It would not be wrong to say that data run the entire world in this digital era. As a result, the significance of the data lifecycle has also significantly increased, so more people want to learn that what are the three main goals of data lifecycle management (DLM)?
Nowadays, every kind of information is stored for a long period of time because every part of the data contributes to the bigger picture. With the massive increase in data, it has become critical to ensure the best strategies are implemented for data lifecycle management.
Keep reading to learn the various aspects of data lifecycle management and get the answer to the common query that what are the three main goals of data lifecycle management (DLM)?
What is Data Lifecycle Management?
Data Lifecycle Management (DLM) is the process through which the flow of the data is managed in various stages in an information system. The complete data lifecycle includes every stage of data from its inception to destruction.
Creation, application, sharing, saving, and deletion are the primary stages of the data lifecycle. The importance of data lifecycle management varies in industries, but it has three main goals to ensure a fair policy is put in place to manage the flow of data.
Furthermore, these goals are closely associated with the information lifecycle management strategies to make sure all of the data is passing through a certain number of stages.
What are the Three Main Goals of Data Lifecycle Management (DLM)?
Data management can be a challenging procedure because several factors have to be considered in this comprehensive process. Three main goals in data lifecycle management streamline the flow of information. These goals are availability, confidentiality, and integrity.
Let’s discuss the three goals of DLM in detail.
1. Data Confidentiality
A massive volume of data is being used in different companies and websites for various purposes. The security risks associated with different types of data have also increased due to such a large amount of data.
Every individual and organization needs to take some steps to protect their data and make it confidential. Ensuring data security simply means protecting your data from being stolen by unauthorized users and protecting it against trojan horses, malware, and viruses that can corrupt the data.
Once you have acquired or created some data, it must be stored safely to prevent the misuse of data. Generally, structured data is stored in cloud storage, while file servers are used for unstructured data. Whichever storage method you use, it must be safe against unauthorized access and viruses.
Every stage of the data lifecycle management must organize and secure the data so that no data is being lost, stolen, or corrupted by the viruses. It can be achieved by implementing strict rules and regulations to ensure only authorized people are accessing the data.
It is the primary reason why data security and confidentiality is considered to be the most important goal of data lifecycle management (DLM). Without proper security, you cannot expect to accurately utilize the data for a long period.
2. Data Availability
Data is the driving force of the digital age. Hence, it is crucial that data is available at all times. If the data is not available when required, it can fail different processes and the entire data management system.
In other words, data availability is essential for the success of any project. It will make the DLM much more efficient and reliable because the database will have all of the required data that can be accessed at all times.
You should also keep in mind that even if you make your data safe and secure without focusing on the accessibility of data, it will have an adverse impact on data lifecycle management. In this fast-paced era, everyone wants to use things that are quick and easily accessible.
Organizations typically rely on modern cloud-based solutions for data management because they make the data more accessible than local databases. As a result, businesses are able to process and visualize the data quickly.
3. Data Integrity
Once the data is stored, it is likely to be changed due to a variety of edits and revisions. Modern tools and technologies like cloud computing are also being implemented to create multi-user environments.
Therefore, there are many situations in which multiple users might be accessing and editing similar data in the same database. As a result, it can lead to a variety of differences in the data being accessed by multiple users.
Hence, maintaining data integrity is one of the main goals of the DLM to make sure the same data is being shown to all of the users. It makes the data more flexible and consistent as the changes will be made quickly at every place.
What is Hot, Warm, and Cold Data?
Data is divided into different categories on the basis of the temperature scale. The most frequently accessed data is called hot data, while less frequently accessed data is called warm data. Similarly, the data that does not hold much importance and is least frequently accessed is called cold data.
Let’s look at these different types of data:
Data that is essential for day-to-day business activities and has to be accessed frequently is called hot data. Such kind of data has to be optimized for easy accessibility. Thus, companies have to spend a significant amount of money on storing hot data on Tier 1 type storage.
Data that is accessed infrequently but should be available online to meet the business rules and regulations are called warm data. Some companies choose to achieve warm data in a separate database, but it is not deleted for a long period.
Data that does not hold any fundamental importance to an organization is called cold data. It is typically achieved or deleted after a certain period.
6 Phases of Data Lifecycle
There are total six stages in a data lifecycle. These stages are:
The first step that starts the entire data lifecycle is creating or capturing the data. There are many different ways to achieve this goal. For instance, you can get existing data from other entities, use human-driven inputs, or gather data from other devices from your business. Similarly, it is also possible to capture data via sensors, machine learning systems, and external databases.
Data Maintenance or Storage
In simple terms, data maintenance means processing the data to make it usable for an organization or individual. After proper processing, data has to be stored in a suitable place so that a company is able to access and control the data easily.
The third stage of the data lifecycle is to use the data for different purposes like analysis, storage, visualization, and decision-making. Data is a raw material that has to be sufficiently maintained so that it can be used appropriately.
Data publication means sharing the data with various users who are authorized to access it. The purpose of this stage is to make the data available to a wider audience. It typically refers to releasing the data into the public domain with few rules and regulations after getting the approval of all concerned authorities.
It is important to save the unused data because you might need it in case of any direct or indirect dependencies. Data archiving means that you are saving the data that is not recently used and indexing it for future use. Such data is stored in a different location than the primary storage location.
Data deletion is the last stage of the data lifecycle. As the name suggests, data deletion means permanently destroying the data from storage and archives, which you cannot recover the data after removing.
Once you are sure of the fact that there are no direct or indirect dependencies, you can get rid of the data. Generally, the data is removed from the archives. This process also includes deleting the duplicated data.
Benefits of Data Lifecycle Management
There are many benefits of data lifecycle management. Some of them are:
Implementing an effective DLM and following its three main goals allow companies to comply with the rules and regulations related to data storage and ensuring maximum compliance.
A reliable DLM strategy is one that ensures maximum data security and safety, including in emergency situations. It also gives more confidence to the customers that their data is fully protected and is not being duplicated. Hence, both organizations and the customers are able to have peace of mind that their data is safe.
DLM is the foundation of information lifecycle management. Having an effective DLM strategy is important to implement the best practices of information lifecycle management.
Moreover, you should keep in mind that it is the goal of every organization to have maximum efficiency and productivity. When an organization properly implements DLM and ILM, it is able to enjoy maximum efficiency with safe and readily available data.
The bottom line is that data management is a critical part of any organization because it facilitates the flow of information. Advancements in technology are allowing different types of businesses to opt for various services to manage and store data.
Data is undoubtedly the currency of the digital world. Even small-scale companies and startups have to think about implementing the best data management practices to ensure maximum safety and accessibility.
The short answer to the question, ‘what are the three main goals of data lifecycle management (DLM)?,’ is that data confidentiality, integrity, and availability are the three main goals that must be considered in data management.
This article provides a lot of other important information about data lifecycle management and its three main goals. Now that you are familiar with the three main goals of DLM and other aspects of data management, you should be ready to use and implement the DLM in the best way possible to enjoy its benefits.
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