Virtualization Technology News and Information
How to Optimize Data Security with Data Lifecycle Management

Data is your company's life blood. Every activity, process, and procedure performed to support and deliver service and products to your customers is built around data. As such, it needs to be protected from vulnerabilities using Data Lifecycle Management, or DLM. 

Many of these vulnerabilities are part of the data lifecycle. This dictates where data is stored: on-premise, cloud storage, with third-party providers, and so on. Furthermore, knowing where the data resides within the system allows you to take consistent security and data privacy measures.

Data is most vulnerable at five key points: collection, storage, sharing, analysis, and deletion. These points present the possibility of a data leak, and no one needs to be told how devastating such a situation could be. Managing these vulnerabilities should be a cornerstone of any organization's business strategy, not simply as something that separate departments carry out based on basic governance principles.

That's why an organization's executives should be in charge of the data lifecycle management plan, and it should be handled seriously.

What is Data Lifecycle Management?

Data lifecycle management (DLM) should not be confused with Data Loss Prevention (DLP). While DLP involves identifying and preventing exfiltration, unwanted destruction, and loss of sensitive data, DLM is far more comprehensive.

Data lifecycle management is a policy-based strategy concerned with data management for information systems. Management covers all stages, from data creation to storage and eventual deletion after use.

It is more than simply a procedure for controlling data from start to finish within an organization; it should be viewed as a comprehensive practice that your enterprise should apply to all the data you manage. DLM goals are targeted at improving data storage, availability, and resilience.

 Image showing the main goals of Data Lifecycle Management: data storage and security, data availability and data resiliency

Image Source

The following elements should be included in the Data Lifecycle Management process:



This covers governance, risk management, and ensuring that everyone involved understands how to handle data safely. For example, the organization and its clients must adhere to regulatory requirements, data integrity, security, and confidentiality standards, especially with a hybrid workforce.


The process should be officially defined in terms of data stages by the organization. Determine the data's RACI (the basic tenets of the Responsibilities Assignment Matrix, the acronym stands for Responsible, Accountable, Consulted, Informed), and set up data pipelines, and translations. Process controls (management, standards, and policies), activities, process enablers, and organizational assets should all be included.


This should be available to support effective and efficient data management at all data stages. The technology should make it possible to take a holistic approach to data management from all sources, including the cloud.

Analytics Metrics

Data should include key performance indicators, crucial success criteria, and goals connected to the desired outcome. This allows firms to refine their data and gain a better understanding of their limitations.


The process of analyzing data to aid decision support is known as the Research Data Lifecycle (RDL). Several firms have already turned to automation to complete certain data analysis tasks using artificial intelligence or machine learning solutions such as AWS sagemaker.

Legal Scale

Data must be governed, risks should be appropriately managed, and compliance procedures should be implemented. Personal Identifiable Information (PII) and sensitive data should be subject to stricter controls.


Data Lifecycle Management can be supported by third-party vendors, such as cloud storage providers. Almost anything, particularly capabilities that an organization lacks, can be provided as a service by vendors to help your organization save the time and cost of acquiring capabilities like cloud security.

This usually leads to greater productivity and a shorter time-to-market for your products. You should be vigilant about the cost of services and products over time, and consider the return on investment.

What are the Stages of Data Lifecycle Management?

There are several variations of DLM because each business has its unique model, tools, and data sets. Most firms, on the other hand, normally follow these stages:

Flow chart showing the data lifecycle 

Image Source

Data Gathering or Data Creation

This is the first stage of the data lifecycle, in which new data value arrives in your organization's information systems. Either by leveraging existing data created by the company, obtaining data created by external sources, or by receiving signals from various devices like Internet of Things (IoT) devices. The data gathered or created could be structured or unstructured.

You can categorize data based on file type, such as private, internal, sensitive, and public, at this step to determine how the data will be managed in subsequent phases.

Data Storage/Maintenance

It's important to securely store and maintain data once it's been created or collected. To ensure that data is kept safe throughout its lifecycle, a complete data backup and recovery mechanism with solutions like cloud computing should be in place.

To prevent data tampering, you must employ appropriate security measures. Data should be stored in a way that complies with all applicable laws and contracts. This stage isn't about extracting any meaningful information from the data.

Data Usage

This is among the most crucial stages in the data lifecycle. You can see, handle, alter, and store data at this point. This stage entails utilizing data for a variety of organizational purposes such as sending recurring emails.

Some data governance issues come with data usage. Understanding whether or not your organization can use certain data in the way it wants can have legal ramifications.

Data Sharing

Employees, consumers, stakeholders, and other authorized users are all given access to data at this point. Since data is shared both internally and externally for purposes such as marketing and advertising, this stage is one of the most vulnerable in the data lifecycle.

Data Archiving

Data archiving is the process of keeping a copy of data that isn't regularly accessed or utilized but needs to be kept for legal and investigation purposes. Archived data can be restored to a live production environment if necessary. The DLM approach for your company should specify when, where, and how long data can be archived.

