Data Lifecycle Management
The concept of Data Lifecycle Management became widespread as businesses recognized the need to manage data effectively at every stage, from data collection to disposal.
It addresses challenges like storage costs, security, and compliance, and has developed a lot in response to evolving IT trends and business requirements. In this article, you’ll learn the definition of what is DLM, its goals, stages, and main principles. This information will help you establish a clear structure for your own data process flows.
What Is Data Lifecycle Management?
Data Lifecycle Management (DLM) is a model for managing the entire lifecycle of an organization’s data, from its creation and initial storage to deletion.
A DLM strategy promotes compliance with the data retention regulations of various industry sectors.
This model separates data into five distinct stages, sometimes known as phases, based on different criteria. A DLM approach addresses data in every one of these stages to ensure it’s available in an organized way.
Data security is another priority of a DLM strategy. Both daily protection and disaster recovery work to mitigate data loss, data breaches or system failure situations. Otherwise, your data collection process may be inefficient, ineffective and a major liability to your company.
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The Three Main Goals of Data Lifecycle Management
There are typically three main goals of data lifecycle management. They collectively contribute to the overall efficiency, security, and strategic use of data within an organization.
They also help you avoid liability issues as you turn data into insights. Also known as the CIA triad, the three goals are confidentiality, integrity and availability.
These goals are all interrelated; for example, if data isn’t confidential, then you may not be able to ensure its integrity. It’s recommended to review each goal independently to see how these distinct features come together in a triad your business can rely on.
Confidentiality
Confidentiality is often considered a goal of Data Lifecycle Management. It ensures sensitive or private information is accessible to authorized users only.
This means protecting data not only from malware and threat actors but also from unauthorized individuals attempting to access proprietary or personal information.
Ensuring the confidentiality of information is a crucial aspect of managing data throughout its lifecycle. This goal is particularly relevant in industries and organizations where data security and privacy are paramount.
Integrity
This goal is fundamental to building trust in the data and making informed decisions based on accurate information.
Some data is highly restricted and rarely accessed. Other information in a DLM model can be routinely accessed by multiple users. Regardless of the way it’s stored and who accesses it, data needs to have integrity: it must remain authentic and cannot be improperly modified or deleted.
Availability
The third goal of data lifecycle management is to provide data in a fast, efficient way, supporting business operations, decision-making processes, and maintaining overall productivity.
Workflows can easily fail and bottlenecks can occur if data isn’t available at the right time for appropriate users. A strong DLM approach enables IT teams to consistently tag metadata for usability.
5 Stages of the Data Lifecycle
A data lifecycle is technically a circular pattern of five distinct phases. A strong DLM approach continuously moves through these 5 phases of data life cycle management while taking steps to always meet the three goals.
Review each stage to see how this competitive framework can help your business get the most out of your data.
Stage 1: Data Creation and Collection
Without data, the entire DLM engine doesn’t operate. The data collection stage involves gathering information from surveys, forms, internet of things devices, and other sources. Some data creation processes can be time consuming, so considering the benefits of automated data capture to streamline this first stage is a good step.
Remembering the three goals of DLM, it’s critical that you review the integrity and security of the data you’re creating and collecting. Starting a data lifecycle with corrupted data can compromise the entire process.
Stage 2: Data Storage and Processing
Stage two involves processing the data collected in stage one and storing it in the most efficient way for its type. For example, structured data is generally stored in relational databases, while unstructured data can be in non-relational databases.
Part of the processing step is continuing to review the security and integrity of the data, so stage two of a DLM approach typically includes backing up the data. Keeping copies of all stored data reduces the risk of malware attacks, accidental deletion and data corruption.
Stage 3: Data Usage and Sharing
This is the stage of data lifecycle management where you can start analyzing the data with analytical tools. After you define the individuals with access to the data, these individuals can now view the data.
You must also set the allowed purposes of the data. For example, some individuals may only be able to view the data, while others can perform data mining activities and leverage machine learning in their data usage. Stage three is also when you can allow external access to your data, such as using dedicated software to perform in-depth analysis and reporting.
Stage 4: Data Archival and Maintenance
Data typically has a limited lifetime of usefulness for routine analysis. If you’re reviewing data sets on a daily, weekly or even monthly basis, data that’s more than a year old may no longer be useful. Rather than deleting all this information, however, it may make more sense for your organization to archive it.
Retrieval of archived data is still possible even though it isn’t accessed frequently. The goal of data archival is to maintain DLM efficiency, leaving only relevant data in the active production environment.
Stage 5: Data Deletion and Security
Deleting data opens up more storage space and may be a necessary security step. If you’re confident you’ll never need the data again, then it doesn’t make sense to archive it.
You may need to delete data after a set period of time or at the request of a user. This is particularly true for personal information.
Stage five of DLM promotes security through effective data deletion from both the active production environment and the archives. It also aids in cost-controlling measures, since storing, backing up and archiving irrelevant data is a waste of data storage.
The results of following these five stages are effective data usability, streamlined data functionality and reduced costs for data management. Using an efficient DLM strategy reduces the risk of compromised data affecting your analysis or becoming a security liability for your business.
How Fluix Can Help Manage Your Data Lifecycle Management
Fluix is data collection software that offers tools to manage every stage of DLM. You can use it for digital form creation, secure storage, workflow automation, archival management, and data removal, enhancing your strategies and improving overall operational efficiency.
| DLM Stages | Features for Efficient Management |
| Creartion | Data capture with customizable digital forms |
| Storage | 10 free GB of cloud storage with the option to extend the limit |
| Usage | Unlimited automated workflows for data processingVersion control Integration-based analysis and reporting |
| Maintenance | Archiving stored documents for up to 1 year |
| Deletion | Secure document disposal and data removal |
Start incorporating these features into your operations today, reducing manual processes and ensuring the security and accessibility of data throughout its lifecycle.
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