What is data lifecycle management? Data Lifecycle Management (DLM) is the process of managing the stages of a data’s lifecycle, from its creation and acquisition to its archiving or deletion. It involves identifying, classifying, and controlling data throughout its entire lifecycle to ensure it is used efficiently and effectively, while also minimizing risks and costs associated with data storage. The goal of DLM is to optimize the value of data while minimizing risks to the organization.
Most businesses today consider data to be one of their most valuable assets. Any attempted or actual data breach, mishandling, or data loss can potentially severely disrupt many operational areas. The process of Data Lifecycle Management (DLM) can help lessen these risks.
Protecting and managing your organization’s information effectively has become more critical than ever. DLM is a process for guaranteeing data security and privacy from inception to end-of-life. DLM offers advantages like risk management, a better comprehension of a company’s needs and objectives, and the best decision-making.
DLM also makes it possible to recover data at previously unheard-of rates under extreme time constraints.
In the technical process known as integrated logistics support (ILS), support and logistics considerations are incorporated throughout the product or system’s design life cycle. A system that is both affordable and long-lasting and operates to its full potential is the ultimate goal.
At CSMI, we can offer our customers a complete solution for technology lifecycle support for domestic and international deployment. This is thanks to our background in technology installation/integration, integrated logistics support, operations & sustainment, lifecycle support, and training. Get in touch with our team today.
In this article, we’ll go over everything you need to know about data lifecycle management. Our goal is to make sure you fully comprehend the strategy and can maximize control over complex organizational data.
What Is Effective Data Management?
Data Lifecycle Management (DLM) integrates best practices from all stages of the data life cycle, including production, data cleansing, data management, data protection, and data governance. It specifies how data is collected, prepared, transported, managed, analyzed, and governed throughout the data life cycle.
The DLM process can assist businesses in ensuring that the right data is in the right place at the right time. As a result, they are able to capitalize on data insights and create new opportunities.
Businesses get a comprehensive understanding of the different types of data by utilizing data science. They can then keep track of data usage throughout the various customer journey stages. They are also more equipped to spot any data breaches or misuse.
The 5 Components of the Data Management Lifecycle Process
Managing data throughout its lifecycle is a series of phases that occur throughout its useful life. Each phase is governed by a set of policies that maximize the value of the data throughout the lifecycle. As the volume of data incorporated into business work streams grows, DLM becomes more important.
- Data Creation: Data collection is the first step in a new data lifecycle, but there are many data sources available. They can include forms, surveys, internet of things (IoT) gadgets, mobile and web applications, and more. Although data can be produced in a variety of ways, your business will only succeed if you collect every piece of available data. The value and applicability of new data to your business should always be considered before incorporating it.
- Data Storage: Data can be structured differently, which can have an impact on the kind of data storage a company uses. Relational databases are typically used by structured data, whereas unstructured data typically uses NoSQL or non-relational databases. Once the type of storage for the dataset has been determined, the infrastructure can be checked for security flaws. Data can be processed in a variety of ways, including data encryption and data transformation. This will help to protect the company from malicious actors. Furthermore, data munging helps ensure that sensitive data complies with the privacy and governmental requirements for governmental policies such as GDPR. This allows businesses to avoid costly fines imposed by these regulations. A focus on data redundancy is another aspect of data protection. A backup copy of any stored data can protect against both accidental and intentional data changes, such as malware attacks, in situations such as data destruction or corruption.
- Data Sharing and Usage: Data is made accessible to business users during this stage. When organizations share data, they can specify who can use the data and for what purposes by using DLM. Once made available, the data can be used for a variety of analyses. This can take many different forms, ranging from straightforward exploratory data analysis and data visualization to more intricate data mining and machine learning techniques. All of these approaches are used in business decision-making and communication with various stakeholders. Furthermore, data usage isn’t always limited to internal purposes. External service providers, for example, could use the data for marketing analytics and advertising. Day-to-day business processes and workflows, such as dashboards and presentations, are examples of internal uses.
- Data Archival: Data is no longer useful for day-to-day operations after a certain amount of time and becomes what we call inactive data. However, for potential litigation and investigation requirements, it is critical to keep copies of the organization’s data that is not frequently accessed. The archived data can then be restored to an active production environment if necessary. The DLM strategy of an organization should clearly define when, where, and for how long data should be archived. At this stage, data is archived to ensure redundancy.
- Data Deletion: Data is purged from records and securely destroyed at the end of the life cycle. Businesses will delete data that is no longer required to free up storage space for active data. When data exceeds the required retention period or ceases to serve a valuable purpose for the organization, it is removed from archives.
The Importance of Data Lifecycle Management
The success of business operations depends on having the right information, which is why data management is so crucial. Data management, when done correctly, can assist businesses in streamlining their operations and enhancing customer service. Additional insights can be gained by businesses to analyze performance and enhance customer comprehension.
Businesses can gather information quickly and affordably and store it in a secure environment by using a data management approach. A visitor behavior analysis tool or an order management system for e-commerce, for example, may give you better access to personal data than data lifecycle management alone.
The Benefits of a Data Management Lifecycle
Data lifecycle management has many benefits, including lowering costs by enabling more widespread access to and use of data throughout its lifecycle.
Businesses that are driven by data also save time because they can do away with expensive and time-consuming data management procedures. In addition, they can promote more cooperative data sharing among users and employees.
- Maintain adherence to data regulations: Businesses must manage the lifecycle of their data to comply with data regulations. The General Data Protection Regulation (GDPR), which replaced the 1995 Data Protection Directive, is one of the main regulatory bodies. With the primary objective of enabling people and businesses to benefit from digital technologies in a safe and secure environment, the GDPR takes into account both economic and societal factors.
- Better cost controls: A DLM process assigns a monetary value to data at each stage of its lifecycle. When data is no longer useful in production environments, organizations can use a variety of cost-cutting solutions such as data backup, replication, and archiving. It could be transferred, for instance, to less expensive network-attached storage, cloud storage, or on-premises storage.
- Access to relevant data: Data management lifecycle management improves data availability and accessibility. This allows employees to make better and faster decisions, provide better customer experiences, and be more adaptable in the face of change. This is especially important in terms of Internet of Things (IoT) app development and deployments, as organizations seek higher levels of data visibility.
- Data usability: A DLM strategy enables IT teams to create guidelines and practices that guarantee all metadata is consistently tagged, enhancing accessibility when required. Data value is ensured for as long as it needs to be retained by setting enforceable governance policies. The availability of accurate and useful data improves the speed and effectiveness of business operations.
- Increased operational effectiveness: In today’s business world, operational efficiency and agility are critical. Data management lifecycle management can help improve the efficiency and effectiveness of the IT infrastructure, resulting in a more streamlined and efficient approach to day-to-day operations.
- Enhanced customer experience: Data management lifecycle management enables businesses to unlock the value of their data to better serve their customers and increase sales.
- Effective Data Management: Cyber governance training for IT personnel in data management lifecycle management processes can assist businesses in lowering data management costs. Additionally, it guarantees that the right format is used for data collection, monitoring, and maintenance regarding employees.
Implement Data Lifecycle Management in Your Business
Data lifecycle management is now an essential component of all businesses. More and more businesses are realizing the importance of staying current with ever-changing data management requirements.
In today’s fast-paced technological world, this is an increasingly difficult task. Indeed, one of the most significant challenges that businesses face when it comes to data management is that their data is frequently disorganized.
Data lifecycle management ensures that data fragmentation and disorganization won’t ever complicate your business requirements. Instead, they enable you to effectively manage data and utilize it for the best outcomes.