Today’s threat landscape produces a harsh reality: for organizations, a data breach is not a question of “if”, but “when.” Whether a company’s data is compromised via a breach, mishandling, or loss, it presents significant operational challenges. While many data protection tools and strategies exist, one that cannot be overlooked is Data Lifecycle Management (DLM).
Protecting and managing your organization’s data is more critical than ever, and DLM ensures data privacy and security throughout the data’s lifecycle — from data creation to data destruction.
Outside of data protection, other key advantages of DLM include:
Data Lifecycle Management (DLM) is the process and strategy of managing data throughout its lifecycle. It includes understanding the data’s value, where it is located, who has access to it and how it is used. As the amount of data grows and its complexity increases, it has become increasingly important to have an effective DLM system in place.
DLM is more relevant than ever due to the ever-increasing amounts of data, the need for better data governance and the need to securely store and process the data.
As organizations continue to migrate toward cloud and SaaS environments, data is becoming increasingly valuable, resulting in organizations needing a secure and robust system to store and manage the data. This evolution brings about increasing awareness of how critical it is to properly manage the data lifecycle for information optimization and risk mitigation.
When it comes to cybersecurity, DLM helps organizations to more effectively protect their data from cyberattacks. Robust DLM allows for data to be stored and managed securely, as well as ensuring that the data is only accessed by those who are authorized to do so. DLM can also help organizations quickly detect and respond to malicious activity, as well as securing data from external threats.
Any data security discussion isn’t complete without compliance — a critical component addressed by DLM, as it enables organizations to ensure that the data is managed, stored and disposed of in compliance with applicable laws and regulations, which includes understanding data protection and privacy laws.
The data lifecycle management (DLM) framework is a strategy for managing data throughout its entire lifecycle, from creation to disposal. It is designed to ensure that the data is secure, well-managed, and compliant with all regulations.
Data is created, stored, and processed by various systems and applications. The DLM framework outlines a set of processes and policies which must be followed to ensure that data is secure and managed properly. The framework consists of data classification and encryption, access control, and regular data backup.
Once data has been created, it must be stored and managed appropriately: selecting the proper data storage system and ensuring that data is secure, available, and compliant with applicable laws and regulations. Data must also be regularly backed up in case of unexpected events such as system failure or data corruption.
Finally, the data must be disposed of properly when it is no longer needed: securely deleting or archiving the data and ensuring that any remaining copies are securely stored.
Data lifecycle management provides several significant advantages, including:
Process improvement: Data is essential to the successful implementation of an organization’s strategic objectives. Data Lifecycle Management (DLM) helps maintain the quality of data throughout its lifecycle, resulting in improved processes and increased productivity. A well-designed DLM plan ensures that the data available to users is precise and dependable, allowing companies to make the most of their data.
Cost control: With DLM, organizations can assign a monetary value to data at each stage of its lifecycle. This enables them to cut costs based on the data’s age and utility. Solutions for doing this include data backups, replication, and archiving. By moving data to less expensive storage locations, such as cloud storage or network attached storage, organizations can further reduce costs.
Data usability: By deploying a DLM strategy, IT teams can create policies and procedures to ensure that all metadata is consistently tagged, improving accessibility when needed. Establishing enforceable governance policies ensures that data retains its value for as long as it is required to be retained. The availability of clean, usable data improves the agility and efficiency of business processes.
Compliance and governance: Every industry has its own data retention rules and regulations, and a solid DLM strategy helps businesses stay compliant. DLM enables organizations to handle data more efficiently and securely, while remaining compliant with data privacy laws governing personal data and organizational records.
Finally, there are a variety of services, tools and processes available to help organizations with their DLM needs. These include data governance solutions, data classification solutions, data security solutions and compliance solutions. Each of these solutions can help organizations to better understand, manage and secure their data. Additionally, these solutions can help organizations to quickly detect and respond to any malicious activity.
To make sense of the various services, tools and processes available for DLM, it is important for organizations to understand their specific needs and do research on the various solutions available to them. Additionally, organizations should ensure that they are using the most secure and up-to-date solutions for their DLM needs.
Q: Who needs DLM?
A: DLM should be used by any organization that handles sensitive, private data that is subject to regulatory compliance. Implementing DLM is critical if your company collects or stores data — such as personal contact information, bank account numbers, healthcare data and other personally identifiable information (PII).
Q: Why is DLM so important?
A: Leveraging DLM creates processes for data collection, access, use, and destruction that protect your data and satisfy regulators. DLM also aids in the development of cost-effective structures for how your technology stack interacts with data.
Q: What are the primary objectives of DLM?
A: The primary directives of DLM are security, confidentiality, integrity, and availability. The phases of DLM start with data collection and creation and continue through archival and destruction. DLM ensures that organizations both protect and dispose of data properly while maintaining data availability.
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