business.
Phase 2: Data storage
Data can also differ in the way its structured, which has implications on the type of data storage that a company uses. Structured data tends to leverage relational databases while unstructured data typically makes use of NoSQL or non-relational databases. Once the type of storage is identified for the dataset, the infrastructure can be evaluated for any security vulnerabilities and the data can undergo different types of data processing, such as data encryption and data transformation, to safeguard the business from malicious actors. This type of data munging also ensures sensitive data meets the privacy and governmental requirements for governmental policies, like GDPR, allowing businesses to avoid any costly fines from these types of regulations.
Another aspect of data protection is a focus on data redundancy. A copy of any stored data can act as a backup in situations, such as data deletion or data corruption, protecting against accidental alterations in data and more deliberate ones, like malware attacks.
Phase 3: Data sharing and usage
During this phase, data becomes available to business users. DLM enables organizations to define who can use the data and the purpose for which it can be used. Once the data is made available it can be leveraged for a range of analyses—from basic exploratory data analysis and data visualizations to more advanced data mining and machine learning techniques. All of these methods play a role in business decision-making and communication to various stakeholders.
Additionally, data usage isn’t necessarily restricted to internal use only. For example, external service providers could use the data for purposes such as marketing analytics and advertising. Internal uses include day-to-day business processes and workflows, such as dashboards and presentations.
Phase 4: Data archival
After a certain amount of time, data is no longer useful for everyday operations. However, it is important to maintain copies of the organization’s data that is not frequently accessed for potential litigation and investigation needs. Then, if required, archived data can be restored to an active production environment.
An organization’s DLM strategy should clearly define when, where, and for how long data should be archived. In this stage, data undergoes an archival process that ensures redundancy.
Phase 5: Data Deletion
In this final stage of the lifecycle, data is purged from the records and destroyed securely. Businesses will delete data that they no longer need to create more storage space for active data. During this phase, data is removed from archives when it exceeds the required retention period or no longer serves a meaningful purpose to the organization.
Data lifecycle management has several important benefits which include:
• Process improvement: Data plays a crucial role in driving the strategic initiatives of an organization. DLM helps maintain data quality throughout its lifecycle, which in turn enables process improvement and increases efficiency. A good DLM strategy ensures that the data available to users is accurate and reliable, enabling businesses to maximize the value of their data.
• Controlling costs: A DLM process places value on data at each stage of its lifecycle. Once data is no longer useful for production environments, organizations can leverage a range of solutions to reduce costs such as data backup, replication and archiving. For example, it can be moved to less-costly storage located on-premises, in the cloud, or in network attached storage.
• Data usability: With a DLM strategy, IT teams can develop policies and procedures that ensure all metadata is tagged consistently so it can improve accessibility when needed. Establishing enforceable governance policies ensures the value of data for as long as it needs to be retained. The availability of clean and useful data increases the agility and efficiency of company processes.
• Compliance and governance: Each industry sector has its own rules and regulations for data retention, and a sound DLM strategy helps businesses remain compliant. DLM lets organizations handle data with increased efficiency and security, while maintaining compliance with data privacy laws regarding personal data and organizational records.
IBM offers business solutions that help organizations manage data from requirements through retirement to improve business agility while reducing costs. IBM Cloud Pak for Data leverages microservices and its leading data and AI capabilities to enable the intelligent integration of data across distributed systems, providing companies with a holistic view of business performance. This facilitates faster collection, organization, and insight into enterprise data, and it allows businesses to make decisions at scale. Data management teams can also trust that their data is secure with IBM’s competitive security frameworks, ensuring adherence to regulatory policies and reducing any compliance risks.