What Are The Three Main Goals Of Data Lifecycle Management (DlM)

What Are The Three Main Goals Of Data Lifecycle Management (DLM)?

Management 7 Mins Read September 29, 2023 Posted by Mashum Mollah

Question: What Are The Three Main Goals Of Data Lifecycle Management (DLM)?

Correct Answer:

  • Data Security
  • Data Availability
  • Data Confidentiality

The entire world is running depending on data. So there have been many discussions regarding data life cycle management (DLM). The trail from the origin of data to its removal has become a matter of concern these days. But do you have any idea what are the three main goals of data lifecycle management (dlm)?

I am sure your perception about this is not much clear; that is why you are here. Well, we would give you a comprehensive picture of the three main goals of data lifecycle management. With the increase in the amount of data worldwide, there is a dire need to co-ordinating it efficiently. This gives a sharp need to understand data lifecycle management goals.

Explanation Of Database Lifecycle Management (DLM)

In simple terms, Database Lifecycle Management is nothing but a methodology of managing data assets, schema, metadata, and DLM.

What are the benefits of DLM? Precisely, a systematic DLM facilitates organizations to store and regulate data resources as per several factors like cost, protection, performance, etc. Essentially, project design and intent are the starting point of DLM, while test, deployment, monitoring, maintenance, and building are the passing stages. Finally, it ends with archiving data. 

Now that you know what DLM is and some basic information about it, it?s time to know what are the three main goals of data lifecycle management (dlm)? So without any more ado, let?s dive right into it.

What Are The Three Main Goals Of Data Lifecycle Management (DLM)?

The Data Life Cycle Management Structure

If you plan to learn more about DLM, your initial query will be the three main goals of data lifecycle management (dlm). And why not? You are learning the basics, benefits, and stages of DLM, but why not the goals.

Indeed, the data lifecycle management goals are essential for getting a vision of its scope from a broader aspect. Data management is literally complex, and without definite objectives, you cannot streamline the continuous information flow seamlessly.

Check out ?what are the three main goals of data lifecycle management (dlm)?? below:

1. Data Security

Data Security

Data is the lifeline of the digital world. Do you want to search for something on Internet?- then the only thing on which you will depend is Data. Therefore, you must make sure that people are using it authentically. When the amount of data becomes high, the likelihood of data misapplies also increases.  

DLM enables organizations to safeguard their data from deletion, leakage, cyberattack, and loss. Wanna know how?- DLM basically enacts standards on how you should use, treat, and store data. Thus, it makes sense why data security is one of the data lifecycle management goals.

2. Data Availability

Data Availability

DLM is essential to make sure that data is available to the users as soon as they need it. Unavailability of data as and when required would disrupt a series of systems one by one. What can be the worst-case scenario? – failure of a single process would mess the entire process up.

Thus, one of the three main goals of data lifecycle management is always to keep the same data supply.

3. Data Integrity

Data Integrity

As we mentioned earlier, a particular database has an endless volume of data that numerous users use at the same time. With the advancement of machine learning and artificial intelligence, technologies like IoT, cloud platforms have come into popularity. 

Unfortunately, science is a blessing on one hand and a curse on the other. This abundance of using data in Omni-user environments may lead to plenty of errors, duplications, missing data, etc. 

Do you know what data scientists do at this time? With the help of DLM, they review and edit data from time to time. Of course, a plethora of highly complex tools is necessary for these actions. In this way, robust data integrity leads to data integrity and data authenticity for the long term. 

I hope you now got your answer regarding ?what are the three main goals of data lifecycle management (dlm)?? including their relevancy.

The Data Life Cycle Management Structure


Is your mind asking for more insights after learning the data lifecycle management goals? It is quite natural as the goals of Dlm are related to its structure.

