What Are the Three Main Goals of Data Lifecycle Management

What Are the Three Main Goals of Data Lifecycle Management

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Data lifecycle management (DLM) allows corporations to ensure digital hygiene and security measures concerning data creation. It includes where the data originates and how it evolves. DLM serves three main objectives in the enterprise data control policy. This post will elaborate on the three goals of data lifecycle management.

DLM or data lifecycle management means that a company regulates and protects the data created, stored, processed, archived, or deleted across the duration for which data is available. Therefore, data modernization services involve robust strategies that determine how to ensure data security.

While businesses have increased the application of modern technology like data analytics solutions, cyberattacks and the resulting losses have also increased. However, many companies lack the necessary safeguards to avoid data leaks or block malicious actors who want their datasets.

Data modernization services develop DLM strategies to serve the three goals or ‘CIA triads’ in data lifecycle management. CIA stands for confidentiality, integrity, and availability, which specify the values to uphold when designing and utilizing data processing systems.

Business communication data and customer datasets must remain safe within the organization’s systems. Therefore, companies must ensure that sensitive information stays secure/secret. Data modernization services use encryption and controlled access privileges to limit the visibility of such datasets only to authorized people.

Data changes over time, and your staff or suppliers can modify it in several ways. So, some databases require verification, while others might be obsolete. Data integrity implies that you and your data modernization team implement reliable services to regulate data modifications and relevance.

Corporations cannot afford to waste time due to the unavailability of data. So, the availability of datasets when required by a person is one of the goals of data lifecycle management. Regional institutions and international ones prioritize the 24*7 work policies. So, they need an efficient IT infrastructure and governance frameworks to deliver their services throughout the day or night.

External parties can generate data and share it with your firm. Also, employees and automated tools create new datasets in a corporate setting. This phase is the data creation stage when data becomes available to the organization for the first time.

Datasets can undergo strategically unpleasant changes like corruption or incompatible formatting. So, storing data requires professional data management assistance, while backup creation demands powerful hardware.

Stored data must serve a purpose, and every stakeholder requires to know it. Data usage often includes a lot of editing, sharing, and updating databases. You can leverage data analytics solutions to analyze dataset patterns and extract strategic insights. However, the company stakeholders must practice utmost care when utilizing personal or confidential data in the firm.

Some datasets are relevant to a particular department. So, monitoring how data sharing happens within an organization is essential for data lifecycle management goals. Data integrity can suffer if unrelated departments or less knowledgeable employees access specific datasets.

Data gets old, and it requires appropriate policy to determine how long the firm must hold a specific dataset. Inconsequential data points are often less valuable, while companies find archiving financial records crucial for decades.

Data lifecycle management strategies specify what to do with the data once it has served its goals. Eventually, all datasets become obsolete or irrelevant though you can repurpose them for a different business problem.

You have learned about the three main goals of data lifecycle management and DLM stages. Data analytics solutions require DLM compliance to reduce the cost and effort of data cleansing. Therefore, corporations leverage data modernization services to fulfill the confidentiality, integrity, and availability goals (CIA triads).

Business datasets contain sensitive information that requires protection from cyberattacks and unauthorized modifications by employees. Meanwhile, data protection regulations have evolved to enforce correct compliance requirements on all industrial and institutional units.

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