Best practices for data lifecycle management

At this stage, data scientists perform analysis to transform the raw data into a resource that’s valuable for the organization. The advanced analytics allow improved insight into what is happening at the data’s creation point or see the data combined into larger datasets to get a macro picture. Then, DataOps teams compose this data into readable datasets for other users.

Data lifecycle management is a critical process for data operations, as it ensures that data processing, analysis, and sharing are all streamlined. The flow of data is considered and data friction points are reduced to increase data value and ROI. An effective data lifecycle management process can identify and smooth obstacles as soon as they appear. 

With varied data collection points that could number in the millions, data lifecycle management helps data functions create and maintain interoperable data architecture that reduces friction and improves the usability of all collected dataflows.