Covid-19 has impacted the world a lot and data analytics services are helping the world to stand up. But how are we approaching data analytics during this pandemic? How are we handling the data analytics process? Has the data analytics community failed to learn a lesson from this global pandemic?

It has been more than 7 months since Covid-19 began terrorizing lives and businesses on a global scale. Dashboards and analytics are helpful in conveying the data to public and influencing the government’s decision making. Data literacy and data collection among consumers has progressed very well during this pandemic. Data analytics is also helping in business contexts, enabling the employers understand their organization-level and workforce needs.

This article explains what the Covid-19 pandemic has taught the data analytics community.

What Covid-19 has taught Data Analytics community?

1) Understand the context of data

It is important to analyze the context of data after the data collection. The collected raw data form the base of any subsequent analysis. So, the data quality establishes trustworthy conclusions.

A recent research paper published in The Lancet is an example of unreliable data source during the Covid-19 pandemic. The paper highlighted that administering Hydroxychloroquine on Covid-19 patients increased the heartbeat fluctuations and death rates. This resulted in stopping different Hydroxychloroquine trials. Later, the Lancet retracted the paper since the authors were no longer able to establish the authenticity of data sources. And it has had a negative impact on the Covid-19 treatment landscape. Finally, Covid-19 has shown that accurate and reliable data is an absolute necessity.

Extra care is necessary while understanding the data collection process and knowing more about the different data sources. It is good to clean and process the data considering the existing ambiguity and uncertainty. Besides, the data collected can bring new implications to the data interpretation.

2) Ensure availability and timeliness of data

Availability and timeliness of data is an important aspect when preparing for the unknown. at a rapid pace. Experiences and expectations have contributed a lot to that learning process. Furthermore, organizations make data-driven decisions in hours. Appropriate analysis is necessary before the value of data fades. In the same way, capturing the data in time is vital to minimize the risk of missed opportunities.

3) Forecast future scenarios

Data Science and Machine Learning are important factors in decision making, considering the vast data volumes and cheaper computing power. They are useful in generating basic descriptive and diagnostic analytics, to complex predictive or prescriptive analytics. It helps businesses to learn quick, plan before desperate times, and take immediate action.

People wanted to use the power of machine learning to prepare themselves for the future, ever since Covid-19 appeared. However, there was not enough data or past data patterns on the global pandemic. Consequently, data analysts had to work from the scratch during the early days of Covid-19 pandemic.

Data analysts could use other methods such as scenario modeling to examine and evaluate the possible future events. In addition, determining new ways to model the unknown would be pivotal to success among data analytics community.

4) Tell stories with visualizations

Charts and graphs are commonly seen everywhere these days. People keep sharing the numbers they see on the news and visualization skills have been booming with multiple tools around.

Data literacy is an important aspect, but it is not just about designing thoughtful charts or developing complex data functions. Data literacy involves interpreting data results and gaining the insights, apart from data cleaning and data transformation. Many times, visualizations do not relate to interpretations or stories. As a result, the help of subject matter experts is required to decode the visualizations. So, analysts must explore the data deeper and think more on how it can improve business processes, efficiencies, or any operational decisions.

5) Act on the data insights

Gaining the data insights is all about the business actions and decisions to be made. Analytics team spends more time on the data collection, cleaning, and visualization sections. But it is important for the team to think ahead and know how the insights are applied.

Businesses can survive and thrive during challenging times if they can set the front foot in data analytics. Innovation, collaboration, and speed would be the key to success. And enterprises should focus on how to utilize every possible data available.