The Search for COVID Answers Begins with the Right Data (Part 6)

The COVID-19 pandemic has raised questions never faced before. Businesses getting the best answers were already prepared to use AI, data, the cloud, and agile practices.

By Bret Greenstein & Tom Godden

Originally posted at https://digitally.cognizant.com/the-search-for-covid-answers-begins-with-the-right-data-codex6529/.

When the COVID-19 pandemic struck, businesses had more questions than answers: Which policies would stem the spread? What’s the best way to distribute vaccines? How can we safely reopen? While many questions are still being debated, it’s clear what distinguishes leaders from laggards: preparedness with data, artificial intelligence (AI), the cloud, and agile practices.

The maturation and readiness of these four components have enabled organizations to ask—and answer—questions they couldn’t have fathomed a short time ago. How can we rapidly screen antibodies to determine which vaccine has the highest efficacy? How can cell phone data help us understand COVID-19 spread? And how do we do all this faster than has ever been accomplished before?

For those who had already prepared their data, migrated to cloud, and honed their agile skills, adapting to the changed environment was less of a pivot in their strategy than an acceleration of it. What might have been a multiyear endeavor to rethink data architectures and make sure data was available for scalable AI/machine learning (ML) analytics suddenly became a highly focused project that promised near-term outcomes, in phases that delivered high business value.

For businesses that weren’t similarly prepared, migrating to the cloud or beginning an AI pilot were suddenly no longer abstract “someday” concepts or theoretical problems but rather a means of survival or helping others survive.

Getting Answers to Unforeseen Questions

We used these same four components—preparedness with data, AI, the cloud, and agile practices—to answer our own pandemic-related question: how to predict COVID-19 infection rates and prescribe the most effective intervention plans. With XPRIZE, Cognizant launched the Pandemic Response Challenge, a $500K, four-month competition in which teams will use data-driven AI systems to develop models that attempt to accurately predict local outbreaks and produce prescriptive intervention and mitigation approaches.

Here’s how Pandemic Response Challenge competitors are using the four components, which we believe will drive the answers to the COVID-spurred needs of today.

• Sharable, accessible, and updatable data: The challenge uses data compiled by the Oxford COVID-19 Government Response Tracker (OxCGRT). Oxford takes an open approach to collecting and sharing its dataset, which is updated daily and available immediately. Data is valuable, but not when it’s locked away.
• AI/ML modeling: Using this data, as well as technology and AI models that Cognizant developed, competing teams are building data-driven AI models to predict local COVID-19 transmission rates and prescribe intervention and mitigation measures that minimize infection rates and negative economic impacts.
• Cloud computing: Teams are developing their proposed solutions using the data science capabilities and scalability provided in the cloud, courtesy of supporting partner Amazon Web Services (AWS).
• Agile, autonomous teams: In combination, data, AI/ML, and the cloud served as an agile foundation for participants to rapidly respond, innovate, and try new experiments. These technologies enable multidisciplinary teams working toward a shared goal to operate in a high-frequency, high-agility, rapid manner.

Moving Forward by Working Backward

Ultimately, the COVID-19 pandemic clarified that the value of digital initiatives is not only cost savings but also something we’re in much greater need of today: the flexibility to adapt to problems, known and unknown, as they arise. Suddenly, the motivation isn’t “get me off my legacy systems” but “help me set effective COVID-19 policies, support a remote workforce, enable mobile ordering of food delivery,” or whatever business problem became acute during the pandemic.

In this way, the COVID-19 pandemic gave us the ultimate lesson in what AWS calls working backward: working back from a business need—rather than starting from the product or service—to arrive at a solution. In a blink, COVID-19 uprooted outdated thinking and made clear that digital initiatives succeed when you aim an innovation at a specific goal.

Preparedness Paid Off

Between Cognizant and AWS, we’ve seen business after business during the pandemic launch a crucial capability or solution more quickly than they’d ever thought possible. All relied on cloud scalability, low-risk experimentation, and fast insights—and the technologies that support these capabilities.

We saw a life sciences company deliver the first clinical batch of its COVID-19 vaccine candidates for the Phase 1 trial just 42 days after the virus was sequenced. A health organization engineered a new way to diagnose pneumonia earlier in the disease progression to provide care even before a COVID-19 diagnosis is confirmed.

Outside of healthcare, there was the videoconferencing platform and virtual education provider that scaled to support double-digit growth. There was the convenience retailer that staved off fast-dropping revenues by quickly assessing new customer needs, ultimately boosting per-customer purchases of these products by 25%.

For those who’d already done the initial transformation of their data, opportunity met preparation. These businesses were ready to act quickly to use data, AI, the cloud, and agile practices to adapt to the changing markets.

Averting Future Crises

As we deal with the immediate and lasting impact of the COVID-19 pandemic, these same four components that have sped business response can be used for something that will become even more crucial: identifying the next crisis.

Whether for business or global health, the questions and answers may change, but the tools to prepare for and fend off the worst effects of anything yet to come are the same: better data, early warning systems, and fast response teams. By capturing early indicators and market signals (spending on fever reducers and cough suppressants, travel patterns to high-risk areas, abnormal results from water and waste system testing, natural language scans from urgent care facilities, etc.), we can catch issues before they become catastrophes.

Before the pandemic, we could be forgiven for not having the answers to the crisis we face today. But if we learn nothing else from these trying times, we know what it takes to prepare the foundation to better answer the questions we’ll face tomorrow.

Learn more about Cognizant’s Pandemic Response Challenge with XPRIZE, a $500K, four-month challenge that focuses on the development of data-driven AI systems to predict COVID-19 infection rates and to prescribe intervention plans.

Read our news release for a look at the finalists, or see Parts 1, 2, 3, 4, and 5 of our blog series. 

About the Author

Bret Greenstein

Senior Vice President and Global Head of Data

Bret leads Cognizant’s Global Data Practice, focused on helping chief data officers transform their businesses through data modernization. The Data Practice provides essential capabilities to help companies architect for and adopt cloud, embrace external data, increase accessibility of data and ensure data governance at scale. Bret also helps drive data transformation across Cognizant’s business units and participates in industry advisory boards to shape data policy, accelerate education and share best practices.

Prior to Cognizant, Bret led IBM Watson’s Internet of Things offerings, establishing new IoT products and services for Industrial IoT. He built his career in technology and business leadership across a range of roles throughout IBM in software, services, consulting, strategy and marketing, and as IBM’s CIO for Asia-Pacific. He has worked globally in these roles, including living in China for five years working with clients and transforming IBM’s IT environment.

Bret holds patents in the area of collaboration systems. He earned his bachelor’s degree in electrical engineering and master’s degree in manufacturing systems engineering from Rensselaer Polytechnic Institute. He can be reached at Bret.Greenstein@cognizant.com.