The Future of Data Science in the Age of COVID-19

Introduction

2020 will go down in history as one of the most challenging years in which the novel coronavirus infected millions across the world. Researchers are conducting and comparing assessments to what is happening now versus the impact of the 1918 flu. The difference between then and now is technology. Today, we can assess the spread of the virus, how it is mutating, and its effect on international economies. For example, with data, we can see real people moving around in places, and monitor behavior changes when authorities implement lockdown policies. All this happens in real-time.

Gaining an in-depth view of COVID-19 is possible because of Data Science, along with advancements in computing power and analytical techniques. Data-focused analytics play an increasingly essential role in tracking and recording global cases of the pandemic. For instance, Google has created maps that display stats on verified cases, deaths, recoveries, and predictions on how the crisis will evolve in the coming years.

The State Of The Pandemic

Chances are almost everyone is using a map like Google’s to stay informed on coronavirus news and latest reports. The fact that Data Science and cloud-based analytics make all this possible is lesser-known. From helping us maintain business operations to prioritizing our approaches and laying the foundations of streamlined processes, Data Science is the key to success.

Here is what you need to know about the future of Data Science in COVID-19 and beyond.

Healthcare 

Why is Data Science the career of the future? Here’s your answer. The demand for healthcare providers is rising everywhere as governments urge communities to contribute to the front lines. Professionals are already leveraging Data Science analysis techniques to conduct R&D on the virus. They use reliable information from organizations such as WHO to create interactive maps like that of Google’s which people refer to every day. Data visualization in healthcare helps specialists understand how the pandemic is progressing and provides access to the latest stats. Even ML and AI as a technique feed on data to provide actionable findings.

By opting for a career in healthcare analytics, you can generate accurate reports, not to mention detailed maps of red zones and safe locations. It becomes easier to study data and conduct qualitative as well as quantitative analyses of healthcare reports from countries affected earlier. These measures will be useful in initiatives like predicting needs for masks, hand sanitizers, hospital beds, ventilators, and other medical equipment. As a result, hospitals, clinics, and insurance companies will urgently require more data scientists to process healthcare-related information that reveals critical trends and patterns. Data scientists should be at their best, and the healthcare industry will invest more in the following fields:

  • Statistics
  • AI & ML methods
  • Storytelling, delivering meaningful analytics and insights
  • Data-oriented problem solving
  • Developing custom products and services, such as vaccines
  • Communicating findings and results, publishing studies
  • Data manipulation and developing intelligent algorithms
  • Hardware for collecting data, sensors, and database management solutions

Digital Marketing

With everyone going remote, we can expect significant growth in digital marketing post-COVID-19. Regardless of industry, every business requires a digital marketing strategy to increase their presence on the web and create a compelling online value proposition. SEO, blogs, social media, local search, organic search, and Google AdWords enable companies to reach as many customers as possible. Digital marketing offers analytics that translates customer habits and behavior into valuable business information. For instance, an SEO expert can quickly pull up analytic reports to test ad campaigns and identify what customers are browsing, reading, or purchasing.

Digital marketing offers a variety of communication strategies that focus on helping businesses acquire clients, retain them, and create loyalty programs. In this case, AI and ML solutions are essential. Let’s look at a few examples. AI analyzes buying behavior and the decision-making of target audiences. AI-driven data analysis formulates successful marketing strategies and provides what customers truly need. Moreover, AI improves accuracy and precision when experts deeply analyze data for meaningful insights. We have AI voice assistants or software agents with the ability to carry out tasks based on commands.
In terms of ML, you can feed computers with quality data and train them through ML models that run on various algorithms. ML offers fast and accurate results for businesses to capitalize on profitable opportunities and prospects. Combining these solutions with AI in digital marketing makes it even more effective when processing large chunks of information.

Analytics

Will Data Science last? The answer is: yes. We can expect to see the rise of robust economic engines for Data Science, AI, and ML research in 2021 and beyond. There will be common interests surrounding data collection, analysis, modeling, and prediction. Companies will rely on these technologies to recover from the hard-hitting effects of the pandemic and drive business continuity or WFH initiatives.

Let’s explore this further. The coronavirus is the first pandemic that depends on sophisticated technologies to help us navigate the unknown. As government and health authorities issue directives for lockdown, almost everyone has gone entirely online. Now, we work from the comfort and safety of our laptops and smartphones. Companies are equipping themselves with an automated supply chain to enable quick delivery of essentials, from edibles to healthcare equipment and hobby kits. AI powers many logistics systems while streaming giants like Netflix use smart algorithms to offer recommendations according to our preferences. It is the explosion of Big Data that led to this era of remote work, home delivery, and entertainment.

