A focus on high-quality data—and now "data for the AI lifecycle"—can help your company see a high return on investment in AI projects and more easily scale your work with AI.

Data lifecycle management is essential to ensure it is managed effectively from creation, storage, use, sharing, and archive to the end of life when it is deleted.

In today’s world, new software development will often touch, capture or create new data. And as such, there must be checks in place to ensure that the data is handled and protected according to ...

Fragmented data across enterprise systems often disrupts product lifecycle management (PLM), preventing companies from ...

Data normalization facilitates the flow of data across front-, middle-, and back-office operations—in both directions. For example, when Broadridge provides dashboards with real-time lifecycle data to ...

For all systems which store, process, and retrieve data – such as thermal analyzers – data integrity is paramount. “Data integrity is the degree to which data are complete, consistent, accurate, ...

Building A Robust Hybrid Cloud Data Lifecycle Management Strategy. Date: Tuesday, March 4 th at 11am PT / 2pm ET Data lifecycle management (DLM) connects infrastructure lifecycle management with ...

NEW YORK, May 1, 2025 /PRNewswire/ -- BigID, the leader in data security, privacy, compliance, and AI data management, today announced the launch of its industry-first comprehensive Data Lifecycle ...

Agiloft, the leader in data-first contract lifecycle management (CLM), announced today the acquisition of Screens, the world's first standard-based and community-supported Generative AI contract ...

Each page is dedicated to a particular step of the research data management process and contains sections focused on elements of that step. Visitors to this site may navigate directly to the page that ...