Data Lifecycle Management (DLM) is simply referred to as the various stages that the data traverses throughout its life from the time of inception to destruction. Data lifecycle stages are made up of creation, utilization, sharing, storage, and deletion.
Data life cycle management (DLM) for a while now has been viewed as a policy-based approach to managing the flow of an information system’s data throughout its life cycle. Also note that DLM products automate the processes involved, more or less organizing data into separate tiers according to specified policies, and automating data migration from one tier to another based on those criteria.
Currently, it is already globally recognized that information is an independent resource that contributes to the accelerated development of various areas of human activity. The 1980s brought the introduction of random access storage (RAM) and with that enterprise businesses transitioned from sequential card-punch and tape approaches to databases. This era heralded the rise of data management to solve the issues of the time. The duplication of sensitive customer data, for instance, was a major cause of concern.
However, with the development of high-performance computing tools and broadband communication networks for establishing databases and organizing access to them, the issues of production, storage, and presentation of information have become particularly crucial when creating complex engineering and technical objects.
According to reports, the most widespread concept is the automation of processes in the life cycle of data products and services, where information is a major element of automated systems. As a rule, newer data, and data that must be accessed more frequently, is stored on faster, but more expensive storage media, while less critical data is stored on cheaper, but slower media.
What are the 3 Main Goals of Data Lifecycle Management (DLM)?
One of the major challenges that companies face while growing and amassing data is a data breach, which entails that the data must be managed effectively throughout its lifecycle. The three most important data lifecycle management goals can be explained as follows:
Table of Content
Data Storage and Security
Immediately after the data is acquired it needs to be stored safely to avoid or limit the misuse of data. Note that structured data can be stored in on-premise databases or in the cloud while unstructured data is normally stored in file servers and or in the cloud. But irrespective of where it is stored, the data needs to be safe against unauthorized access and theft.
Since business in this age is more or less driven by data, it’s pertinent to ensure its availability to the business. Availability also includes processing and visualization of data as required by the business.
Notably, as data ages, it can evolve and change over time due to modifications, and cleansing activities. Such activities can also result in data sprawl, meaning the same data can exist in multiple locations in slightly different forms. Therefore, it’s necessary to put a process in place to ensure the integrity and resiliency of data.
Benefits of Data Lifecycle Management to Businesses
There are many reasons why businesses in the United States would want to implement DLM processes. These are as follows:
Compliance and Governance
Have it in mind that every industry sector has its own stipulations for data retention, and implementing a sound DLM strategy helps businesses remain compliant.
A good DLM strategy offers redundancy that can ensure data stays safe in the event of an emergency. It also aids to ensure that customer data is safeguarded from being duplicated in different parts of a data infrastructure, where security may be a concern.
At the foundation of ILM is DLM. For businesses in the United States to fully actualize an ILM strategy that keeps data current and secure, they must first have a working DLM strategy that pulls data through the lifecycle.
Have it in mind that at the crust of every IT solution is greater efficiency. When DLM and ILM are properly implemented in tandem, useful data is clean, accurate, and readily available to users. Automation helps drive this process. All of this helps businesses achieve greater agility and efficiency.
Different Stages of Data Lifecycle Management
According to experts, data will go through four different key stages during its lifecycle. Note that each stage tends to revolve around the Primary purpose and value of data and to whom the data is valuable. Other factors that will influence each phase include – data privacy, data security, and data compliance.
Generate and Collect
This first phase in the management of the data life cycle consists of the creation and capture of those that were virtually non-existent in the company. Data is also created by on-premise IT systems to aid further analysis of the data generated by business actions. The type of data generated could be both structured and unstructured.
Process and Manage
Note that after the creation of data, it is stored in relational databases, NoSQL databases, and files shared based on the nature of the data. Data could additionally be processed to meet business needs, such as finance, marketing, customer relationship management, etc.
However, as part of data processing, data is also classified as internal, sensitive, restricted, and public. Also note that data protection policies including access control, data encryption, data masking, and data loss prevention are also leveraged to the data as part of managing it. At this stage depending on the age of the data and its relevancy to business processes, it is classified as hot, warm, or cold.
Analyze and Visualize
At this point, data is cleansed and validated after which it is shared with business users, consumers, and other third parties. Note that data protection policies comprise access control, data encryption, data masking, and data loss prevention, which are all applied to the data prior to sharing it. Enterprise resource planning (ERP), Human Resources (HR), Customer Relationship Management (CRM), Data Warehouse (DW), and inventory systems are some of the IT systems that are used to provide access to the data.
Archival and Destruction
For long-term availability, cold data is typically archived to tape, disk, and cloud storage preferably in an encrypted format. Archival could either be online or offline. Online simply means that the cold data is stored in the same exact format as the hot and warm data and is usually due to regulatory compliance reasons.
Offline means that the data is either stored in a file format or database dumps or exports and are typically encrypted. However, depending on the nature of the data, all or most of the archived data can be targeted for deletion. This is especially true for derived data rather than raw collected data.
Data management has become actively important as businesses face compliance, especially ones that regulate how organizations must deal with particular types of data. According to experts, data life cycle management is not a product, but a comprehensive approach to managing an organization’s data, involving procedures and practices as well as applications.