So, businesses will focus more on the following:

  • Managing the supply chain lifecycle
  • Accessing the operational impact of COVID-19
  • Developing simulations based on historical recession datasets
  • Conducting social media sentiment analysis of queries and concerns related to the pandemic
  • Using POS data to assist distributors with identifying and shipping the most crucial items to customers
  • Analytics investment that is driven by the need for efficiency measures and low-cost customer support platforms

Global Big Data Analytics

According to Cision, the Global Big Data Analytics Market was worth the US $37.34b in 2018. This figure is likely to touch the US $105.08b by 2027 at a CAGR of 12.3% from 2019 to 2027. If we think about it, these numbers are not small. So, what brings about the difference?

Today, embedded ecosystems have contributed to the rise of IoT which translates into a hyper-connected world. Ubiquitous networks enable IoT to connect endpoints and disclose valuable data. The volume and value of data empower decision-making like never before. It helps draw, visualize, and use intelligence in near real-time especially when meeting time and mission-critical objectives.

Global Big Data analytics brings data discovery and visualization as well as advanced analytics. Even before COVID-19, investors worldwide were showing a keen interest in both groups, and funding attracted millions of dollars. Research unveiled use cases across industry verticals and niches. Businesses began to form industry partnerships across countries and provinces to fulfill the growing need for these solutions.

As we navigate the pandemic, the Global Big Data analytics market becomes even more crucial. This is because of the operational benefits of Big Data analytics allow Data Science engineers to make more informed decisions.

Conclusion

What is next after Data Science? One thing for sure is that Data Science future trends appear to be exciting because technology is working for us in ways like never before. Statistically speaking, the prospect of Data Science is promising especially as we adapt ourselves to a new normal. The COVID-19 pandemic has increased the prominence of technologies that we use to obtain high-resolution data on countries and cities. So, data engineers will contribute even further to such causes, especially considering that the virus is not likely to go away anytime soon. What we can do is leverage the vast data resources we have to ask the right questions and find some useful answers.

An Ideal Plan For Implementation Of Big Data Analytics

Big Data Analytics best practices are useful for enterprises looking to extract actionable insights from user data that they collect through various channels. Data-driven solutions ensure infrastructure security and make it possible to perform effective market segmentation and capital allocation. There are multiple factors involved in the implementation of Big Data analytics and we would like to go through them one by one.

What are the Key Metrics?

The most essential factor is to identify the key metrics that must be monitored for your project. Then, decide the kind of data that is relevant for your business. For example, conversion rates will be the center of interest if you work on market segmentation. Similarly, analyzing a website means that bounce rates will matter. In any case, because metrics constantly change, it is always a good idea to pinpoint and analyze them for increasing the chances of project success.

Preventing Human Error

Overlooking small aspects can lead to losses in the thousands because many critical decisions are taken based on data analytics. On the other hand, missing out on small data details, resources, merging rogue or inaccurate data, and uncalculated fields could compromise a complete data model.

A Great Dashboard

Creating a clear dashboard that offers transparent and visible details is the next step in the data analytics process. For this, stakeholders must understand advanced data designs and the hierarchy and relation between different fields. Focusing on these elements alone makes it very simple to design a simple and intuitive dashboard that works for everyone.

Choosing a Tool

A business analytics tool needs to handle huge chunks of data and create useful visualizations. Although most tools offer similar features, you must conduct thorough research to match the ideal one to your needs. For example, while visualization tools provide an understanding of the importance of data in a specific context, they may not be of much use when it comes to handling large databases. In addition to feature comparison, you should also settle for an option that best suits your budget.

Involve the Experts

Do not hesitate to ask business professionals or Big Data experts for suggestions before deciding to purchase any Big Data tool or implement a specific technique. Take note that approaching analytics from a business point of view is entirely different from an IT perspective. In any situation, seek recommendations from those who truly understand the value of Big Data in transforming businesses.

Conclusion

It is all about the needs of your enterprise and how Big Data would be useful for your organization. While usage varies from company to company, data analytics provides great insights that are suitable for repeat use especially when you have to conduct qualitative or quantitative research. For example, if you are running a marketing campaign for an online store, analyzing its customers or target audience’s interests greatly maximizes the chances of improving sales levels. How is this so? The relevant Big Data Analytics helps you create products or come up with promotions that are more likely to pique customers’ interest and impress them. Overall, it is more about involving your teams while deciding to implement Big Data and work smart in the long run.

