The race for the adoption of data analytics has been under way for years, but strengthened digital disruptors such as technological advancements and improved data accessibility, when paired with the COVID-19 pandemic, have, in just a few short months, catapulted an increasingly data-driven world forward a few years to data reliance and optimization.

COVID-19 has cast a nearly insufferable strain upon every facet of our society, undoubtedly influencing the way we will live, interact, and conduct business for decades to come. And, in a time when agility and resilience are necessary norms, companies that had already embraced data analytics found themselves positioned to combat the disease and its consequences with data.

It has never been more urgent for businesses to adopt data analytics. Insights from data analytics are required to surpass, or, in some instances, merely remain on par with, competitors. Companies leveraging data analytics in response to the pandemic have already progressed along the path to digitization by establishing a data ecosystem—the infrastructure, applications, and analytics needed to drive business intelligence, generate insights, and inform strategic decision making.

When overlaid with data governance, these businesses are able to benefit from the integration of data from multiple platforms and implement digital strategies that include data lakes, cloud computing, the Internet of Things (IoT), Big Data, and natural language processing, among others.

The abilities to process large data sets as often as constantly, deliver granular infection and immunity rates, make projections that present ideal disease testing locations, predict consumer spending patterns, and evaluate decisions regarding entire product and service lines make data analytics one of the key drivers of progress during the pandemic and highlight its broad reach and significance to businesses in, and out of, times of crisis.


Causing tragedy and disruption all over the world, the coronavirus has ravaged livelihoods and adversely affected business activities. As of mid-August, there are nearly 21 million confirmed cases, with an estimated 744,000 COVID-19-related deaths and an infection rate of more than 275,000 new cases per day worldwide. Family businesses and multinational enterprises alike have seen complete devastation, laying off employees, filing for bankruptcy, or closing operations permanently, while others are met with unimaginable surges in demand, depleting inventory and prompting supply chain complications that could take months, at best, to resolve.

Fortunately, a host of organizations were already well-versed in the power of data analytics when the COVID-19 pandemic presented itself. Communities, governments, and healthcare workers are leading analytics efforts to track the virus, help affected families, and try to halt the disease’s spread. While developing and executing plans to protect employees and support customers, companies are leveraging analytics to assess the extent of financial hardship faced due to stay-at-home orders, fear, and uncertainty.

We’ll explore use cases within various industries to show how the four types of data analytics—descriptive, diagnostic, predictive, and prescriptive (see “4 Types of Data Analytics”)—are being applied to deal with the COVID-19 pandemic and related issues.

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Recognized for its predictive power and problem-solving capability, data analytics has become an essential tool to fight the pandemic and counter its effects. No industry appreciates this more than the healthcare industry. Researchers and healthcare workers around the world have partnered together to make data transparent and accessible, fostering the generation of powerful insights in many ways.

Among those most noteworthy is the World Health Organization (WHO), which uses data to report confirmed cases, deaths, and recoveries by geography (see Figure 1). Perhaps the most widely visible depiction of the WHO’s descriptive analytics is the data visualization of a map with relative intensity of confirmed infections in various regions (see the dashboard titled WHO Coronavirus Disease (COVID-19)).

In addition to diagnostic analytics, the WHO uses other analytics models to predict infection, recovery, and mortality rates of the coronavirus. This information has been shared with governments and other relevant institutions to raise awareness of the disease’s severity, informing the magnitude of preparations healthcare facilities and other organizations make to receive and support patients.

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Researchers at Johns Hopkins University took this a step further and effectively modeled the spread of COVID-19 at the county level in the United States (see Figure 2). This modeling has proven critical for public health officials and policy makers to take appropriate containment and aid actions. Examples of global actions resulting from data that forecasts future infections are stay-at-home orders, social distancing guidance, small business relief, debt forgiveness, and stimulus packages, among a host of others.

See Figure 3 for prominent examples of the role of data analytics in the healthcare industry’s fight against COVID-19, including:

Medical imaging. In the earlier days of the pandemic, China faced a shortage of qualified imaging doctors who could perform COVID-19 diagnoses through review of CT features of the lungs, primarily due to the rapidly evolving nature of the disease. Through a collaboration of Huawei Cloud, Lanwon Technology, and Huazhong University of Science and Technology, computer-vision AI-based techniques were leveraged to accurately and repeatedly produce CT quantification results in seconds ( This development considerably improved the efficiency of diagnoses.

New drug development. Researchers use machine learning models to determine whether a drug will be effective in fighting the virus. Examples of inputs to these models are historical data for COVID-19 as well as data relaying the effectiveness of drugs in fighting other diseases.

Combating misleading information. Social media companies like Twitter used a deep learning algorithm to separate reliable and authoritative sources from unreliable ones to reduce the spread of misinformation regarding the disease and how it is contracted.

