Data Lifecycle Management: All You Need to Know in 2021

The three main goals of data lifecycle management are to adopt a policy-based approach to managing data and information flow. It is closely tied to information lifecycle management approaches in order to ensure that all data passes through a set of stages. The three basic aims of data lifecycle management are availability, confidentiality, and integrity. All are critical in the management of information systems. Three main goals of data lifecycle management will have to be fulfilled at all times.

What is data lifecycle management (DLM) and how does it work?

“A policy-based method to regulating the flow of an information system’s data throughout its lifecycle, right from creation and initial storage until the point when it becomes obsolete and is deleted,” according to TechTarget. DLM refers to an organization’s attempt to manage its data through various strategies, processes, and DLM technologies.

While businesses recognize the value of data, safeguarding, conserving, and managing it is a completely different story. Complicated rules, as well as the fact that data today sits in various locations – on-premises, in the cloud, in remote workforce computers, and on SaaS platforms — exacerbate the problem.

Businesses will be able to keep up with continuously changing legislation and eDiscovery requirements if they have a solid DLM strategy in place. DLM enables businesses to get the most out of their data. It also gives them more control over their company’s data, assists with archiving, decreases IT workload, lowers storage costs, and allows for faster decision-making and recovery during a crisis. Three main goals of data lifecycle management can be explained as Data Storage and Security, Data Availability, and Data Resiliency.

Difference between data lifecycle management (DLM) and ILM

Because both are policy-based approaches to managing data, data lifecycle management and information lifecycle management (ILM) are sometimes misconstrued as synonyms and used interchangeably. These conceptions, on the other hand, are developed for various purposes and have some significant variances.

The flow of data from one stage to the next — from data collection or generation through deletion or reuse — is the focus of DLM. DLM is concerned with determining when specific data should be erased, whereas ILM is concerned with the information’s relevance and accuracy. Data files are managed by DLM products based on their kind, size, and maturity. They enable organizations to search the stored data for a specific sort of file from a specific time period. ILM tools, on the other hand, go beyond that, allowing organizations to quickly search multiple sorts of files for a specific piece of information, such as a customer’s email address.

While ILM is sometimes regarded as a subset of DLM, both DLM and ILM are critical components of any data protection plan. There are many reasons why businesses in the USA would want to implement processes of three main goals of data lifecycle management.

What’s the role of data lifecycle management model in smart cities?

When it comes to managing the vast amounts of urban data that smart cities are responsible for, they encounter huge hurdles. These data come from a variety of sources, including waste management, urban habitation, pollution levels, the chance of crimes being committed, and so on, all of which are historically independent and considered “data islands.”

All of this information, as well as some additional private information such as medical records or statistics on district mobility, is stored in databases and hybrid clouds that are linked together by instances.

Because it introduces secure and anonymous data supervision, this adds a layer of complexity to urban policies. To put it another way, the DLM flow must protect itself from cybercriminals. Data Storage is one of three main goals of data lifecycle management.

What are Three Main Goals of Data Lifecycle Management?

Organizing and managing data is a difficult task, and the amount of data grows every day, making these tasks even more difficult. In fact, you might be surprised to learn that the number one challenge that businesses confront is a lack of security. Data breaches occur frequently as a result of the collection and expansion of data.

We understand the value of data to a business. It is so powerful that it has the ability to either bring a whole organization to its pinnacle of success or entirely destroy it.

It is here that three Main Goals of Data Lifecycle Management may make a significant contribution by successfully handling and maintaining data throughout its lifecycle. In fact, the DLM’s key goals serve as the foundation for the data flow that is both freed and streamlined.

Although the importance and requirements of data lifecycle management vary for every company, it has three major objectives that must be met at all costs. So, without further ado, here’s a quick rundown of the three main goals of data lifecycle management (DLM) to give you a better concept of what they are:

1. Data Protection and Confidentiality

Data Protection and Confidentiality are one of the three main goals of data lifecycle management. The need for, amount of, and use of data is growing exponentially with the passage of time. As a result, the data is granted the status of “modern money.” In other words, the better organized and protected your data is, the more likely you are to succeed and leave the competition behind.

However, as the amount of data grows, it becomes more difficult to manage and its confidentiality becomes more vulnerable. The data is so powerful that if it falls into the wrong hands, it may bring down an organization’s entire infrastructure.

As a result, the most important element that the company must consider is how to manage data as securely and effectively as feasible. So, what can be done to ensure that no data is lost and that it is managed properly?

This is how the three Main Goals of Data Lifecycle Management work their magic, turning the tide against the invaders. Why?

It has the ability to elegantly organize and secure data, ensuring that no data loss, theft, corruption, or viruses occur. What makes it possible? It has made its laws so severe that no one from the outside can even get close to it, let alone alter it.

All of the structured and well-organized data is saved in a database and on a cloud-based server here. Unstructured data, on the other hand, is saved in a file or on a cloud server.

That is why data upkeep and secrecy are the most important goals among the three Main Goals of Data Lifecycle Management. Because without it, no data can be used properly, and even if it is, it will be unprotected.

2. Accessibility

Accessibility is one of the three main goals of data lifecycle management. So, now that your data has been properly kept and secured, what’s next?

Nobody will want to use data that is poorly maintained and secured if it is difficult to access. We prefer to use goods that are rapidly and easily accessible in this fast-paced society, rather than those that are slow and difficult to obtain.

Furthermore, what if the relevant data isn’t found and can’t be used when it’s needed? The end consequence will surely be disastrous. Why? Because many other processes rely on the data from the prior phase, their functionality is being disrupted. What could possibly be more terrifying than such a terrifying nightmare?

It’s no surprise that access is the second most important purpose of data lifecycle management. Data accessibility is made considerably easier and faster with the help of DLM. That person enjoys himself and receives the information he or she requires at any time.

