Understanding data lifecycle management

Data lifecycle management enables businesses to manage data properly throughout its lifecycle. It is a regulated, strategic approach to managing and organizing data from creation to destruction.

Written by

Belinda Walsh

Reviewed by

Adam Roberts
Share on Social Media

What is data lifecycle management (DLM)?

Data lifecycle management (DLM) is a regulated, strategic approach to managing and organizing data throughout its lifecycle, from data creation to data destruction. 

The primary goal of DLM is to ensure that every piece of data a business possesses is structured, correctly categorized, and readily accessible to the right people at the right time. 

This allows an organization to meet critical objectives, such as:

  • Protecting data confidentiality
  • Maintaining data integrity
  • Ensuring data availability

Why is DLM important?

Businesses must deal with a significant volume of data, much of it unstructured and siloed on multiple different systems and data platforms, making it increasingly difficult to manage, store, and use critical data effectively.

Data lifecycle management is a way of making sense of this chaos and transforming unstructured data into valuable intelligence. 

Whether data is stored on-site, in cloud-native platforms, at colocation facilities, or within edge computing environments, a DLM system helps a business locate, organize, access, and safeguard its data wherever it lies. 

Data security

If you don’t know where all of your data lies, how can you be sure it’s safe and secure? 

DLM establishes well-defined protocols and policies regarding the handling of data in an active production environment. 

This ensures you’ll always know exactly where your sensitive data lies and have confidence it’s protected from any internal and external threats, such as a data breach. In the worst-case scenario, DLM will also help with disaster recovery.


With great data comes great responsibility. No matter your location, compliance standards dictate how you need to store, protect, and use your confidential information.

DLM keeps you on the right side of these regulations by establishing policies for the handling of data. Effective DLM strategies also ensure you’ll have all of your data readily available to provide during audits and investigations, so you’re always ready to prove your compliance. 

Data discovery

As we discussed earlier, DLM helps you combine and organize data sets that would typically be isolated. Doing so gives you the opportunity to discover insights, trends, and patterns that are only possible when you view all of your data collectively. 

Once you’ve gained the complete picture, you can use the insights to support strategic planning. DLM is an excellent way to make informed, data-driven decisions and elevate your operational efficiency. 

DLM vs ILM: What’s the difference?

Data lifecycle management (DLM) and information lifecycle management (ILM) are often used interchangeably, but there is a crucial difference that separates the two terms. 

The main distinction is that DLM focuses on managing entire data files throughout the lifecycle, while ILM considers the value of the information data possesses.

DLM is all about ensuring core tasks, such as data creation, storage, and destruction, are carried out in line with compliance. ILM takes a more holistic approach, focusing on the ways businesses can benefit and find insights from the data they possess.

Understanding the five stages of the data lifecycle

The data lifecycle consists of several critical phases, each with unique policies and best practices to maximize your data’s value throughout its useful life. Stages include: 

  • Data collection and creation
  • Data storage and maintenance
  • Data usage and analysis
  • Data archiving
  • Data destruction

The DLM process categorizes data into these stages based on specific criteria and requirements. 

To elaborate on how this works, let’s run through the five steps in the data lifecycle. We’ll also detail some best practices for each stage. 

1. Data collection and creation

The first stage centers on creating, capturing, and collecting data. This data can be unstructured (e.g. text documents), semi-structured (XML files), or structured (databases). 

Data is abundant, and businesses typically have numerous sources generating it—from databases and mobile applications to Internet of Things (IoT) devices, machinery, and more.  

The goal of DLM during this stage is to take all of this disparate, siloed data and unify it by categorizing it and providing context to make it easily identifiable. 

It’s important to note that not all data is created equal. Some data simply isn’t valuable for your company. For example, machines generate mountains of sensor data on a regular basis, but this information is only helpful if there’s an anomaly. In the early stages, it’s crucial to eliminate redundant, obsolete or trivial (ROT) data so you can focus on the information that matters.

Data creation and ingestion - best practices

  • Develop rules to standardize data formats, naming conventions, and metadata.
  • Create policies for securely collecting sensitive information.
  • Implement robust validation processes to ensure data accuracy and completeness.
  • Classify data according to its value, sensitivity, and any regulations that impact it.

2. Data storage and maintenance

Once the data is classified, it needs to be stored securely in a stable environment and maintained to ensure integrity, for its retention period.

The way you store your information will largely depend on the way the data is structured. You’ll typically store structured data in a relational database while storing unstructured data in either a NoSQL format or a non-relational database. 

At this stage, you’ll also need to perform data processing, such as by compressing, transforming, cleansing, or encrypting the information. This ensures all of your data is well-structured and easily identifiable within your database.

Data storage and maintenance - best practices

  • Implement data access controls and encryption to safeguard information.
  • Employ redundancy measures to back up data and aid with data loss prevention.
  • Conduct routine audits to assess storage efficiency and eliminate obsolete data.

3. Data usage and analysis

With the data securely stored, you’re now ready to make it accessible to your business users.

At this stage, authorized users who have access can use and modify data as required to carry out their day-to-day tasks, such as visualization and analytics. 

The usage stage is one of the most productive phases of the data lifecycle because it allows you to extract information, find patterns, and uncover valuable insights through analysis. 

