Seamless Contextual Data Integration Using Virtualized Clinical Trials Data

This white paper explores the strengths and weaknesses of popular data management solutions and proposes a new virtualized semantic AI option: LeapAnalysis.

LeapAnalysis (LA) offers advanced search and analytics capabilities, without requiring materials sciences companies to change their systems or processes. LA dramatically accelerates research, improves data availability and quality, and drives results automatically to dashboards and user interfaces, without requiring additional training for end users. Because data stays in place and can be integrated virtually with LA Data Connectors, we can complete a project in 3 months. There's no need to build any expensive ETL (extract, transfer, load) pipelines, no time-consuming migration to the data lake, no indexing steps required to organize queries, and no elaborate dashboards or UIs.

Download this paper to:

• Understand why data lakes are not a viable solution.
• Learn how to transform your multi-year set up to a three-month process that drives real answers for a faster time-to-market.
• Explore how the six-step LeapAnalysis pilot project can accelerate your innovation efforts.

LeapAnalysis's high performance, enterprise-scalable technology runs complex queries and analytics directly on disparate data sources, without the need for lengthy and expensive data pipelining. With LeapAnalysis, “data stays separated and answers come together” because no data is ever copied, migrated, or transformed. This means you have direct access to your original data.

LA also reduces security risks and GDPR concerns, because we can identify personal data using machine learning + semantics, and no further copies of that data get created in new storage systems (unless requested). Security and governance rules can remain as is on data sources as well.

LeapAnalysis is designed to sit next to data lakes and use data from within the data lake, in combination with data that is external to the data lake. This is what we refer to as a “data lake on demand” in that our product provides an ad hoc search and analytics capability, as if one had already gone through the arduous steps of constructing a data lake pipeline and complex index or mapping of the data.

LeapAnalysis has been built to be horizontally scalable across many large data sources and available to large and diverse sets of users. Queries and analytics are extremely fast and highly performant, allowing it to be used in enterprise applications with very good user experience reported.