What Is Data Lifecycle Management? A Complete Introduction

Do you know the biggest challenge companies face? It’s maintaining a large information base. Too much information compromises data flow. But it’s important to manage the information at hand. A company has to succeed in managing data well. Only then can the employees make the right decisions. That’s where data lifecycle management comes in. What is data lifecycle management, you ask?

No matter how important data is, it loses its relevance over time. After all, no information can hold the same relevance for eternity! You have to manage the data to make the most out of it. But before we you can do that, it’s important to understand the data lifecycle.

In this post, we’ll take a detailed look into data lifecycle management (DLM), including the different phases of DLM and best practices. We’ll also highlight the importance of managing the data lifecycle in a company. So, let’s dive into the details.

What Is Data Lifecycle Management?

Before getting straight into what DLM is, we’ll first review the definition of lifecycle. A lifecycle is something we all learned about in first grade! A lifecycle of an entity describes various stages from its creation to the end of its lifespan. The same goes for data in software companies. A data lifecycle is a sequence of different phases a data unit goes through. The starting phase is the initial generation of data and its storage. After that, the lifecycle continues until the time the data becomes obsolete.

Data flows through the information system. This data flow happens in various stages of the data lifecycle. DLM is an approach that manages this data flow. DLM includes policies that ensure proper sorting of data into different tiers. It also enhances the automation of data migration.

Why Do Companies Need to Manage the Data Lifecycle?

If your company works with internal as well as customer data, you need a DLM policy. Without a policy, a situation may arise when you’re unable to find critical data when you need it most. In the worst case, your company may have to deal with the loss of important data, thus leading to lawsuits and penalties from customers. A good DLM policy ensures that your employees always keep backups of important data. It also ensures that your employees follow data security guidelines and archive unwanted data.

The data security measures in a DLM strategy assure clients that their data is in safe hands. For developers and testers as well, they’ll always have access to useful and clean data, and the data is readily available for their work. Above all, a sound DLM strategy makes sure that in case of risk or emergency, the data will stay safe.

All clear about why your company needs a DLM policy? Then let’s discuss the seven stages of DLM next.

Phases of Data Lifecycle Management

The data lifecycle has a total of seven phases. Each phase has different features. Let’s take a look.

1. Data Capture

A company obtains new information in the data capture phase. The data can be in text or image form. There are certain data that have never existed in the company. The data capture phase marks the creation of data that doesn’t exist yet. There are three main ways of capturing data. They are data acquiring, data entry, and signal reception.

2. Data Maintenance

Once a company has captured data, it’s important to maintain it. Proper data maintenance helps the staff carry out different processes. This phase makes accurate data available in real time. You can use this data in development as well as testing. Some actions in this phase include purging, integration, or enrichment.

3. Data Synthesis

Data synthesis uses other data as input to create data values. This phase uses inductive logic. In inductive logic, the results obtained from several sample data are combined. The aim is to come to a conclusion. For example, the creation of credit scores is a field using inductive logic where the algorithm calculates the score based on your bill and loan payment records. Moreover, using inductive knowledge calls for a certain level of expertise in inductive logic programming. Some areas of use include accounting, risk modeling, and investment decisions.

4. Data Use

Enterprises need some data to run and manage properly. The data use phase includes the application of data to tasks related to management and development activities. However, in this phase, the firm has to understand the legal aspects of using data. This is where data governance comes into play. It enables firms to take legal constraints into account. Moreover, it also dictates how firms can use the data.

5. Data Publication

The data publication phase includes the use of information outside the business environment. For instance, a vendor sends its client’s monthly statements via your app. A word of caution for all firms! Ensure that all the data is correct before it goes out of the enterprise. Once the data is out, you can’t correct it. This is because the information is now out of reach for the firm.

6. Data Storage

Data storage is a phase that occurs as the data lifecycle is ending. This process takes place without any data processing. Active production environments contain a lot of company data. This phase awaits either the restoration or deletion of that data.

7. Data Purging

There always comes a time when certain data becomes obsolete. At this stage, it’s the responsibility of the firm to delete the data. Moreover, the company has to make sure that data purging covers every copy of the data. This means no copy should be visible after deletion.

Best Practices of Data Lifecycle Management

Now, let’s discuss some best practices that will enhance DLM.

Follow a Naming Convention for Your Files

Often, we get lost among the vast ocean of data while searching for a certain file. This is because we don’t know what to search for. You must follow a structure or some standard to name your files. This will enable you to search for the file easily in the database.

Define the Data Types

Your company most probably works with different types of data. The data may be in the form of text files, XML, or media files. It’s very important that you store the data in different locations based on type. For instance, suppose you have an application like Lenskart. You should handle the eye checkup reports of a customer differently than their purchase details. Easing the way of managing different data types will lead to smooth DLM.

Archive Rarely Used Data

Data that you rarely use does nothing except occupy unwanted space in the database, ultimately leading to a longer search period. For instance, let’s talk about the Lenskart app we discussed above. Suppose you have records of a customer who made their last purchase years ago. Follow an archiving principle in your data management process. Archive their data in a way that it doesn’t interfere when you’re working with fresh and frequently used data.

Create a Backup Plan

Ever wonder what to do if data is lost because of hardware issues or accidental file deletion? Invest in software that creates data backup. The backup data ensures that in case there’s a loss of some important data, there’s a way to retrieve it. Also, create a data archive policy. The policy will allow your employees to store unused or redundant data instead of deleting it. Thus, if you need that data, you can get it from the archives.

Apart from the best practices we discussed above, focus on creating a DLM policy for your company. The policy should contain proper guidelines for storing, creating backups, and archiving data. Share it with your employees. Make sure that your employees follow these policies. This will keep your data safe and ensure a smooth data lifecycle.

Reach Great Heights With a Killer DLM Strategy!

As we sum up, we might say that DLM can seem abstract at first. It’s true that there is no one-size-fits-all approach. However, there are some vital takeaways every company can use to their benefit. You can create a deployment process that is cost-efficient. You can also prevent rollbacks and create a reliable app, thereby increasing the client’s trust in your company. Having a sound strategy for implementing DLM isn’t optional. If a company wants to increase efficiency and agility, DLM is a must. Once you have proper risk management in place with DLM, you can unleash the true potential of your business and reach the pinnacle of success.

This post was written by Arnab Roy Chowdhury. Arnab is a UI developer by profession and a blogging enthusiast. He has strong expertise in the latest UI/UX trends, project methodologies, testing, and scripting.