Since the appearance of information technology, creating databases has helped organizations manage and access necessary data appropriately. But the rapid development of the digital era means that companies are generating data volumes at an exponential rate. Today, it becomes a challenging task for businesses to collect, store, protect, maintain, and delete data efficiently.
Modern data environments are famous for diversity, distribution, complexity, and high costs required for managing them. Apart from that, IDC states that the “international datasphere” will reach the point of 181 zettabytes by 2025. That makes a data lifecycle management (DLM) strategy a crucial tool for businesses to generate and store their information correctly. But each effective DLM strategy needs to be sufficiently comprehensive. Below, we will deeply analyze data lifecycle management and consider the importance of data backup in the DLM strategy.
What Is Data Lifecycle Management?
Data lifecycle management serves as a policy-based approach used to manage the flow of your information system’s data during its lifecycle. It covers the creation and initial storage as well as the time when your data becomes obsolete and deleted. You may also consider DLM as the company’s effort to manage your information using various techniques, processes, and tools.
Of course, many businesses understand the importance of information. But the ability to protect, preserve, and manage it appears as a different knowledge. Another critical challenge is the complex regulations that companies must follow, along with the fact that information can exist in multiple places. Such places include on-premises, the cloud, or specific SaaS platforms.
In this case, the robust data lifecycle management strategy helps organizations stay updated with continuously evolving regulations. Besides, it provides them with greater control over the corporate data and enables them to meet archiving needs. Finally, DLM allows minimizing the burden on IT, reducing storage costs, enabling faster decision-making, and recovering from a crisis quickly.
What Are the Goals of Data Lifecycle Management?
While the advantages of implementing the DLM model are numerous, there is a need to define its main goals. So, let’s consider the three key goals of data lifecycle management below.
- Security. A crucial objective of data lifecycle management is to keep the information secure. You have to create specific protocols to manage data from creation to deletion. That helps prevent such information from being accessed by different cybercriminals or other unauthorized users. Also, that allows your organization to avoid the potential corruption provoked by malware or other infections.
- Availability. No doubt, making sure that certain users cannot access your data is important. But at the same time, it is vital to ensure the availability of this information for the required users at the right time. If not, many of your processes and workflows can interrupt or even fail.
- Integrity. Among the main goals of data lifecycle management is data integrity. It means that you create and store only the newest and the most high-quality information in your database. With no DLM, users will access, store, and work with outdated data versions.
What Are the Phases of Data Lifecycle Management?
All companies have their own business models, tech stack, and types of information. Thus, you can find multiple variations regarding the DLM framework.
For instance, during the online research, you will notice the difference in the number of phases. Also, the words used in the stages’ names can differ. For example, the first phase may refer to data collection, acquisition, or capture. That depends on how the organization’s data becomes a component of its database.
But generally, there are five key phases: collection, storage, maintenance, usage, and cleaning. So, let’s analyze them more thoroughly.
Phase 1. Data collection
Data collection is the DLM phase when new values enter your organization’s data infrastructure. Such infrastructure may be hardware or software. To make this stage successful, you have to create a set of rules for gathering information in standardized formats. Therefore, you can access and manage it later on. These specific policies must correspond to all types of data you collect, including employee and partner information, billing data, etc. Besides, you should take the existing data privacy regulations into account.
Phase 2. Data storage
The current best approaches to data storage rely on its use. The information you collect may serve as an active asset you can use or reuse. Also, such data may become inactive, so you can archive or delete it. But in all of these scenarios, you must provide storage-related policies. Another critical point to consider is the backup and recovery capabilities.
Phase 3. Data maintenance
This phase involves several processes like inspecting and enriching information before it becomes accessible to the relevant users. The main objective of data maintenance is to ensure the availability of the particular information to the right team at the right time. Thus, after the appropriate validation and enrichment, you should move this data to the correct place. Finally, this DLM stage can also cover data integration and data synthesis.
Phase 4. Data usage
During this DLM phase, information starts playing a role in your business decisions. Here, administrators and stakeholders can easily locate such information and make critical decisions depending on it. Among the parts of data usage is data publication. So, you have to establish relevant protocols to ensure the proper data publication, especially when sharing data outside your business environment.
Phase 5. Data cleaning
The last phase is called data cleaning. It involves deleting, purging, destructing, and archiving information since it is constantly growing, and storing such data becomes costly. Thus, when your information stops bringing value, the best option is to delete it from your database. But also, you can have inactive data that you will use in the future. In this case, you should establish specific policies on the ways of archiving or separating it from the active information.
8 Benefits of Data Lifecycle Management for Companies
Data serves as the core of each business. Thus, applying an efficient data lifecycle management strategy is essential in the modern data-driven environment. Organizations can obtain numerous advantages from the DLM approach, including:
- Consistent information quality. Thanks to the appropriate DLM, companies can eliminate data-related inconsistencies. Also, they can maintain a truly single version during the data lifecycle.