Solving Business Problems With Data Science

In the past few decades, emerging technologies have led to revolutionary changes that enabled humans to implement responsible research and innovation. Today, many businesses are utilizing various technologies to enhance performance and revenue. Organizations are understanding the value of data more than ever and with this realization, the demand for Data Science expertshas seen a huge surge. Every aspect of routine business processes is now open to data collection. These include general operations, manufacturing, supply-chain management, customer services, customer behavior, marketing, and workflow management.
With the number of connected devices increasing rapidly, more companies are looking forward to automating their services. In such circumstances, data collected through different channels can be leveraged effectively to facilitate business growth.
The question is: as a business owner, how can you benefit from data science to overcome the toughest business challenges? Let’s start with the basics.

Thinking Out Of The Box

Every online activity leaves a trace of data. If you allow an organization to monitor your online activities, it adds them into some form of a data storage unit or a data warehouse. Such data is useful for businesses, as it can reveal valuable insights associated with customer demands, buying behavior, and even information to help solve business problems.
Now, let’s look at how Data Science is related to all of this. Data Science fundamentally observes, monitors, as well as manages data, and creates solutions based on what has been learned from the data. Subject matter experts gather all the data and suggest measurable and actionable insights for businesses. Having a Data Sciences expert onboard will enable you to identify niche markets or capitalize on untapped opportunities and apply data-driven decision-making techniques in everyday operations.
For example, companies gather consumer behavior data online and use them to create products and solutions that are most likely to attract their target audience. One can also make the most of customer interactions with support staff to improve the quality of services they provide. In a nutshell, Data Science is an umbrella term covering several technologies that work together to give you data-driven results that enable smart business decision-making.

Solving Problems

Data Science is valuable as it helps organizations operate more efficiently and enhance business strategies based on predictions as well as factual statistics. Predictive analytics is one of the most important aspects of Data Science which assists in forecasting future scenarios by learning from past scenarios and behaviors. Common examples include the annual revenue of a business, resources or departments most likely to leave an organization, roles open to automation, and factors increasing or decreasing revenue, etc.

Exploring Data 

Data Science features a variety of analytical processes and techniques to explore massive databases. You can implement the same processes and techniques to analyze consumer behavior with the help of patterns. It is then easy to validate customer behavior by applying these patterns to new data sets that can deliver further, more valuable insights. Primarily, all these efforts lead to useful predictions based on factual findings backed up by data.
Let’s approach this from another perspective. What about monitoring and analyzing the behavior of previous customers? Chances are that individuals from a similar background, age group, city, and similar demographics sharing the same behavior patterns will perform similar actions. For instance, imagine a scenario in which people from a certain age group or region tend to leave an online platform without making a purchase (assuming one is using Data Science to optimize their online store). Future visitors from the same age group and region will likely do the same unless the business identifies the reason behind this behavior and resolves it. Taking the necessary steps will not only improve the overall marketing and business initiatives. Doing so will enable an organization to identify the best or worst performing areas in the business and take necessary action.

Implementation and Continuous Improvisation

Data Science provides enterprises with effective ways to continuously improve business operations. Based on statistical data analysis, even if a strategy fails, one can re-examine data to discover the reasons or factors that affected implementation and results. A hit and trial method would give you the perfect set of strategies for your business. You can validate them regularly using data analytics, data mining, predictive analytics, and numerous other techniques that come under Data Sciences. For instance, you can opt for a hit and trial method in scenarios with limited test opportunities. A business can simply work through different tests until they come across one that works best for them.
Any problem in the world that involves people, facts, or numbers can become a data problem that one can tackle with the help of Data Science. Essentially, it is all about looking into available data and observing it closely to detect patterns that are going to repeat themselves in the future. This also allows businesses to stay informed and prepare in advance.
While it is not necessary to hire a Data Science specialist for your organization, doing so will allow you to easily outsource the role to a company with experts on board. For example, FiveRivers Technologies, our leading custom software development company has been specializing in emerging technologies since 2003. Our Data Science experts have helped both startups and enterprises improve their decision-making abilities and business services. We take care of all the hard work and you benefit from useful insights that allow you to plan for the future of work.

Global Markets Are Embracing the Change

The international market has changed and it is no way near to what it used to be a few decades ago. Almost all industries are transforming operations with connected devices and huge chunks of data coming their way regularly. Capitalizing on these trends as an SMB or enterprise will allow you to stay relevant and competitive in the For example, enterprises that realize the operational benefits of leveraging analytics empower their teams to better target customers and improve access to cloud-based models driving digital transformation. Almost all industry verticals are adapting by utilizing modern technologies to stay ahead of the competition, and we recommend you to do the same.