Using Search & AI for Information Sharing Around COID-19

Quickly ask questions and share insights around COVID-19 across multiple large and complex sources of data with the use of AI to automate anomaly detection.

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Inspiration

We want to help! Working with technology partners, we have a solution where we can consolidate multiple and very large data sources, with data governance in mind, giving users the ability to:

  • Quickly ask questions, without training, through an easy to use search interface converting a natural language search request into visualizations
  • Ask highly complex and personalized questions against exceptionally large data sets (hundreds of billions of rows, TB of data)
  • Generate new automated insights using machine learning that help answer questions that are relevant today that no one thought to ask

What it does

  • Contingency planning for fungible resources
  • Supply Chain planning

How we built it

  • Using Alteryx, we merged OPM with JHU COVID data to help identify high-risk employees
  • Data can be loaded into Snowflake where we directly connect or we can ingest the data directly into ThoughtSpot
  • We connected to the data and merged the multiple datasets within ThoughtSpot's platform
  • We modeled the data to enable end-user friendly terms to ensure the search experience is simple for any novice user

Challenges we ran into

Access to data - if data is available, we can load it

Accomplishments that we're proud of

A major pharmacy is leveraging ThoughtSpot to provide rapid response around first responder activity - determining where to set up tents for COVID-19 testing and how to allocate resources, staff, and volunteers based on a specific radius of high-risk populations.

What we learned

What's next for Using Search & AI for Information Sharing Around COID-19

The use-cases are endless because of the flexible nature of this solution:

  • Transparency into spending and early analysis and detection into fraud, misuse, or waste
  • Understanding the availability of resources (hospital beds, staff, supplies, etc) for efficient communication to the public. For example, staff can quickly ask questions to understand bed available, beds that still need to be sanitized, supplies available and how to move them around to local facilities, identifying where volunteers or staff should be at specific times and where shortages exist.
  • Collaboration of data between

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