So, get a sneak peek at the 6 staged models in the section below:

Data Capture or collectionIt includes gathering data from different sources. Technologies for acquiring data are Machine learning and the Internet of Technology (IoT). 
Data MaintenanceIn this stage, your process, maintain and curate data as per organizational needs. 
Data Usage This stage is for employing the processed data in different business activities. 
Data Release Data containing rich perceptions is released amongst the users for their use. Sometimes, some users get specific access to view a set of published data. 
Data Archival In the process from data collection to data publication, some irrelevant data is archived for future usage.
Data Removal In this last stage, the concerned team deletes unnecessary data considering no existing dependencies are left. 

Benefits Of Data Lifecycle Management   

Other than streamlining the information flow and data optimization throughout its whole lifecycle, Data Lifecycle Management provides several other benefits, which may include:


Some of the standards of industry compliance need organizations to retain data for a certain period of time. For example, the Criminal Justice Information Services Security Policy has stated that

?agencies shall retain audit records for at least one year. Once the minimum retention period has passed, the agency shall continue to retain audit records until it is determined they are no longer needed for administrative, legal, audit, or other operational purposes.?

DLM helps companies comply with both regional and local regulations and, at the same time, meet other needs like auditing, legal help, and other investigations.

Data Governance   

Companies rely on data that help them improve their business operations and make decisions that are informed and well thought out. A powerful data lifecycle management strategy is a constant preference and is readily available, reliable, consistent, and secure, and aligns perfectly with all the regulations of data privacy.

Data Protection   

When we talk about the landscape of threats that we face, data security is one of the top priorities of business giants and IT professionals. DLM helps companies protect their confidential data from the loss of data, cyberattacks, data deletion, and many other forms of cyber crimes. It allows businesses to determine how their data is treated, saved, used, and shared. This helps to minimize the risk of breaching data and prevent any vital information from falling into the wrong hands.

Value And Efficiency   

Businesses in this generation are data-driven. Data plays one of the most vital roles in accelerating the strategic initiatives that a company takes. Therefore, it is important for organizations to make sure that their data is clean, authentic, and up-to-date. A good DLM strategy looks after the fact that data that users can access is reliable and accurate, thereby letting businesses derive the maximum value out of their data. Data Lifecycle Management also helps in maintaining the quality of data throughout the entire lifecycle, which in turn allows for improvement in the process and increases efficiency.

Difference Between DLM And ILM   

There have been cases where individuals have gotten confused between data lifecycle management and information lifecycle management. The reason why they are quite often used interchangeably or as synonyms is because they are both data-managing policy-based approaches.

DLM only concerns the data flow from one stage to the other, which goes on from creation or collection to the deletion of data. While DLM operates on the data files, ILM goes beyond that and works with the information present inside the data files. While ILM is mostly taken as a subset of DLM, they both play a crucial role in protecting the data of an organization.

Frequently Asked Questions (FAQ):

Q1. Why Is Data Lifecycle Management Important?

As the data navigate through a medium of phases, a data lifecycle management strategy assigns value. That is why it marks itself as a critical factor in data management.

Q2. Do Data Scientists Need To Follow Data Lifecycle Management Goals?

Of course, data scientists and data engineers need to adhere to the DLM goals for the sustainable preservation of data.

Q3. What Are The Three Main Goals Of Data Lifecycle Management (dlm)?

The three main goals of data lifecycle management are data privacy, data integrity, and data availability. These are mandatory to make any organization function smoothly.

The Final Words

Data lifecycle management goals ensure that the piles of data in an organization or a group are being effectively handled. Without data, we are simply lost in darkness. So every kind of organization, irrespective of its size, must take the responsibility of storing, managing, and editing its own data.

We expect you to have thoroughly understood ?what are the three main goals of data lifecycle management (dlm)?? So if you are an aspiring data analyst, then you should not be having any more problems now understanding the fundamentals of DLM.

Let us know in the comment area if you are having any donuts regarding the goals of DLM. We will try our best to solve it. So stay tuned to our page to enjoy more intriguing reads soon.

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