Data PrivacyData Lifecycle Management- A Necessity in the Privacy-Driven Era

November 16, 20220


Data Lifecycle Management (DLM) was introduced in the 1980s when databases first became widely available and mechanical punch cards and magnetic tape storage were replaced.

Data volumes increased as data storage costs decreased. Data duplication, poor data quality, security concerns, data backup, recovery hurdles, and the general chaos of too much data with inadequate file organization quickly became problems.

Big Data is transforming the way DLM procedures are developed nowadays. Companies are gathering an increasing amount of data so they can profit from big data analysis business advantages. The demand for DLM to efficiently manage all of this data and keep it secure has grown as a result of these massive data repositories.

DLM, in simple words, is a technique for handling data from the starting stage of data entry to the end of the data deletion. Data is divided into phases depending on various criteria, and as it completes various tasks or satisfies particular needs, it advances through these stages.

Organizations can now plan for dire repercussions in the event of data breaches, data loss, or system failure by having in place the DLM Policies and Procedures. Data protection and disaster recovery should be given top priority in a Data lifecycle management strategy, especially given the influx of harmful actors into the market brought on by the increasing growth of data.

This will prevent some catastrophic consequences on a brand’s bottom line and overall reputation by having an effective data recovery plan in place in the case of a crisis.


Organizations today are becoming more data-driven. Every part of their business, from attracting clients to developing new goods, depends on the data. The ever-growing volumes of data from several sources, including consumers, financial transactions, marketing campaigns, the cloud, mobile apps, emails, text messages, social media, and Internet of Things (IoT) devices, are what fuel the data-driven approach to business.

It can be difficult to fathom having access to all the information when needed taking into consideration the fact, how much data organizations collect every day. The administration of data across its full life cycle has become simpler due to the introduction of DLM.

For any organization, managing this information is now essential. Whether it’s from one department to another within an organisation or back and forth with a client, data may flow in many different ways. Most organizations require a data lifecycle strategy to manage all of these many data streams. This strategy helps to protect the security of the data while ensuring that it is the most accurate and up-to-date version possible.


There are a number of problems that organizations face as they expand and gather data, thus it is essential that the data be adequately maintained throughout its existence. The following are the top three objectives for data lifecycle management:

1. Data Security and Confidentiality

The core objective of DLM is data security. Data must be securely preserved after collection in order to prevent its misuse. Unstructured data is often kept on file servers or in the cloud, whereas structured data can be kept in on-premise databases or the cloud.

The data must be protected against theft and illegal access regardless of where it is kept. DLM helps to protect data from being accessed by threat actors and other unauthorized users, or from being damaged by malware and other viruses, by developing procedures for managing data from the point of creation to the point of deletion.

2. Data Availability

While preventing access to data by certain users is one of objectives of DLM. It is also crucial to make sure that the appropriate people have access to data at the appropriate times. Numerous procedures and processes might be stopped or fail if someone has wrong or inappropriate access to certain data.

Additionally, processing and visualization of data as needed by the company are included in availability.

3. Data Integrity and Confidentiality

Due to updates and cleaning procedures, data might change over time as it matures. The same data may be present in many places in marginally different formats as a result of such operations, which is known as data sprawl.

As a result, a procedure must be established to guarantee the reliability and integrity of data. Maintaining data integrity entails creating and storing only the most recent, high-quality data in your database.


Every organization has a unique method for storing, managing and erasing data. The process of DLM is as follows:

i. Creation of data

Data generation entails gathering data, determining its purpose, categorizing it, and deleting superfluous data.

ii. Handling of data

Processing, combining, aggregation, categorization, and data selection are all parts of data management.

iii. Data removal

The information included in the datasets is removed from the system during the data deletion phase of the data lifecycle process.


1. Generate and Collect Data

Data acquisition and capture occur at the beginning of the cycle when an organization obtains new, vetted information, including creating data internally, purchasing third-party data, and collecting data as it streams from apps.

 An organization often produces data in one of three ways:

a. Data acquisition is the process of getting already-produced information from sources outside the organization.

b. Data entry is the manual entering of fresh data by organization staff.

c. Data capture is the collection of data produced by equipment used in a variety of organizational procedures.

2. Store and Manage Data

 In DLM, data storage refers to the use of data redundancy and security measures on active data (as opposed to inactive data that is archived), as well as the storage of data in a manner that prevents inadvertent modification.

