The global pandemic has disrupted many things in our lives. It has also left its footprint on healthcare, education and the hospitality industry, to name a few. This topic came to us from MIT Sloan Management Review in their article, “Data Science, Quarantined.”
Companies are just now beginning to baby step back to normal with regards to workforce and technology. As they reboot their machine learning and analytics, the data from unprecedented, dramatically changed markets are telling a surprising story.
Supply chains, transportation, food processing, retail, e-commerce and many other industries have transformed overnight. Unemployment in the United States has reached levels unknown and GDP is expected to fall around the world. Over the past decade, we have moved toward data-driven decision making. This data is mostly automatically collected and reported from point-of-sale data, the Internet of Things (IoT), cell phone data, etc.
Now what happens to this accelerated, data-driven approach when a large-scale disruption, such as a global pandemic, results in a comprehensive and sizable change in data? Machine learning models make predictions based on past data, but there is no recent past like today’s present. This is uncharted territory.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
