Data Science to revamp the businesses post Covid-19 era

By Prof. Mayank Mathur, Associate Professor of Practice, Operations, FLAME University

In the modern era, living in a developing or developed nation, we have to agree that the meaning of data is not just a record lying in a place, in some file anymore. Data in the information management world is an integral part of any consumer, any business, no matter it is utilised for growth or not. Many scientists, engineers and business analysts worldwide play around and live with data throughout their lives. We relate to the data, information and related insights all around us. We appreciate that data has resulted in technological advancement in almost all industries in the last two decades. 

However, the perspectives and paradigms have completely changed in each industry now in this decade. The world came to a halt in 2020 and has started reviving itself again in a very different perspective. Covid-19 has impacted almost all businesses, individuals, and human beings in some way or the other. Since then, the companies have been busy trying to find the “new normal.” There is a need to adjust the business plans, revenue streams and revamp the related technology infrastructure after reaching the new normal post- Covid. For instance, the evolution of the education technology industry in the last 18 months is a remarkable leap ahead. 

It is not just the technology advancement but also the data science that enables the leaps and bounds in implementing more suitable technologies for each industry.  Let us expand the paradigm and see some definitions.

 Data Science

Data science refers to the science of examining raw data in the form of data sets to obtain conclusions. These conclusions are based on the information contained in those data sets. Data science is an interdisciplinary field that uses scientific methods and algorithms to derive insights from structured and unstructured data and apply actionable decisions across various applications. Data science is more of a collection of capabilities to train machines in helping businesses for better decision-making. A company must understand that it can only do better or innovate when predicting the demand. The challenge is to know how to implement it along with the current business operations. One could have ideas of what could make the business successful next year, but most business managers do not know what the world will be like next year. The current Covid-19 situation has created even more complicated challenges for the businesses to operate as per the original business plans. 

Data science, in principle, is a very powerful source of technologies that helps each of the businesses and individuals to fight against this pandemic. However, one has to be a bit cautious and should make sure that the experts who use the technology are as much involved and updated with the progress as the technology is. The data scientists and data analytics leaders must ensure that they respond to the situation responsibly since it changes very fast. 

Let’s shift gears to understand Data science and data analytics in the world beyond Covid-19. Undoubtedly, the pandemic has left many industries and organisations in those industries in a difficult situation. On the other hand, these times have also taught us how to effectively use the power of data science and data analytics to address challenges. With everything around us changing, many existing models based on historical data might no longer be valid. Then it brings better and more stringent obstacles to the table. It will call for reinforcement learning and create a demand for data analytics more than before. 

Let us look at an example of industries where data science is helping plan the business to grow and predict the companies’ futures. 

Manufacturing Industry 

One of the most common data science real-life examples is the manufacturing industry. Global organisations depend on data science insights to create forecasts of product demand. It helps them in making their supply chain operations and delivery of the orders more effective. Data science can make many savings for a manufacturing company, especially in cost optimisation around the supply chain. Some benefits of data science implementation into manufacturing organisations:

• Data science minimises the risk that parts will not be delivered and stocked on time.
• Data science in supply chain optimisation considers shipping costs, material availability, weather conditions, market scarcity, and many more that can influence the entire process. 
• The organisation will be able to analyse the needs and behavior of customers and the demand for the products using data analysis. The analysis results are important in identifying what products are going through the highest order on the market.
• Using the forecasts and appropriately learned conclusions, the organisations can optimally allocate resources and better control expenditure.

Healthcare Industry 

Another important data science example – predictive analytics in healthcare. The predictive model analyses historical data, learns from it, identifies trends and generates accurate predictions based on those tendencies. The risk of visiting a place like a hospital increases the vulnerability of individuals due to the higher probability of the Covid-19 virus being present in hospitals and health centers with Covid-19 wards, so everyone wants to use the digital space in healthcare to explore better services and delivery systems. Here is how the data science in healthcare helps hospitals to:

• Find several correlations and associations of symptoms within the set of users on the health portals. 
• Improve patient care by personalisation and record building. 
• Improve supply chain efficiency and pharmaceutical logistics for better outreach to the patients. 
• Predict deteriorating patient health, provide preventive measures through videos and live sessions and use digital content to initiate the therapy early.

E-Commerce retail (eTail) Industry

A few years ago, everyone was visiting the same mall,  a place with some indoor fountains, a jewelry kiosk, a retail store with clothes, accessories and electronics under the same roof, then a body shop, and finally a few eating joints to hang out. Today, though, buyers can shop in their personalised digital mall and choose the same items with ease and more personalised format without going to a mall and risking themselves to a Covid-19 infection or any hassles because of the guidelines. This has become more prominent in the post-pandemic era, with people still buying the items sitting in their living rooms and experiencing digital platforms’ artificial intelligence to provide a similar experience as the physical visit. Online retailers automatically tailor their web storefronts based on viewers’ data profiles. Personalisation and profiling of individuals is a very common concept in the e-commerce world. Tweaking page layouts and customising spotlighted products, among other things, is a common way to attract more people to buy online. Some online stores may also adjust prices based on what consumers seem able to pay and a practice called personalised pricing. Even websites that sell nothing feature personalised ads.

Education Tech sector 

Post the Covid-19 hit the world and shutdowns closed all the schools and educational institutes, the industry moved most of the world into emergency remote learning situations. Schools worldwide turned to video conferencing tools, online learning management software and related digital solutions to keep functioning. These remote teaching experiences highlighted the barriers to digital learning adoption and education worldwide. The education industry is still improving upon the use of software and data science to predict the usage and effectiveness of the delivery systems. Data science helps identify the overall impact of the existing tools and software and better use technology to improve the learning experience. The following steps are being opted for in the ed-tech sector to enable better education systems and platforms: 

• Smart uses of digital devices or software for enhancing learning inside and outside the classroom.
• Smart uses of data produced or collected in formal education settings to personalise learning and improve educational decision-making and policies (learning analytics based on instructional and administrative education data, research and evaluation, policy design).
• Personalisation of learning and improvement of AI-based decision-making and policies in education. 
• Learning analytics based on big data: New uses of personal data gathered through Internet navigation, social networks, and networked devices and sensors for personalizing and improving people’s educational experience.

What Next?

The pandemic was an absolute shaker for all of us. It was not only a wake-up call that was the unexpected than anyone had previously thought of. It turned out to be a bitter reality as it became a painful, expensive time for the businesses to run. It has offered an opportunity l for a few others that not many organisations have been able to capitalise on. In any case, the business leaders and owners have to accept that Covid-19 related induced changes in the management, operations, and budgetary priorities will be there for a longer period. This brings a great opportunity for the leaders of the world who could survive the difficulties during the pandemic to use their experience to get that digital and AI transformation in their business priorities. 

This new world permits no time for nostalgia or going back to the “normal” pre-Covid times. Times are changing faster than we could think of, and the speed and determination to change are the keys. The decision-making has been seeing a new paradigm of new normals and hence new implementations. World leaders need to start thinking differently and not wait for things to go back to normal again. Take calculated risks, and opportunities are never-ending.