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A Sea-change for New Drug Discovery

By MemberSupport, in SPONSORS' PAVILION

Dave_M
Message added by Dave_M

The data fabric has rightly caught the attention of the analyst community and is celebrated as a major step forward supporting new efforts toward digital transformation as well as super charging existing programs for AI, ML, data science, and business intelligence.

Forrester has recognized  a new category of products that address this space, referring to these solutions as “big data fabrics.” According to Forrester, “Big data fabric focuses on automating the process of ingestion, curation, and integration of big data sources to enable the analytics and insights that are critical for business success. It minimizes complexity by automating processes, workflows, and pipelines, generating code automatically, and streamlining data to simplify data access.

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Sponsor; The International Association of C+H Value Chain Organizations

 

Sea change or sea-change is an English idiomatic expression which denotes a substantial change in perspective, especially one which affects a group or society at large, on a particular issue. This term is quite appropriate when it comes to C+H new drug discovery efforts.

The average period to develop a new drug application is 10 - 12 years. Industry leaders are now trying to half this time-to-market. This feat will vastly change our Time-To-Market and Time-To-Volume norms. But how are we to accomplish this?

The answer: Graph knowledge bases and massively parallel processing can be used by progressive organizations to support Value Chain information flows as well as tackling both OLAP and OLTP (On-Line Analytical Processing and On-Line Transaction Processing.

Here's why...

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Semantic ontology enables rich, relevant terms that accurately describe your data, setting the stage for a knowledge graph that will help you make smarter business decisions.

One Value Chain Management technical data engineering aspect is the use of Knowledge Graph databases to address  digital transformations of highly complex data using both graph and massively parallel processing. 

Here's why Time-To-Market and Time-To-Volume are so important in the New Drug Discovery realm...

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