Data has become a crucial asset for businesses in the current digital era. It prompts innovation, decision-making, and overall corporate performance. However, the enormous volume of data generated daily can be overwhelming. It makes it crucial for businesses to implement effective data management strategies.
Data Lifecycle Management (DLM) stands out as a strategic method that directs attention to the complete lifespan of data, spanning from its beginning to its removal. This process is essential for guaranteeing the effectiveness, security, and purposeful use of data within a corporate setting.
But, what are the three main goals of Data Lifecycle Management (DLM)?
Keep reading to learn DLM’s primary objectives and its significance for businesses looking to harness the full potential of their data.
What Are the Three Main Goals of Data Lifecycle Management (DLM)?
Data management may be very challenging for businesses at times. The three primary objectives of data lifecycle management are usually as follows. Together, they support an organization’s overall effectiveness, security, and thoughtful use of data.
As you transform data into insights, they also assist you in avoiding liability concerns. The three objectives are availability, integrity, and confidentiality; they are also referred to as the CIA trinity.
These objectives are all connected; for instance, you might not be able to guarantee the integrity of the data if it isn’t confidential. It is advised to examine each objective separately to determine how these unique characteristics combine to form a trio that your company can rely on. Let’s see what are the three main goals of Data Lifecycle Management (DLM):
1. Data Security and Confidentiality
The digital world depends on data to function. As we’re working with lots of data, the probability of data misuse also rises. Therefore, we have to confirm that people are using it in a legitimate manner. Here comes the DLM’s first goal into the picture.
Maintaining the security and confidentiality of data throughout its lifecycle is one of the main objectives of data lifecycle management. Protecting sensitive data is essential in a time when cyber threats are everywhere. It does this by securing data from deletion, leakage, and loss.
The CIA triad, consisting of Confidentiality, Integrity, and Availability, forms the foundation of this goal.
The following security advice can help your data management function more efficiently:
➜ Audit and Log Data Access: Regularly audit and log data access to track who interacts with the data and when.
➜ Limited Data Storage: Store only the data that is essential for business operations to minimize the risk associated with unnecessary data storage.
➜ Data Location Awareness: Understand and control where sensitive data is stored, ensuring it is in secure and compliant environments.
➜ Encryption: Use encryption for data at rest and in transit to protect it from unauthorized access.
➜ Network Security: Maintain a fresh and secure network configuration to thwart potential breaches.
2. Availability at All Times
Making sure that data is always available is the second important goal of Data Lifecycle Management. If data isn’t available to the correct users at the right time, workflows can easily break down and blockages can appear.
Data must be available to authorized workers whenever they need it in an environment where businesses run around the clock. IT teams may regularly tag metadata for usability with a solid DLM technique.
Strategies for Ensuring Availability:
➜ Minimize Downtime: Implement measures to minimize system downtime and ensure that the data system remains live and available.
➜ 24/7 Accessibility: Recognize that the business world operates non-stop, and so should your data access systems. Ensure that data is available whenever it is needed by authorized users.
3. Long-Term Structural Integrity
The third pillar of Data Lifecycle Management is keeping the long-term structural integrity of the data management system (DMS). Even if specific data points might become outdated, the system should last. It happens because it will continuously adjust to new information and change along with the needs of the businesses.
Adaptability and Endurance:
➜ Business Evolution: Acknowledge the ever-changing nature of the business world and ensure that your DLM system is equipped to evolve alongside it.
➜ Data Relevance: Strive for a data management system that remains relevant and useful despite the dynamic nature of business operations.
➜ Success-Driven Approach: Embrace an indeterminate and success-driven approach to data management, recognizing that the value of the system lies in its ability to endure and adapt.
Technically, a data lifecycle is a five-phase, circular pattern. While taking action to consistently achieve the three goals, a strong DLM method repeats through the five phases of the data life cycle management process, including collection, storage, usage, maintenance, and deletion.
Tools and Technologies for Data Lifecycle Management
Businesses use a variety of tools and technologies in order to efficiently accomplish these goals.
Those tools and technologies optimise data storage and utilisation, stimulate innovation and growth, and speed up DLM procedures.
Data Management Platforms (DMPs)
DMPs gather, organize, and make use of data from several sources. They do this to generate detailed user profiles for purposes of personalization and targeted advertising. Data integration, audience development, cross-device targeting, and automated data analytics are some of the features.
Data Classification Tools:
Data Classification Tools help identify and classify sensitive information within a company so that it is properly managed and safeguarded. A few examples of these tools are Microsoft Purview Data Catalog, IBM Watson Knowledge Catalog, and Oracle Cloud Infrastructure Data Catalog.
Data Monitoring and Analytics:
Frequent data monitoring guarantees the accuracy and validity of essential business data. On the other hand, analytics converts data into insightful knowledge. This combination helps in the maintenance of high-quality data and the making of wise judgements by businesses.
Conclusion
The three main goals of Data Lifecycle Management are the bedrock of effective data management. It ensures data security and confidentiality, availability at all times, and long-term structural integrity. Overall, it takes a mix of technology, and continuous monitoring to implement a strong DLM plan. Companies need to make adjustments to their data management procedures to meet both legal and business standards.
By implementing this approach, businesses or organizations can boost the significance of their data. It positions itself for success in an era where the use of data continues to grow.
