At present, businesses give a high priority to efficiently managing data due to the ever-growing digital transformation ecosystem. They are battling with an unparalleled volume of data produced by many sources, including people, devices, apps, and Internet of Things (IoT) devices. In this scenario, one approach gaining prominence is Data Lifecycle Management (DLM).
This policy-driven strategy manages the flow of data from the moment of creation to the point of deletion. In its entire cycle, Data Lifecycle Management covers important aspects like security, integrity, and availability.
Read on to learn more about Data Lifecycle Management, what exactly it means, its 3 main goals, key phases, and many more things you must check out!
What Are the Three Goals of Data Lifecycle Management?
In the field of DLM, three main goals stand tall as crucial pillars for success:
1. Data Security and Confidentiality:
The rise of regulatory frameworks such as the (CCPA), (GDPR), (HIPAA), and (SOX) have made safeguarding sensitive data an absolute necessity. Data Lifecycle Management makes sure that information is safe all the way through its lifespan, protecting sensitive information from possible breaches.
2. Data Integrity:
The second primary foundation is Data Integrity, which highlights the critical need to guarantee the precision and dependability of data, regardless of its location or the volume of users engaging with it. Within this context, the objective of Data Lifecycle Management (DLM) is to uphold data integrity, defined as the capability of information to remain trustworthy and unaltered, irrespective of where it is stored or the interactions it undergoes with users.
3. Data Availability:
Data must be easily accessible to authorized individuals without interfering with daily operations or workflows. This goal guarantees that the right people may access the appropriate information at the appropriate moment. Data availability is a basic necessity in the fast-paced commercial world of today.
Key Phases of Data Lifecycle Management
DLM can only be fully understood by breaking down its major stages, which offer a foundation for efficient data management all the way through the lifecycle:
1. Generate and Collect Data:
Data, both structured and unstructured, is continuously generated by a variety of sources, which we stated above. DLM acknowledges all possible sources of data and makes sure that it is collected effectively. Interestingly, based on relevancy, not all generated data may be gathered.
2. Store and Manage Data:
During this phase, data security and integrity are maintained. To protect data, proper handling, processing, and storage are used. This stage also includes encryption, compression, purification, and transformation to ensure data availability, security, and integrity.
3. Use and Share Data:
The value of data is realized when authorized individuals can easily access and modify data to carry out daily tasks. This stage boosts an organization’s overall efficiency by facilitating user operations. It includes collaboration, business intelligence, analytics, and visualization.
4. Archive Data:
Data ageing means that information is no longer required for daily operations. However, it may still be important for analysis or compliance. Archiving data to safe, long-term storage systems guarantees that it will always be usable and available while keeping with legal requirements.
5. Destroy Data:
Data that has reached the end of its lifecycle must be safely and legally deleted. DLM makes sure that data is permanently erased without going against any data protection laws.
All these phases are interconnected in a way, which frequently leads to simultaneous activities. For example, data generation, collection, and storage usually take place in parallel, which illustrates how dynamic data management is.
Benefits of Data Lifecycle Management
Adding an advanced Data Lifecycle Management (DLM) strategy offers your business with several advantages:
➜ Improved Data Access: A well-structured DLM strategy enhances data accessibility, authorizing organizations to make informed decisions.
➜ Regulatory Compliance Assurance: Incorporating a DLM system guarantees regulatory compliance by providing a systematic approach to data management.
➜ Effective Data Governance: Controlled data governance through DLM results in enhanced data quality, reduced data management costs, and increased accessibility for all stakeholders.
DLM: A Cornerstone for Business Success
Unquestionably, data is the lifeblood of innovation and growth. That’s why, a strong Data Lifecycle Management (DLM) must be put in effect in modern businesses. This involves combining strong security measures, data governance principles, and compliance with regulations. Which in turn encourages growth, innovation, and success in the current atmosphere of the competitive landscape.
As the world grows more and more data-driven, a well-designed DLM strategy becomes essential to the long-term profitability and sustainability of businesses of all kinds and sizes.
DLM vs. ILM: Decoding the Differences
A source of frequent misunderstanding lies in differentiating between Data Lifecycle Management (DLM) and Information Lifecycle Management (ILM).
DLM primarily revolves around the preservation of entire datasets or records, highlighting the progression of unprocessed data throughout its lifecycle. On the other hand, ILM considers the value of information to an organization beyond raw data.
Both DLM and ILM are essential to handle data effectively. While DLM defines the lifecycle of data, ILM oversees the accuracy and storage of information.
Data Lifecycle Management: Challenges & Solutions
Although implementing DLM has many advantages, there are certain common challenges that businesses confront. They have challenges with resource allocation, precise data acquisition, storage techniques, and effective data management.
These Data Lifecycle Management issues need careful thought. Businesses can use the below-listed tactics to address these challenges:
1. Automated Solutions: In DLM, automation becomes a valuable friend. It streamlines manual procedures including data entry, verification, transfer, and archiving. Automation technologies improve accuracy as well as efficiency in the data management process.
2. Proper Data Governance Protocols: For businesses, having strong data governance protocols is very crucial. These protocols offer a well-organized framework for data management. It also ensures consistency and creates a solid data strategy.
3. Strong Security Measures: Safeguarding data privacy relies on the use of encryption techniques. To enhance defence against possible threats, it is advisable to install firewalls and antivirus software.
Conclusion
Data Lifecycle Management is a dynamic and all-encompassing strategy. It is essential for negotiating the challenges of modern data management. Businesses can unlock the full potential of their data assets and drive themselves toward long-term success. Achieving this involves learning its stages, addressing prevalent challenges, and adjusting to its objectives.
All in all, in a world that is becoming more and more data-centric, the Data Lifecycle Management (DLM) process becomes not just a necessity but also strategically vital.