Integrating and governing big data

whitePaper | September 12, 2022

This whitepaper examines clinical trial data contribution policies and the data protection methodologies applied to protect patient privacy. Information published by 29 biopharma sponsors was collected across three data-sharing platforms, collated by sponsor size. Results showed that large sponsor contribution policies can provide helpful benchmarks for medium and smaller sponsors.

whitePaper | April 1, 2021

Metadata is more important now than ever. New technologies have enabled businesspeople who have traditionally not been analysts to work with data. The consumerization of IT means people expect systems to be intuitive and require little training. With so many people using data to support so many kinds of decisions, it’s critical that your data is described, defined, and understood.

whitePaper | June 2, 2022

Data lakeshold enormous promise in supporting modern enterprise data architectures. Their ability to store a full variety of data types and handle ever-larger data volumes generated at high velocity have contributed to their widespread adoption.

whitePaper | September 2, 2022

This year, unlike previous years, we asked professionals the most difficult elements of a successful data strategy instead of the most important.

whitePaper | March 29, 2023

Migrating to a new analytics environment can be a complex, timeconsuming and resource-intensive undertaking. Significant investments have been sunk into existing analytic environments, and migrating to a new platform can involve months of effort. When organizations go to this level of effort and expense, ideally, they should achieve more than simply migrating the existing environment. A migration provides an excellent opportunity to look to the future, rethink the environment, and deliver new capabilities to the business that can improve efficiency and serve as a source of competitive advantage.

whitePaper | November 18, 2022

It’s time to treat data like a product, not an afterthought. A new paradigm has emerged among forward-thinking data teams: treat your data like a product. This may sound easy in theory, but it’s far from it! Product development processes have become incredibly sophisticated, with resources dedicated to reliability, adoption, feature roadmaps, and more.