In doing so, you’ll find new ways to make better decisions, create well-planned strategies, and improve your operational efficiency. 

Data usage and analysis - best practices

  • Anonymize personally identifiable information when analyzing to protect privacy.
  • Stay informed about data protection regulations relevant to your industry.
  • Establish ethical guidelines for data usage to ensure proper handling of information.

4. Data archiving

At some point, your data will no longer be necessary for your day-to-day operations. But before you delete it, you must archive a copy for future investigations, reporting, and compliance. 

Archiving ensures the preservation and long-term availability of data. It involves moving data into a long-term storage system. If you require the information at a later date, you can quickly restore your data backups to your active directory for further usage. 

Data archiving - best practices

  • Ensure you safeguard your data archives just as securely as active data.
  • Establish data retention policies outlining what data should be archived and for how long.
  • Archive data in standard formats that are likely to remain accessible as technology evolves. 

5. Data destruction

In the final stage of the life cycle, your data has reached the end of its usable life. You can now delete it to create more storage space for active data.

While this may seem like a straightforward task, it’s crucial that you securely destroy your data in line with compliance standards. The goal here is to make your data completely inaccessible so that malicious individuals won’t find a way to recover it.

It’s necessary to have well-documented data destruction procedures, as many regulations, such as NIST-800-88 and the General Data Protection Regulation (GDPR), have strict guidelines about how and when data should be destroyed. 

As you delete data, you’ll need to verify that you’re destroying it properly. To achieve this, you’ll need a Certificate of Data Destruction stating you destroyed your data in line with relevant regulations.

Your Certificate of Data Destruction should contain information about the data you’ve destroyed and how you destroyed it. Maintaining these certificates is essential for audits and chain of custody verification processes. A comprehensive Record Management Solution like RecordPoint can take care of data destruction and maintain certificates on your behalf.

Data deletion - best practices

  • Employ secure deletion methods, such as shredding, encryption, or data wiping. 
  • Practice data sanitization and gain validation to prove you are destroying data properly.
  • Educate employees on proper data deletion to minimize the risk of data exposure.

Challenges and solutions in data lifecycle management

While DLM is a comprehensive approach, it isn’t foolproof. Common challenges include ensuring data quality, maintaining consistent standardization across all data sources, and achieving regulatory compliance.

Fortunately, there are solutions to these problems. Businesses can adopt the following strategies to ensure their DLM solution is watertight. 

  • Robust security measures: One of the primary reasons businesses fail to meet compliance is a lack of DLM security measures. Organizations should utilize firewalls, antivirus systems, and encryption techniques to ensure their data is private and secure. 
  • Data governance policies: A comprehensive data governance framework provides a cohesive business strategy for the handling of data across an organization. Maintaining detailed policies and educating all users on these protocols is essential for ensuring consistent, improved data management. 
  • Automation: Manually entering, indexing, and archiving data is a time-consuming process and prone to error. Automating the data management process with a Records Management System (RMS) ensures accuracy, allowing businesses to spend more time using the data they possess.  

The importance of an electronic document records management system (EDRMS)

The best advice for businesses handling large amounts of data is: Don’t go it alone. 

The more time you spend manually handling data to meet compliance, the less time you have to discover valuable insights from the information you possess. This is where an electronic document and record management system (EDRMS) really shines. 

An EDRMS provides a centralized solution for managing data throughout its lifecycle. It empowers you to automate the DLM process with powerful features, such as automated data classification, retention policies, audit trails, and robust security systems. 

At the start of the lifecycle, your EDRMS will help you discover your data and manage it in place, so you won’t waste time transferring siloed data types to your catalog. 

Following this, your EDRMS will automate the indexing and storage process, keeping your data correctly labeled and filed in line with compliance standards.

Your EDRMS will also flag records for retention, holding, or disposition based on the contents of the file. Combined with accurate record-keeping, this means you’ll always be able to prove compliance with relevant regulations.

RecordPoint: Records management, done right. 

Our cloud-native platform offers a hassle-free way to manage your data throughout its lifecycle, at scale. 

With support for over 900 applications, our solution will let you control everything in one central place, no matter where your data lives. 

When you migrate your data through our platform, our AI model will flag records based on their importance so you can safeguard the data you need, and defensibly dispose of the information you don’t. 

Once your data reaches the end of its useful life, we’ll destroy it in line with regulations and maintain a certificate of data destruction on your behalf so you’re always prepared in case of an audit. 

With RecordPoint at your side, you’ll have everything you need to handle compliance with regulations, so you can spend more time working on the business processes that directly benefit you. 

Ready to get started? Reach out and book a tour today to learn how we can help your business grow this year.

DLM is the key to driving business growth

Implementing a comprehensive DLM solution with robust security measures is the only way to remain organized and proactive against evolving regulations. 

Once you’ve achieved this compliance, you can begin to learn more about the data you possess and use the information to your advantage. Whether it’s optimizing the customer experience, improving your products, or discovering new opportunities for innovation, your DLM strategy will open new doors to propel your business growth.

The key to realizing the full benefits of DLM is to partner with a robust records management solution that can handle data lifecycles, compliance, and security on your behalf. 

RecordPoint is the key to unlocking the advantages of the data you possess. Reach out today to learn more.