Contracts and legal requirements must be complied with when storing data that may entail keeping content solely on servers or keeping backup copies exclusively on discs that are encrypted.

At this time, only designated people should have access to the stored data. The sensitivity of the material is frequently specified, with options like private, sensitive, limited, or public. This safeguards the intellectual property of both, the organization and its clientele.

Planned data recovery is also managed at this step. Organizations should have a strategy in place in case of failure, such as a temporary backup on a hierarchical storage management system so that users can still access the data.

3. Data Usage and Sharing

 Data are utilized to support organizational operations throughout the consumption phase of the data lifecycle. It is possible to examine, process, modify, and save data. To guarantee that all data updates are completely traceable, an audit trail should be kept for all essential data. Additionally, data may be made accessible for sharing with others outside the organization.

DLM provides organizations the ability to specify who can use the data and for what purposes. Once the data is made public, a variety of investigations can be performed on it, ranging from straightforward exploratory data analysis and data visualization to more complex data mining and machine learning methods. All of these techniques are used in business decision-making and stakeholder communication.

Furthermore, data utilization is not always limited to internal purposes. The data could be used, for instance, by outside service providers for marketing analytics and advertising. Daily corporate activities and processes, such as dashboards and presentations, are examples of internal usage.

4. Data Archival

Data archiving is the process of transferring data to a location where it is kept in case it is ever needed in an active production environment again and removing that data from all current production settings. It is simple storage of data without any maintenance or broad use. Such data can be recovered, if necessary, in a working environment.

This stage involves an archiving method that guarantees redundancy for the data. For organizations that frequently store big amounts of data and occasionally need access to it, active archives are the perfect storage option. Inactive data is archived so that information can be accessible for ad hoc reporting and analytics, it can be saved on discs that are not connected to the network.

DLM strategies specify where, for how long, and under what conditions data may be kept. The specifics change depending on the data. Such data can be something that is subject to legal restrictions, including medical, scientific, or personal data or perhaps may be used as internal corporate records.

5. Data Destruction

The amount of preserved data will surely increase, and while the organization might desire to keep all data indefinitely, it is not practical. Data destruction is compelled by storage costs and regulatory concerns.

The removal of all copies of a data item from an organization is data destruction. Usually, it is carried out from a storage facility for archives. During this stage of the lifespan, the hardest part is making sure the data has been properly removed. It is crucial to confirm that data items have outlived their mandatory regulatory retention time before discarding them. Data is retained or deleted at the end of the life cycle.


Data is the heart of a company, and in this data-driven world, it is crucial to develop an effective DLM plan. An efficient DLM strategy may assist organizations in a number of ways, including:

  • Process optimization: Data is a key component of what drives an organization’s strategic efforts. DLM aids in preserving data quality throughout its lifespan, allowing for process optimization and boosting productivity.

Organizations may maximize the value of their data by implementing an effective DLM strategy that guarantees the accuracy and dependability of the data made available to consumers.

  • Cost management: A DLM process gives data value at every stage of its lifespan. Organizations can employ a variety of options, including data backup, replication, and archiving, to cut expenses after data is no longer usable for production situations. It might be transferred, for instance, to less expensive storage that is on-site, in the cloud, or in network-connected storage.
  • Data usability: A DLM approach enables to the creation of guidelines and practices that guarantee all metadata is consistently marked, enhancing accessibility when required, data value is ensured for as long as it needs to be preserved by setting enforceable governance principles. The availability of accurate and valuable data improves the speed and effectiveness of business operations.
  • Governance and compliance: Every organization has its own policies and guidelines regarding the retention of data, and a strong DLM strategy aids companies in maintaining compliance. While ensuring compliance with data protection rules involving personal data and organizational records, DLM enables enterprises to manage data more efficiently and securely.


DLM is essential to today’s businesses. More and more organizations are now aware of how important it is to stay on top of the constantly evolving data management standards, which may be very difficult in today’s fast-paced technological environment. The fact that data is frequently unorganized is perhaps one of the biggest problems businesses have with data management.

Through DLM, an organization can make sure that data fragmentation and disorganization help in providing the ability to govern and utilize an organization’s data to its fullest potential. Organizations can safeguard information, save costs, and find weaknesses in their data technology ecosystem by using DLM.

DLM and supplementary software that warns and detect compromises in real-time should be seriously considered by almost every organization that handles private or sensitive data that has to be protected.

– Team AMLEGALS assisted by Ms. Ishita Jaiswal (Intern)

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