Best practices for data lifecycle management

Best practices for data lifecycle management
1 min read

Once data is created, it is sent to data storage, which may be on-premises, cloud-based, or a hybrid of the two. The storage may consist of a data lake, data warehouse, or data lakehouse approach, depending on the needs of the organization.  In this stage, data is cleaned, processed, and prepared for the next stage. Essentially, data lifecycle management means planning and building architecture for data integrity around all of these stages to make sure they are functioning optimally and reaching their expected outcomes. 

Data lifecycle management ensures a consistent approach to data usage throughout its lifecycle and helps ensure compliance. Among these, the final stage of the lifecycle, data deletion, is essential for reducing the chance of data breaches or contamination of datasets with data whose permission has expired.

While also minimizing organizational risk by adhering to data regulations and ensuring data security best practices. Creating the best plan for your needs requires some core features to ensure it works as expected now and in the future. 

 Data security and privacy introduce significant organizational risk at various stages of the data lifecycle, though at some more than others. A clear data custody plan informs your data lifecycle management by clarifying privacy and security expectations all along data’s journey so as to minimize this risk.

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Saahil Khan 278
Joined: 3 years ago
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