Data Reuse and Data Deletion

It is difficult to store all of the data generated every day, which can reach staggering levels. Storage costs and compliance regulations add to the challenge. As a result, you must remove data that is no longer needed to free up storage for active data.

When data surpasses the specified retention time or no longer serves a relevant function for the organization, it is deleted from archives during this phase.

What are the Benefits of Data Lifecycle Management?

Infographic showing the various reasons why data management is important for organizations 

Image Source

Besides simplifying information flow and optimizing data during the lifecycle, DLM provides several other advantages, including:

Data Governance

Data is used by businesses to enhance their operations and make informed judgments. A good data lifecycle management approach ensures that your data is always accessible, reliable, and protected, as well as being compliant with data protection laws.

Value and Efficiency

Today's businesses are data-driven. Data is critical for your organization's strategic goals to succeed. As a result, you must verify that your data is secure, current, and authentic.

An effective DLM approach guarantees that data provided to users is valid and accurate, allowing businesses to get the most out of their data. DLM aids in the preservation of data quality during its lifecycle, allowing for process optimization and increased efficiency.

How Does Data Lifecycle Management Contribute to Data Security and Optimization?

In general, a system for protecting unstructured data should help your organization determine the value of available data, prevent data breaches while in use, and store that data in a long-term repository that protects it not only today but also in the future.

On the other hand, DLP (Data Loss Prevention) software requires a clear policy to identify sensitive data and lock it down by quarantining it.

Data lifecycle management takes a different approach to the problem. It aids an organization's proactive awareness of its data and the application of policies so that it can be secured while in use and throughout its lifecycle.

It includes features like data discovery, data classification, encryption, obfuscation, and defensible disposition, which allow the data to be removed once its commercial purpose has been achieved.

The value of information and the people who require access to it changes with time, and a data lifecycle management system understands this. A document's business relevancy window is roughly two years; for emails, it's closer to 90 days. While the value of a particular document may decrease, the need to keep it might not.

DLP responds to documents in transit in accordance with policies. It will react to activities in the vicinity of the document. When people are working on a project together, this might be an issue.

These kinds of hitches are avoided because data lifecycle management can be used to determine who can access a document. DLP can be useful, but you need a more comprehensive toolkit to make sense of the data you have and are producing to assess its value and safeguard it.

Furthermore, data lifecycle management enables modifications to access over time to accommodate the changing nature of your documents.

If it's the type of document that needs to be kept for compliance or legal purposes, it'll go into the long-term repository, where it'll be accessible to those who need past data, such as lawyers.

This repository has the potential to be your organization's single source of truth.

DLM can complement proactive solutions too, ensuring data security and optimization by helping you cross off items on your data integrity checklist.

Infographic showing the best ways to preserve data integrity 

Image Source

It will inspect, study, and analyze the data to discover value by identifying sensitive and low-value data. For instance, this could be anything from customers' details to a sample bookkeeping proposal. DLP alone cannot give such a complete approach to data security and optimization.

This viewpoint is incredibly valuable to a business. Especially as businesses must now analyze large amounts of data to comply with new rules such as the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR). You now have to cope with data in the petabytes.

Many businesses aren't sure where their sensitive data lies in this deluge of information. You need to identify it quickly and make a decision about whether or not to keep it. So, while you focus on outgoing marketing efforts: asking questions like "what is a brand ambassador program?", you should also consider your data security.

Master Data Lifecycle Management to Improve Data Security

Data Lifecycle Management is a crucial practice for any company. Holistic data management must not be left to chance but instead tackled head-on. Data trustees, data custodians, data stewards, data owners, and data managers are all vital roles that your company should take into account.

Failure to manage data effectively poses a huge danger to your organization and its clients. Data is growing at a breakneck pace, and its management cannot be ignored. Open structures and the desire to connect to information systems from anywhere provide a better experience since data is more available.

However, they also present additional cases of data security concerns, notably concerning illegal data exploitation. To nip these concerns in the bud, make data lifecycle management a company-wide strategic priority for your organization today.



Severine Hierso

Severine Hierso 

Severine Hierso is EMEA Senior Product Marketing Manager for RingCentral Office, the leader in cloud communications solutions, and is passionate about creating value, differentiation and messaging, ensuring a better experience for customers and partners. She has gained extensive international Product Marketing, Market Research, Sales Enablement and Business development experience across SaaS, Telecommunications, Video Conferencing and Technology sectors within companies such as Sony, Cisco, Cogeco Peer 1 and Dimension Data/NTT. Severine Hierso also published articles for domains such as Recruiterflow and CEO Blog Nation.

Published Monday, June 27, 2022 7:30 AM by David Marshall
Filed under: ,
There are no comments for this post.
To post a comment, you must be a registered user. Registration is free and easy! Sign up now!
<June 2022>