Data science to reorganize businesses after the Covid-19 era
By Prof. Mayank Mathur, Associate Professor of Practice, Operations, FLAME University
In the modern age, living in a developing or developed country, we have to agree that the meaning of data is no longer just a record in one place, in a file. Data in the information management world is an integral part of every consumer, every business, regardless of whether it is used for growth or not. Many scientists, engineers and business analysts around the world play and live with data throughout their lives. We relate to the data, information and related ideas that surround us. We understand that data has driven technological advancements in almost every industry over the past two decades.
However, perspectives and paradigms have completely changed in every industry now during this decade. The world stopped in 2020 and started to pick up again from a very different perspective. Covid-19 has impacted almost every business, individual, and human in one way or another. Since then, companies have strived to find the “new normal”. There is a need to adjust business plans, revenue sources and reorganize the associated technological infrastructure after reaching the new post-Covid normal. For example, the evolution of the education technology industry over the past 18 months is a remarkable leap forward.
It is not only technological advancement, but also data science that enables giant leaps in the implementation of technologies more suited to each industry. Let’s expand the paradigm and see some definitions.
Data science refers to the science of examining raw data in the form of sets of data to arrive at conclusions. These conclusions are based on the information contained in these datasets. Data science is an interdisciplinary field that uses scientific methods and algorithms to derive information from structured and unstructured data and apply actionable decisions in various applications. Data science is more of a set of skills to train machines to help businesses make better decisions. A business must understand that it can only do better or innovate when it anticipates demand. The challenge is to know how to implement it with current commercial operations. We might have some ideas on what could make the business successful next year, but most business leaders don’t know what the world will be like next year. The current Covid-19 situation has created even more complicated challenges for businesses to operate according to the original business plans.
Data science, in principle, is a very powerful source of technology that is helping every business and individual to fight this pandemic. However, you have to be a little careful and make sure that the experts who use the technology are as involved and informed about the advancements as the technology is. Data scientists and data analysts need to make sure that they react responsibly to the situation because it is changing very quickly.
Let’s move up a gear to understand data science and data analysis in the world beyond Covid-19. Without a doubt, the pandemic has left many industries and organizations within those industries in dire straits. On the flip side, these times have also taught us how to effectively use the power of data science and data analytics to tackle challenges. With everything around us changing, many existing models based on historical data may no longer be valid. Then it brings better and tighter obstacles to the table. This will require reinforcement learning and create a demand for data analysis more than before.
Consider an example of industries where data science helps plan business growth and predict the future of businesses.
One of the most common real-life examples of data science is the manufacturing industry. Global organizations depend on data science knowledge to create product demand forecasts. This helps them make their supply chain operations and order delivery more efficient. Data science can save a manufacturing company a lot of savings, especially in terms of optimizing costs around the supply chain. Some benefits of implementing data science in manufacturing organizations:
- Data science minimizes the risk of parts not being delivered and stored on time.
- Data science in supply chain optimization takes into account shipping costs, material availability, weather conditions, market scarcity and many more that can influence the whole process.
- The organization will be able to analyze customer needs and behavior and demand for products using data analysis. The results of the analysis are important in identifying which products have the highest order in the market.
- By using the forecast and learned conclusions appropriately, organizations can optimally allocate resources and better control spending.
Another important example of data science: predictive analytics in healthcare. The predictive model analyzes historical data, learns from it, identifies trends and generates accurate forecasts based on those trends. The risk of visiting a place like a hospital increases the vulnerability of individuals due to the higher likelihood of the Covid-19 virus being present in hospitals and health centers with Covid-19 services, so that everyone wants to use the digital space in healthcare to explore better services and delivery systems. Here’s how data science in healthcare is helping hospitals:
- Find multiple correlations and associations of symptoms among all users on the health portals.
- Improve patient care by personalizing them and creating records.
- Improve the efficiency of the supply chain and pharmaceutical logistics for better communication with patients.
- Predict deteriorating patient health, provide preventative measures through videos and live sessions, and use digital content to initiate therapy early.
Retail e-commerce industry (eTail)
A few years ago, everyone was visiting the same mall, a place with indoor fountains, a jewelry kiosk, a retail store with clothes, accessories and electronics under one roof, and then a workshop. bodywork, and finally a few joints to hang. outside. Today, however, shoppers can shop at their personalized digital mall and choose the same items with ease and in a more personalized format without going to a mall and without risking a Covid-19 infection or the hassle of due instructions. This has become more important in the post-pandemic era, with people still shopping for the items sitting in their living room and experimenting with the artificial intelligence of digital platforms to provide an experience similar to the physical visit. Online retailers automatically tailor their web storefronts based on viewers’ data profiles. Personalization and profiling of individuals is a very common concept in the world of e-commerce. Adjusting layouts and personalizing featured 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 afford and a practice called personalized pricing. Even websites that don’t sell anything offer personalized ads.
Education Technology Sector
After Covid-19 hit the world and closures closed all schools and educational institutes, the industry moved most of the world into emergency distance learning situations. Schools around the world have turned to video conferencing tools, e-learning management software, and related digital solutions to keep it running. These distance learning experiences have highlighted the barriers to digital learning adoption and education around the world. The education industry continues to improve the use of software and data science to predict the use and efficiency of delivery systems. Data science helps identify the overall impact of existing tools and software and make better use of technology to improve the learning experience. The following steps are chosen in the electronic technologies sector to enable better educational systems and platforms:
- Smart uses of digital devices or software to enhance learning inside and outside the classroom.
- Intelligent uses of data produced or collected in formal education to personalize learning and improve educational decision-making and policies (analysis of learning based on educational and administrative data, research and evaluation , policy design).
- Personalization of learning and improvement of decision-making and AI-based policies in education.
- Big Data-Driven Learning Analytics: New uses of personal data collected through internet browsing, social networks, and networked devices and sensors to personalize and enhance the educational experience of people.
The pandemic has been an absolute upheaval for all of us. It wasn’t just a wake-up call that was the unexpected everyone had thought of before. It turned out to be a bitter reality as it became a painful and costly time for business. It offered an opportunity to a few others that few organizations have been able to capitalize on. Either way, business leaders and owners must accept that the changes induced by Covid-19 in management, operations and budget priorities will be there for a longer period. This provides a great opportunity for world leaders who might survive the hardships of the pandemic to use their experience to integrate this digital and AI transformation into their business priorities.
This new world leaves no time for nostalgia or a return to the “normal” pre-Covid times. Times are changing faster than we think, and speed and determination to change are key. Decision making saw a new paradigm of new standards and therefore new implementations. World leaders need to start thinking differently and not wait for things to get back to normal. Take calculated risks and the opportunities are endless.
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