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A 3-month solution for automated, data-driven materials science analytics

Though data-driven processes have accelerated scientific discovery and innovation, data remains an underutilized resource of the materials science industry. To manage growing amounts of data, researchers are combining human reasoning with solutions like materials databases, machine learning, and artificial intelligence.

 

However, incongruent data formatting, inconsistent metadata, and inaccessible data silos inhibit the technological progress of most large materials sciences companies. Many companies wonder what they can do to address these problems (without it costing an arm and a leg).  

This whitepaper explores the strengths and weaknesses of popular solutions and proposes a new option: virtualized semantic AI. LeapAnalysis offers advanced search and analytics capabilities, without requiring materials sciences companies to change their systems or processes.

 

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.

Download white paper