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What is Data Lifecycle Management?

Data Lifecycle Management is crucial, especially for organizations providing data-related services to their customers. As an IT professional, you must know how to define this and track all the actions that data undergoes, from creation to destruction.

Credit: Lukas/Pexels

Defining Data Lifecycle Management

Managing the various stages of a piece of data’s life, from creation to destruction, is known as Data Lifecycle Management (DLM). There are many steps and eras throughout the data lifecycle, and you must be able to manage every step to have proper control of it. 

It is an essential management policy for companies that could face data breaches and system failures. Data Lifecycle Management can help you recover the data lost during a cyber-attack or a system failure since you protected your data by backing it up. 

According to research, numerous versions of varying complexity exist of an abstracted data management or data lifecycle. However, they all share certain basic aspects. These generally include, but are not limited to: 

  • Planning, 
  • Collecting, 
  • Quality Control, 
  • Documenting, 
  • Preserving, 
  • And using / reusing data.

Data Lifecycle examples: Let’s look at it step by step 

The data lifecycle process can be divided into six pieces. It starts with creating data, and the next steps are storage, processing, usage, sharing, archiving, and deletion

Data creation

The data creation step could be manual entry or collection/acquisitions from external resources – such as SaaS applications. After creating or acquiring from external sources, you store that specific dataset and secure it using different security procedures

Data processing

Data can be processed manually, semi-automatically, or automatically. Organizations can use this processed data for interpretation, analysis, and more.

Data analysis

Once your data is processed and ready for analysis, you can use/reuse this dataset for analytics, artificial intelligence (AI), machine learning (ML), and more.

Data sharing

It can be shared with other systems or data warehouses, or people for numerous purposes.

Data archiving

After you are done with using your data, you can archive it – as you might need it later. Data could always be restored when archived, but it doesn’t need to stay in production environments forever. This process happens constantly, and new data is acquired or manually entered continually – thus, archival of data is required for optimization.

Data deletion (destruction)

The last step of Data Lifecycle Management is the deletion or destruction of data. After some time, old data can be deleted from the database as there isn’t enough space to occupy your database with unlimited data. Not to mention, it is vital to adhere to best practices around retention policies for supporting compliance. 

Credit: Lukas/Pexels

Data Lifecycle Management goals

Data Lifecycle Management has three goals: confidentiality, integrity, and availability. These goals create the abbreviation: the CIA Triad. To learn more, check out this article about the CIA Triad.  


Confidentiality is one of the most important aspects of Data Lifecycle Management. It guarantees that private data is shielded against access and exposure by unauthorized parties. Privacy is the number-one issue that data-centric companies take extreme caution with. If an unauthorized party acquires data, the outcomes could be disastrous. 


The second goal is integrity. For ethical and factual reasons, organizations should not alter or change data unless it is absolutely necessary and that data is a piece of public information that wouldn’t cause any issues. On another note, if data has to be updated, then the company should be able to assist the users with the most up-to-date and accurate information. It helps to prevent unauthorized changes to data and make sure that it is authentic.


Availability is the last Data Lifecycle Management goal as it helps users to access up-to-date and accurate data under privacy. Data should be accessible to authorized individuals when it is needed. 

How does Salesforce data fit into DLM?

Salesforce is a CRM platform that constantly and continually creates a healthy data stream of customer data – a treasure trove of data that needs to be collected and protected. By collecting and storing data, organizations can use this CRM dataset for analysis alongside satisfying their security and backup/restore needs for a complete DLM. But to put it into a better context, let’s listen to Salesforce itself:

An asset without lifecycle management represents a product that a customer has bought, but the information stops at the time of sale. By contrast, a lifecycle-managed asset shows information about a product such as a subscription or a warranty after it’s sold. Customer Asset Lifecycle Management is driven by a custom automated process. For each lifecycle-managed asset, you see information in a dashboard and on related pages in Salesforce

Help page of Salesforce

Data Lifecycle Management framework

A Data Lifecycle Management framework is a methodical approach to control every step of data throughout its lifetime. As mentioned above, there are different stages in the data’s lifecycle; each is included in its framework. Creating or obtaining data, storage, processing, usage, sharing, archiving, and deleting are the steps that build the data lifecycle. These steps are the essential points for data to complete its lifecycle.

Data Lifecycle Management policy

Nowadays, most companies, websites, and other publications collect and store our data. A Data Lifecycle Management policy gives information on guaranteeing that data is handled reliably, securely, and effectively throughout its lifecycle as a valued asset. These policies include the needed information, especially on data storage and essential information.

Best Data Lifecycle Management tools

The industry keeps growing, and multiple Data Lifecycle Management tools are announced constantly. However, experience and innovation are the names of the game when it comes to customer experience and the CIA Triad of services. 

GRAX is a Salesforce Data Lifecycle Management Platform serving the community since 2017. With the experience and expertise, GRAX’s team holds, your Salesforce data lifecycle management needs will be fulfilled without trouble. Try GRAX today risk-free to see why Global 100 companies trust GRAX with their most valuable asset: their data. 

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