Data Lifecycle Management (DLM) explained without technical jargon (2023)

In the 1980s, databases were created to make data accessible and manageable, but with that creation came challenges in collecting, storing, backing up, and deleting data. Over time, data and IT professionals have developed theories and shared best practices to provide a model known as Data Lifecycle Management (DLM).

Data Lifecycle Management (DLM) explained without technical jargon (1)

Understanding DLM principles can help any organization, from enterprises to small and medium-sized businesses, to set up a data flow structure or update an existing one. Let's delve into this topic below.

Was it data lifecycle management?

Data Lifecycle Management (DLM) is a model for managing data throughout its lifecycle so that it is optimized from creation to disposal. DLM is divided into phases, which typically start with data collection and end with data destruction or reuse.

By defining, organizing, and creating policies to manage data at every stage of its life, DLM helps you get the most out of your data right up to the point of deletion.

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What are the three main goals of data lifecycle management (DLM)?

While there are myriad benefits of DLM, there are three main goals of this model. Let's take a closer look at them below.


A key goal of data lifecycle management is the security of your data. By creating logs to manage data from creation to deletion, you help prevent attackers and other unauthorized users from accessing that data or from being damaged by malware and other infections.


While one goal of DLM is to ensure that specific users cannot access data, an equally important goal is to ensure that data is available to the right users at the right time. If this is not the case, various processes and workflows may be interrupted or fail.


Another goal of DLM is to maintain data integrity, which means that only the most current and highest quality data is created and stored in its database. Without DLM, users could access, use, and store outdated or different versions of data.

Data Lifecycle Management vs. Information Lifecycle Management

There are many parallel or complementary models to DLM that can guide you through the myriad of data management challenges.

For example, DLM often overlaps withInformation Lifecycle Management (ILM). The main difference between the two concepts is that while DLM determines when a data element ends its useful life, ILM governs when an information element is relevant and accurate and how it is stored accordingly.

Despite the number of parallel or complementary models, the DLM framework has become a prototype due to its simplicity and efficiency. We will define this framework below.

Data Lifecycle Management Framework

Because every company has its own business model, software stack, and data types, there are many variations on the DLM framework.

If you research online, you'll find that the number of phases and words used to describe each phase vary. For example, depending on how a company's data becomes part of the database, the first phase may be called data collection, collection, collection, or creation.

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Each company must adapt the DLM framework to its owntechnological ecosystem, there are five general stages as shown below.

Data Lifecycle Management (DLM) explained without technical jargon (3)

Let's take a closer look at the stages of data collection, storage, maintenance, use, and cleansing below.

1. Data collection

Data ingestion refers to the phase of DLM that brings new value to your organization's data infrastructure. It can be hardware or software. For this phase, you need to establish a set of rules for collecting data in standardized formats so that it is later accessible and manageable.

These rules or guidelines must be appropriate for each type of data collected. Therefore, you need to set policies for employee data, partner data, accounting, etc. For personal data, you should also consider what is applicable in each categoryPrivacy Policy.

When collecting data, you can start using initial categories such as "sensitive data," "internal data," or other labels that will help you decide how to manage or process the data in later stages. storage

Data storage best practices depend on your usage. The collected data may become a live asset and be used or reused, or it may become inactive and be archived or deleted. In both scenarios, you must set storage policies. It is also important to consider the backup and restore options.

As an example of these policies, you may choose to store inactive data that may be relevant in the future in acold storage environment(which would help reduce costs).

3. Data maintenance

The data curation phase involves multiple processes, including reviewing and enriching the data before making it available to the appropriate users.

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The overall goal in this phase of DLM is to ensure relevant data is available to the right team when and where they need it. So after you validate and enrich the data, you need to move it to the right place. That is wheredata integrationForward.

Data integration is perhaps the most complex and important part of data maintenance. In some cases, you can use anative or in-app integration solution. If this is not available for your applications, you need oneIntegration Platform as a Service (iPaaS)or other third-party solution.

Some companies, especially for accounting and investment decisions, add another step after this synthesis.

As your database grows, you continuallyCRM Data Maintenanceit is necessary to ensure that the data can be used for sales and marketing initiatives. Without ongoing maintenance, you'll find that data quality issues can quickly explode and affect everyone who relies on customer data in your organization.

4. Data usage

This is where data plays a role in business decisions. In the earlier phases, the data was collected, organized, reviewed, and moved to the appropriate platform. At this point, it should be easy for administrators or stakeholders to find this data and Make decisions based on information.betrays.

Part of this phase is the publication of data. Creating protocols for data disclosure is important, especially for companies that share information outside of the business environment. An example of a data publication policy could be a set of rules for sharing reports with partners or clients.

5. Data cleaning

The data sanitization phase includes delete, sanitize, destroy, and archive. Your data is growing every day and storage is quite expensive. Because of this, it's a good idea to delete or delete data from your databases when it's no longer useful.

For inactive data that might be useful in the future, you can also create policies for how it's archived or separated from active data.

All removal guidelines are part of the data cleansing phase.

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To learn more about data cleansing and the other stages of DLM, please watch this video fromprojector:

Benefits of Data Lifecycle Management for Businesses

Just by looking at the DLM stages, you can visualize the data moving through your organization to create a data management plan. Implementing a DLM framework can provide additional benefits, such as:

  • We help you comply with data retention regulations and requirements. Each industry has its own regulations when it comes to data. There are also local or regional personal data protection laws that may apply.
  • ensure efficiency. You have access to relevant information at the right time. When you implement DLM, you set the standards for automating data validation, enrichment, and integration.
  • provide security. At all stages, consider the most secure methods of data management. It also creates contingency plans for data storage in case of an emergency.
  • Increase the value of your data. Having quality data and ensuring its integrity makes data a much more valuable asset to your business.

How can data lifecycle management help small businesses?

In general, the benefits of DLM can also be applied to smaller businesses. Creating and implementing all these guidelines and automation processes may seem like overkill when you're running a very small business. However, it's never too early to consider the phases of DLM and create a data management plan that can grow with your business.

On a smaller scale, you can consider the following actions for the different phases of the DLM:

  1. For data collection:Use web forms for relevant content.
  2. For data storage:Put cold or internal data in cloud-based storage services.
  3. For data maintenance:Use iPaaS tools orintegration platforms.
  4. About the use of data:Create documentation to help stakeholders locate data.
  5. For data cleaning:Set up a regular process to remove unnecessary data.

Getting started with DLM

As your business grows, so does the size and complexity of your data. Regardless of the size of your organization or the IT infrastructure you manage, creating a DLM-based framework allows you to visualize the complete journey of your data throughout the organization.

A complete picture of your business data helps you identify vulnerabilities where policies are needed to protect your data. At the same time, you can make the most of your data to make informed decisions for your business.

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