Modeling infection severity. Because there weren’t enough resources for everyone to get tested regularly for COVID-19, it became critically important to gain more information about the possible outcomes of the spread of the disease to make a best effort at allocating medical resources appropriately. Data analytics is responsible for models of the likelihood of an individual being exposed to COVID-19 (infection risk); the chance that, if infected, the individual might require hospitalization, intensive care, or a ventilator (severity risk); and the probability of an infected person dying from COVID-19-related complications (outcome risk).

Effective models leverage data such as age, preexisting health conditions, social habits, hygiene, location, climate, etc., to create a vulnerability index, predicting the risk of an individual contracting COVID-19. Equipped with these factors and the degree to which they meet the description of a statistically significant population of COVID-19 patients, predictive analytics enables chatbots and machine learning to facilitate self-screening for infection risk, and prescriptive analytics empowers medical professionals to develop personalized treatment plans for patients.

This data and its resulting insights also inform additional layers of precautions that should be taken for certain demographic groups, such as restricting access to nursing homes to reduce risk for the elderly and advising persons with chronic respiratory conditions to remain at home even as businesses reopen in their area.


Prior to the coronavirus pandemic, the finance and accounting profession was already well along a transformational shift from a predominant focus on record keeping, control, assurance, and reporting activities to automating many of those tasks in order to spend an equal or greater amount of focus on analysis and strategic decision support. A key component of this journey remains the upskilling of finance and accounting professionals with a heavy focus on digital technology and, of particular importance, data analytics.

During the pandemic, finance functions across the globe have played a critical role in their respective organization’s recovery. Informing key business decisions through insights derived from data analytics, finance teams have leveraged their unique access to financial and nonfinancial data, enabling their organizations to better manage cash flow, identify top products, forecast demand, assess employment levels, and evaluate financing and investment options, to name a few.

Delinquency risk. In financial services businesses, utility companies, manufacturing organizations, and consulting firms, finance teams have leveraged predictive analytics to evaluate accounts receivables and customer account history to identify those presenting the greatest risk of delinquency. Calculating ratings that inform the ability to pay and the propensity to pay, financial professionals use the results of this analysis to recommend tailored communications during proactive engagement of customers to make payment arrangements and offer short-term relief, where feasible.

Cash management. The responsibility of managing cash generally resides with the finance and accounting department. During this time of extreme uncertainty and drastically reduced sales for many organizations, cash and working capital management present one of the greatest opportunities for these teams to employ sensitivity analysis, determining how changes to a variable may affect the outcome if all other conditions remain the same.

In addition to monitoring days sales outstanding and future invoices that will be due, accountants are reviewing overall present and future cash inflows and outflows to determine the impact of a host of cost and revenue drivers, such as manufacturing a new product that’s in higher demand than existing products, an increased cost of raw materials, curtailing specific marketing activities, or offering free or deeply discounted services. This analysis informs the business of how much cash is needed to maintain limited, or increased, operations, provides guidance on the potential need to secure additional funds, and forecasts cash implications of pulling other operational or business levers.

Financial results. Projected asset valuation and profit and loss are crucial to management decision making during recovery. Financial models, such as scenario analysis, change internal and external variables to generate most likely, best, and worst-case scenarios. Examples of these variables are (1) reduced sales revenue because a stay-at-home order is issued and services can’t be rendered to consumers who are inaccessible and (2) an unexpected surge in demand for a product, for example, toilet paper and paper towels. The results of this type of analysis can support anything from inventory management to product pricing and new product or service offerings.

Employment decisions. Scenario analysis can also inform employment decisions. Unfortunately, many businesses have been forced to furlough or lay off tens, hundreds, and thousands of employees during the pandemic. Results of scenario analysis have informed the number of persons released by restaurants facing forced closures or consumers uncomfortable with dining; energy companies dealing with the effects of drastically reduced demand for oil and gas; and travel, entertainment, and tourism businesses of all sizes coping with negligible consumer activity. Alternatively, scenario analysis has advised delivery companies and grocers of how many additional persons they need to hire to meet unprecedented surges in demand. These insights have been generated by accountants and financial professionals while serving as business partners with other departments—human resources, operations, etc.

Garnering insights. Mastercard’s recently launched Recovery Insights initiative has delivered financial and operational results impacting organizations across the public and private sectors. This initiative offers research, tools, and innovation empowering organizations to leverage Mastercard’s insights to make more informed business decisions. With a goal of “enabling smarter decisions with better outcomes,” Raj Seshadri, president of data and services at Mastercard, said its insights “are helping apparel brands refine their inventory to address the rise in e-commerce, grocers fine-tune store hours to give at-risk shoppers peace of mind, and governments guide services to fuel local economies” ( An example of a finance and accounting use case for this analytics data is New York City’s use of geographic spending pattern changes (by neighborhood) to forecast the impact of consumer spending on the city’s sales tax revenue, resulting in more informed budgetary adjustments.