The ease of use and accessibility of the data is the next element that a business must consider. Otherwise, if precautions are not made now, such organizations would fall behind others over time and may find it difficult to exist and cope with others.

3. Consistency and Flexibility

Consistency and flexibility are one of the three main goals of data lifecycle management.  Along with the rise in volume, the data is constantly updated, necessitating changes at all times to stay up with changing trends. Furthermore, multi-user environments have become so well-known that it is difficult to find anyone who hasn’t used one.

As the number of users grows, more and more instances of data are produced every second and accessed by multiple users at the same time. This type of occurrence might result in data being duplicated in multiple areas with minor changes. These minor adjustments, however, are enough to perplex a user. It has the ability to persuade a user that the data provided by these organizations is unreliable and that it is, therefore, best not to utilize them. Obviously, you do not want such a traumatic event to occur in your company.

Perhaps this is why the third key purpose of data lifecycle management (DLM) is to ensure that data is flexible enough to accommodate any change while also being dependable to use because changes are reflected quickly everywhere.

Advantages of Data Lifecycle Management for companies

There are many reasons why businesses in the USA would want to implement processes of three main goals of data lifecycle management. The following are some of them:

  1. Governance and Compliance

Keep in mind that each industry sector has its own data retention requirements, and having a solid DLM policy in place can help firms stay compliant. Three main goals of data lifecycle management are essential in information systems management.

2. Data Security

A solid DLM strategy includes redundancy, which ensures that data is safe in the case of a disaster. It also assists in preventing client data from being duplicated in different portions of a data architecture where security may be an issue.

3. Strategy for ILM

DLM is the cornerstone of ILM. Businesses in the United States must first have a working DLM strategy that pulls data through the lifecycle before they can completely implement an ILM strategy that keeps data current and secure. Three main goals of data lifecycle management are essential in information systems management.

4. Efficiency

Keep in mind that better efficiency is at the heart of every IT solution. When DLM and ILM are correctly integrated, users have access to clean, accurate data that is easily accessible. This process is aided by automation. All of this aids firms in becoming more agile and efficient. The three main goals of data lifecycle management are data strategy, which is becoming increasingly popular in organizations.

What are Data Lifecycle Management Stages?

There are numerous varieties of DLM because each firm has its own unique business model, tools, and data kinds. Most firms, on the other hand, normally follow these six stages:

  1. Data Gathering or Data Creation

Data Gathering or Data Creation is the first point of the data lifecycle, in which a new data value enters an organization’s information systems, either by leveraging current data created within the company or by receiving signals from various devices such as the Internet of Things (IoT). The information gathered or created could be structured or unstructured.

Organizations can classify data based on its file type, such as private, sensitive, internal, public, and so on, at this step to determine how the data will be processed/managed in subsequent phases. Three main goals of data lifecycle management will have to be fulfilled at all times.

2. Data Management and Storage

It’s critical to securely store and maintain data hygiene once it’s been created or collected. To ensure that data is kept throughout its lifecycle, a complete data backup and recovery mechanism should be in place. To prevent data tampering, appropriate security measures must be employed. 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. Three main goals of data lifecycle management can be explained as Data Storage and Security, Data Availability, and Data Resiliency.

3. Application of Data

One of the most crucial stages in the data lifecycle is this. Businesses can see, handle, alter, and store data at this point. This stage entails utilizing data for a variety of organizational goals, including decision-making and analysis. There are some data governance issues that come with data consumption. Understanding whether or not an organization can use specific data in the way it wants can have legal ramifications, for example. There are many reasons why businesses in the USA would want to implement processes of three main goals of data lifecycle management.

Data Sharing vs. Publication: Which is Better?

Employees, consumers, stakeholders, and other authorized users are all given access to data at this point. Because data is shared both internally and externally outside an organization for purposes such as marketing and advertising, this stage is one of the most vulnerable in the data lifecycle. Data Storage and Security is one of three main goals of data lifecycle management.

  1. Archiving of Data

Data archiving is the process of keeping a copy of data that isn’t commonly accessed or used but needs to be kept for legal and investigation purposes. Archived data can be restored to a live production environment if necessary. Data should be archived when, where, and for how long, according to an organization’s DLM plan. Data Availability is one of three main goals of data lifecycle management.

2. Data Reuse vs. Data Deletion

It is difficult to store all of the data generated every day, which amounts to more than 2.5 quintillion bytes. Storage costs and regulatory regulations add to the difficulty. As a result, companies must remove data that is no longer needed in order to free up storage space for active data. When data surpasses the required retention time or no longer serves a relevant function to the organization, it is deleted from archives during this phase. Data Resiliency is one of three main goals of data lifecycle management.

What’s the difference between hot, warm, and cold data?

Data can be categorized using a multi-temperature scale throughout its lifespan. Hot data is often accessed data, warm data is less frequently accessed data, and cold data is the least commonly accessed data. Data classification is usually determined by business regulations and can differ from one firm to the next. Three main goals of data lifecycle management are essential in information systems management.

  1. Hot data is information that is needed to carry out day-to-day company operations and is often accessed. The data must be optimized for quick access by storing it on Tier 1 type storage, which is the most expensive phase of the data lifecycle.

2. Warm data is data that is accessed infrequently but is required to be available to comply with certain business policies and legal requirements.

3. Cold data is data that has served its commercial function but has no intrinsic value to the company. In most cases, cold data is archived or erased.

Conclusion

In the digital age that we live in, data has become the new money. As a result, data management has become a serious factor of the data treatment, giving growth to the idea of data lifecycle management.

All businesses, large and small, handle their own data, and some choose to use services to store data on the cloud. But, in the end, no one can avoid it, lest they become unsustainable in today’s extremely competitive market.

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