The economic impact of COVID-19 is unprecedented, dramatically changing markets and prospects for economic growth. Supply chains, transportation, food processing, retail, e-commerce, and many other industries have transformed overnight. Unemployment in the U.S. has reached levels unknown in recent memory, and GDP is expected to fall around the world. As one economic journalist summed up the situation: “Nearly everything in the world is super-weird and disrupted right now.”
The data we use to make good managerial decisions has been caught up and turned upside down in this unpredictable marketplace. This is no small matter: Over the past decade, we have seen a dramatic movement toward data-driven decision-making, in step with an explosion of available data sources. Point-of-sale data, the internet of things, cellphone data, text data from social networks, voice, and video — are all automatically collected and reported. Coupled with advances in machine learning and artificial intelligence, these resources enable leaders and organizations to use analytics and data science for better-informed and improved decisions.
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But what we’re now evaluating is what happens to this accelerated, data-driven approach when a large-scale disruption, such as a global pandemic, results in a seismic shift in data. Machine learning models make predictions based on past data, but there is no recent past like today’s present.
To better understand the impact on data science of our current moment and how the disruption will be managed going forward, we reached out to a number of data science and analytics directors. We asked what they have experienced in recent months and how they plan to adjust and redeploy their machine learning models as organizations enter a new economic environment.
A Pivot to Fast-Cycle Descriptive Analytics
Every analytics manager we spoke with described the same basic reaction as the pandemic began to disrupt their operations: Regardless of whether the pandemic caused the demand for their company’s products and services to plummet (as it did for, say, apparel) or to spike dramatically (for instance, toilet paper), there was an almost instantaneous shift away from more advanced analytics focused on prediction and optimization to descriptive analytics such as reports and data visualization. Descriptive analytics helped companies get a better understanding of what was happening.
Because of the volatility of the situation, all cycle times for reporting were dramatically compressed. The demand for real-time dashboards increased.