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We don’t have data that represents the new normal

Artificial intelligence can’t keep up with the coronavirus. 

Many companies are therefore having to rethink the complex models they use for the all-important job of forecasting their sales and budgets. The data and assumptions baked into those statistical or machine-learning models are now out of date because of the recent economic upheaval, making the conclusion they provide suspect.

“Our historical, analytical data—what we used in the past—is out the door,” Paul Cormier, CEO of IBM-owned business software maker Red Hat, recently told Fortune. “I think everyone feels that way.”

Doug Merritt, the CEO of IT firm Splunk, shares Cormier’s sentiment. Improving the accuracy of corporate forecasting is something “we are all struggling with,” he said. Although Merritt said models provide accurate short-term forecasts, it’s challenging to develop ones that look 30 to 120 days down the road. 

As Rob Thomas, the senior vice president of IBM’s cloud and data platform, said, “In any instance, your models are only as good as the data.” 

“When something happened that didn’t happen before, most organizations don’t have data that represents the new normal,” Thomas said. On the flip side, he explained that as companies continue collecting more data during this pandemic, they’ll have a better understanding about this “new normal.”

Thomas believes that companies should “never throw out data” and “never throw out models.” They still carry “institutional” knowledge, and if a company were to build newer models from scratch, they would lose some historical insights built into the older formulas, he explained. 

If anything, the coronavirus pandemic has meant that now, more than ever, there needs to be an actual person scrutinizing financial models and not just technology, Thomas explained. Savvy data scientists and business analysts need to use their “human intuition” and reasoning skills to assess how much worth should be placed on the predictions of the impacted models.

“You see a lot of people doing that.” Thomas said.

As for whether companies should expect machine learning or other predictive systems to automatically adjust to sudden upheavals like the coronavirus, it’s unlikely to happen any time soon.

“That would be the Holy Grail,” Thomas said. 

P.S. We’d love to hear any interesting methods you or your company may be implementing to make predictive models more robust during this coronavirus pandemic. Feel free to email me—I’m interested in hearing your story. (Please no vendor pitches!)

Jonathan Vanian 
[email protected]

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