The retail industry uncovered the power of data analytics decades ago. But modern technological advances have exponentially increased the volume of data retailers can process and the speed with which they can process it. With a well-established culture of data reliance, retailers predict consumer behavior with structured data and expose reasons for this behavior through analysis of unstructured data.

When combining structured data (most often captured at the point of sale) with unstructured data, collected through anything from tweets and photos to hashtags and reviews, retail leaders are able to garner insights regarding consumer preferences, engagement, demographics, interests, trends, and more. And these insights, during the pandemic, are enabling strategic decisions around product and service offerings and even staff scheduling (e.g., more employees working shifts during busier times).

Information deficit. Already accustomed to making strategic decisions and shaping tailored customer experiences through the use of data analytics, the COVID-19 pandemic has placed the retail industry in uncharted territory as it’s now presented with an information (or data) deficit. Along with deflated revenue and eroded profit margins, retailers now face the challenge of a sudden absence of sufficient usable data, forcing them to learn who their current customers are (new customers or customers from before the pandemic), what purchasing decisions consumers are prepared to make while facing financial hardship, and when it might be safe for brick-and-mortar locations to resume “normal operations.” Thankfully, data analytics is proving beneficial in providing resolutions to these challenges as well.

To combat the lack of relevant seasonal historical data (for the same month or season of the prior year, for example), retailers are looking to exponential smoothing models to prepare forecasts and observe trends as they’re being formed. Whereas a simple moving average approach to forecasting weighs past observations in the time series equally, exponential smoothing smooths time series data by exponentially decreasing the weights over time. Thus, more recent time periods are weighted more heavily, which is crucial in the pandemic-induced environment of volatile consumer behavior. Now, sales forecasts can be prepared on a daily or weekly basis using spending patterns during the most recent periods of the pandemic to project short-term activity.

Retail analytics applications. Retail organizations that wisely accepted that their pre-pandemic models were unlikely to provide reliable insights now or in the near future used descriptive analytics with their limited COVID-19 data sets to assess who their current customers are and what products they deem essential in today’s environment. Predictive analytics allows retailers to leverage AI to make product changes and purchase recommendations.

Historical data from previous periods of economic hardship is also being used in diagnostic analytics to provide a glimpse into consumer behavior, while prescriptive analytics uses this data to allow retailers to evaluate temporarily or permanently discontinuing product lines, weigh alternative solutions for supply chain issues, and understand how to safely and profitably reopen physical locations (an issue also experienced by restaurants and others in the hospitality industry).

Spatial analysis. When providing guidance to retailers, Angel Evan and Amber Rivera in the Harvard Business Review suggest pairing spatial analysis, analytical modeling of data geographically, with local health guidance and the WHO’s data on relative intensity of confirmed coronavirus infections also presented geographically ( This pairing enables retailers to appropriately prioritize reopening or closing specific locations and assess the pace at which consumers in those respective areas might be comfortable visiting brick-and-mortar locations, even with restrictions.


The broad reach and wide applicability of data analytics across industries and professions have been known for decades, but the essential role of data analytics in delivering a competitive advantage to businesses, efficiently enhancing value delivery, and revolutionizing the role of business professionals is now undisputed. Even more, the enormity of the impact data analytics can have in times of crisis is nearly unparalleled, as evidenced by its contribution to progress during the COVID-19 pandemic.

The power of data connects researchers from all continents, bonds small business owners who have never met, forges alliances between local and national governments, and empowers business professionals, particularly in finance and accounting, to position their organizations to survive, and, in some instances, thrive. Data analytics provides hope to overwhelmed businesses, confidence in the future, and optimism for a cure.

Businesses that have yet to embrace data analytics will find themselves falling behind peers and, over the long term, working harder to survive. Management accountants and other business professionals unwilling to act now to upskill will find themselves trailing in the race for relevance and may, consequently, appear less competitive than their peers who chose otherwise.

The future of business is data. And the future of business functions within organizations—accounting, finance, economics, marketing, etc.—is data analytics. Organizational leaders are demanding agile and efficient delivery of data-backed insights and recommendations from their leadership teams. Thus, functional teams supporting these efforts must be equipped with data analytics skills to deliver.

As you continue to support your organization through the pandemic, seek out ways that the use of data can elevate the insights your team delivers. Develop and strengthen your data analytics skills so you can enhance the value you deliver personally. You’re certain to find that your contribution extends beyond an improved bottom line. The power of data is limitless. The reach of data analytics is the future.

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