- Compliance. Data lifecycle management requires businesses to establish clear policies on how they should manage information. That allows for ensuring compliance with the existing industry standards and rules.
- Reduced risk. A DLM strategy enables organizations to reduce risks related to information governance, compliance, and security. They can do that by ensuring that all the company’s information assets are controlled thoroughly and managed consistently.
- Cost savings. With a well-planned DLM strategy, companies can save money since it helps reduce redundant efforts within their teams and departments.
- Enhanced client satisfaction. Data lifecycle management can improve customer satisfaction by increasing the speed and accuracy of the services provided.
- Increased efficiency and productivity. DLM enables employees to show more efficient performance since they obtain access to accurate information when they want.
- Streamlined processes. If businesses use a consistent method of managing information through departments, they find it easier to implement and control processes.
- Improved decision-making. Nowadays, companies increasingly depend on big data analytics for making quicker and more accurate decisions. With the DLM approach, the decision-making process is much better.
Why Do You Need to Support Data Lifecycle Management?
When you create files, their vulnerability becomes immediate. Thus, all files existing on your physical storage devices or computers are vulnerable to potential loss or deletion. Such situations may occur as the result of physical damages, natural disasters, viruses, or other different threats. But some data breach and loss scenarios may be caused by human error.
After defining your DLM practices, you should invest in the appropriate data lifecycle management backup. The best option is to choose a cloud-based solution and enjoy all the benefits of cloud backup. Other available backup methods require users to conduct and schedule backups. However, that may lead to skipping backup days. Therefore, to protect your information from loss and deletion effectively, you should invest in a constant cloud backup tool.
So, why are backups important, and why is implementing a cloud-based platform necessary? Firstly, this solution performs quietly in the background. It means that your organization can back up files you work on depending on changes made and documents created. Secondly, among the key data backup advantages is the opportunity to retrieve the required files anytime from anywhere. That helps eliminate permanent information loss. Finally, without this tool, your data may get lost or deleted, which will cause downtime or even financial penalties.
How Do Backups Support Data Lifecycle Management?
To start with, there is a need to define the meaning of data backup and recovery.
Data backup means making and storing copies of the organization’s information. Such data may cover application and product information, employee records, client documents, and intellectual property. What about data recovery - it is the ability to access the company’s copies and restore information if its original versions were lost or damaged. Thus, data recovery is the process of applying data backups for restoring the necessary information and getting back to business. It means that backup and recovery are crucial to ensure your company’s long-term viability.
As we previously mentioned, the cloud has already become the most popular destination for backing up corporate data. One of the key advantages of data backup in the cloud is that many cloud-based tools offer powerful automation options. Therefore, administrators can forget about backup planning, setting, and scheduling backups, which allows them to focus on more critical functions. With the appropriate cloud solution, you have all files backed up in real-time due to users’ changes and updates.
In the case of SaaS platforms, providers are responsible for restoring these platforms after relevant catastrophic failures. Meanwhile, the responsibility of end-users and IT departments is to secure information at the individual account level. Such an approach is called the Shared Responsibility Model.
As your organization grows, the size and complexity of the information you deal with will also increase. You can run a small business or a large enterprise, which requires managing different types of IT infrastructure. But in both cases, you have to create a structure depending on the data lifecycle management strategy. That helps you visualize the whole journey of your information through the company.
Getting a full picture of your corporate data allows your company to define vulnerable points. In turn, it also promotes you to establish required practices for keeping your information safe. After all, you can take advantage of your data to make the decision-making process more informed.
Frequently Asked Questions
Data lifecycle management creates benefits for each organization that stores and utilizes internal information or customer data. If you have not clearly defined the appropriate data lifecycle management strategy, you will struggle with downtime and financial repercussions. But with efficient practices, your organization will reduce the risk of information loss and data breaches. Also, they help avoid fines and maintain high-quality reputation management.
The three most critical goals of data lifecycle management include data security, availability, and integrity.
The service life cycle allows increasing service revenue, offering add-on services, or upgrading necessary physical assets.
The data life cycle corresponds to the whole period of time when data is located in your system. Such a life cycle covers all the phases that your data must go through.
Each business has to adjust the DLM framework to its available technology ecosystem. But there are five traditional phases: collection, storage, maintenance, usage, and cleaning.
Data lifecycle management aims to indicate when you must delete certain data. Meanwhile, information lifecycle management deals with how relevant and accurate the information is. The major difference between these concepts is that DLM operates on entire data files. On the other hand, ILM operates on the information contained in the file. Lastly, ILM often serves as a vital subset of DLM.
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