Protecting Your Data: Understanding Data Lifecycle Management
Data is the lifeblood of modern businesses. It is the foundation on which they make decisions, develop products, and engage with customers. However, as the volume of data grows, managing it becomes increasingly complex.
What Is Data Lifecycle Management?
Data Lifecycle Management (DLM) manages data from creation to deletion. It involves creating, storing, retaining, archiving, and destroying data. DLM aims to ensure data is accurate, accessible, and secure throughout its lifecycle. DLM is a critical component of data governance, which is the overall data management within an organisation.
Why Is Data Lifecycle Management Important?
Effective DLM is essential for several reasons. Firstly, it helps organisations manage their data in a way that meets legal and regulatory requirements. Secondly, DLM helps organisations make better decisions by providing accurate and timely data. Thirdly, it helps organisations reduce the cost and complexity of managing data by automating processes and reducing manual intervention.
The Stages of Data Lifecycle Management
1. Data Creation
Data creation is the first stage of the data lifecycle. It generates data through various sources, including customer interactions, transactions, and social media. Data creation is critical because it determines the quality and accuracy of the data.
2. Data Storage
Data storage is the second stage of the data lifecycle. It involves the secure storage of data to ensure it is readily accessible when needed. Depending on the organisation's needs and resources, data storage can be on-premise or cloud.
3. Data Retention
Data retention is the third stage of the data lifecycle. It involves data retention for a specific period, determined by legal and regulatory requirements or business needs. Data retention policies should be in place to ensure that data is retained for the appropriate length of time.
4. Data Archiving
Data archiving is the fourth stage of the data lifecycle. It involves the long-term storage of data that is no longer in active use but may be required for legal or regulatory reasons. Data archiving should be done in a way that ensures the data is readily accessible when needed but is stored securely to prevent unauthorised access.
5. Data Destruction
Data destruction is the final stage of the data lifecycle. It involves the secure and permanent deletion of data that is no longer required. Data destruction should be done to ensure that the data cannot be recovered and is compliant with legal and regulatory requirements.
Best Practices for Data Lifecycle Management
Effective DLM requires a well-defined strategy and a set of best practices. Here are some of the best practices for DLM.
1. Establishing clear policies and procedures for data management
2. Defining data retention policies based on legal and regulatory requirements
3. Ensuring that data is stored securely and is easily accessible when needed
4. Implementing automated processes to reduce manual intervention
5. Regularly reviewing and updating data management policies and procedures
6. Providing training to employees on data management best practices
7. Conducting regular audits to ensure compliance with legal and regulatory requirements
Data Lifecycle Management ensures that data is managed effectively throughout its lifecycle, from creation to deletion. Effective DLM requires a well-defined strategy and a set of best practices to ensure that data is accurate, accessible, and secure. Organisations implementing effective DLM can make better decisions, reduce costs, and comply with legal and regulatory requirements.
Protecting your organisation's data is crucial in today's digital landscape, especially regarding AWS security. Aristiun offers comprehensive Data Lifecycle Management tools that enable you to continuously assess, prioritise and manage the security domains of your public cloud. Contact us today!