How has Data Analytics Been Used During the COVID-19 Pandemic?

The COVID-19 pandemic has changed the way people around the world live, study, socialize, and work. The pandemic continues to alter the day-to-day activities of many Americans, a trend that is likely to continue even post-pandemic. In the past two years, many individuals have begun to work from home, which has led to businesses conducting much of their activities digitally rather than in person. In addition, there has been a huge increase in online business transactions, including grocery shopping.

In addition, more people than ever have gravitated toward online education, online entertainment and gaming, and online shopping. In the two-week period between March 2 and March 16, 2020, for example, there was a more than a 1,000% increase in the number of educational apps being downloaded.

As more people relied on digital platforms for daily tasks, there has been a huge spike in the amount of data being collected by organizations. This data is collected so that it can eventually be analyzed and used to shed insights into customer purchasing preferences, boost sales, and improve product offerings. However, with the onslaught of data comes challenges, such as how to transform this huge backlog of data into helpful insights that can lead to action.

This article will explore how the field of data analytics has changed during the COVID-19 pandemic, as well as what can be expected in the years to come in this field.

How The Field of Data Analytics has Changed During the Pandemic

The field of data analytics is undergoing a rapid evolution amid the COVID-19 pandemic. Here are just a few of the changes currently underway:

• Data analytics is more important than ever. During the COVID-19 pandemic, it’s become increasingly common to see dashboards that illustrate the spread of the virus, as well as the number of vaccinated individuals in a specific geographical location. Data analytics also became more prominent in the public eye to provide predictive models pertaining to pressing issues, such as how much protective equipment a healthcare organization should purchase, as well as what measures a struggling business should take to ensure its survival.

• There’s an increased reliance on external data. During the pandemic, outside factors have led to disruptions within businesses. Internal data about past practices is no longer sufficient to forecast future trends. For this reason, many organizations are relying on external data to learn more about such topics as how customers are behaving.

• Data analytics has become more flexible than ever. The importance of real-time information during the pandemic has caused data analytics to be much more flexible than it was in the past. The need for this flexibility has created the corresponding need for more agile analytics technology. During the pandemic, it’s become vital for Data Scientists and Data Analysts to quickly create predictive models. Unlike before the pandemic, when analytics tools were designed for their stability, they are now being created with flexibility in mind so that they can be tailored to quickly changing circumstances.

• Organizations are beginning to incorporate scenario analyses into their prediction models. The pandemic has made organizations realize that it’s important to have a plan for a range of scenarios, not just one prediction. A variety of factors must be considered when looking at outcomes like the functional end of the pandemic, such as vaccine distribution, levels of virus immunity, and the potential impacts of therapeutic interventions. These variables affect how people live and work, and often are relative to their geographical location. By modeling a range of possible scenarios, a business can better prepare for a range of possible future events.

• Fraudulent activity is on the rise. The COVID-19 pandemic has led to a sharp increase in the number of cyberattacks. As the number of in-person interactions has decreased, the instances of people stealing data, spreading malware, and impersonating others has risen significantly. The use of expanded analytics allows a business to guarantee its customers that they are in fact who they claim to be.

• The remote working landscape has become a normal occurrence. One of the greatest challenges a business can face when its employees are working from home is to keep track of how they are performing. HR analytics allows organizations to monitor not just the performance but the health of its employees. By incorporating measures such as engagement surveys and onboarding checklists that can be completed anonymously and then be analyzed, businesses can spot areas for improvement as well as take note of trends while still respecting the privacy of their employees.

• There’s a shift from on-premises work to cloud-based operations. During the pandemic, it’s become increasingly popular for organizations to allow employees to work at home. This has led to more businesses moving their workloads to the cloud and optimizing SaaS models, as well as managing data entirely off-premises.

• It’s more important than ever to understand the customer. Since more customers are shopping online, more online messaging is being used to target them. In order to remain competitive, a business must understand who their customers are so that they can devise fitting messages to best connect with them. If they do not target their customers in a timely manner on an appropriate platform, revenue will be lost. This is why those who perform behavioral analysis on data have a clear advantage over the competition.

• There’s a greater focus on AI and machine learning technologies for predictive analytics. Before the COVID-19 pandemic, it was typical for an organization to focus largely on past events when collecting data. However, much of the data collected from the year 2020 has created challenges for organizations hoping to gain insights. This is why many businesses are now moving beyond the data collected internally about past events. More than ever, there’s an emphasis on predictive analytics, which can be optimized by incorporating machine learning and AI technologies.

How Will Data Analytics Fare Post-COVID-19?

The COVID-19 pandemic has demonstrated the vital role data plays in our daily lives. It’s become apparent that organizations must be able to react quickly to catastrophic situations, and need to have the necessary tools with which to do so.

Data analytics plays a more important role than ever in many organizations, as well as in providing the public with information about the effects of handwashing, vaccinations, and other health-related information. Not only do those in the government rely on this data to make better-informed policy decisions, but the media also uses the insights derived from data to communicate important health information.

It is likely that the economic and health impacts of the pandemic will be felt for years to come. And it is expected that data analytics will continue to play a crucial role in the way we collect, understand, and share insights from data. It will continue to be an important tool to empower organizations to quickly adapt to changes and make adjustments to ensure their business doesn’t just survive but can thrive in uncertain times.

Get Started Learning Data Analytics with Hands-On Classes

Are you interested in learning more about data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.

In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $27,500.

Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.

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