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Why the Future of Analytical Data Must Consider AI Principles

Published online at Technology Networks.

by Andrew Anderson

Andrew Anderson (VP Innovation & Informatics Strategy, ACD/Labs) published this article discussing artificial intelligence and data science, and why data accessibility is more important than ever.

Data science is the collection of computational methods that enable artificial intelligence. Critical strategic Initiatives in R&D over the last decade have leveraged artificial intelligence (AI) and data science. Both of these key areas require the capability for systems to manage large amounts of data efficiently. For strategic initiatives to produce value to stakeholders, data needs to be collected, stored and organized in a scalable and robust manner, to ensure it is properly structured for use. To do this, biotech and pharma organizations should:

1) Ensure that every scientist—whether synthetic, analytical, chemical, etc.—is involved in data science planning and strategy; and

2) Ensure that software-based data access is provisioned to help manage the chemically intelligent info.

Moreover, with increasing use of automation in scientific experimentation workflows, and COVID-19 driving even more interest in data science and AI, data accessibility is more important than ever. Here's why.

Read the full article online at Technology Networks