Data Science – How It Is Shaping the Post-COVID Business Ecosystem

Data Science- How It Is Shaping the Post-Covid Business Ecosystem

The Evolving Role of Data Science Teams amid COVID-19

Businesses across different sectors are tightening their belt due to ongoing economic
challenges brought by the pandemic. As a result, most organizations are primarily focusing
on preserving cash flow lately. However, what they do not realize is the supremacy of
investing in data science and developing their teams in the current situation. The evolving
data science applications can boost business continuity as well as growth amid uncertain
times. The significance of shifting data science roles can lead to the effective
implementation of business solutions.

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Data Science Boost Forecasting and Simulations

The potential of the second wave of Coronavirus is still a reality. Hence, businesses need to
utilize historical data from this situation of the pandemic in order to anticipate how to
sustain efficiently and act on future events. For instance, even with three months of
consumer behavior data, organizations can imitate different business outcomes amid any
unfortunate phase. Businesses can utilize data science tools to build a simulation model of
operation directly with their consumers – integrating it into the business continuity strategy. An AI-powered business model can facilitate companies to stabilize and drive enhanced outcomes.

The Function of Data Science Teams is Evolving

Undoubtedly, the steep rise in demand for data science across industries drives the need for data scientists. Though hiring has been slowed down for many businesses, especially in the tech sector, the need for data experts is still high as the role continues to evolve. For
instance, the part of the Algorithm Translator is progressively more in-demand as businesses are becoming more data-driven. Translating industry problems to data problems is a top priority to identify data answers. The data need to be articulated into actionable intelligence for decision-makers to apply. Basically, the problem statement needs to break down into use cases and connect them with the right data set. This is to understand the limitations of the data sources to solve with analytics.

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Similarly, data engineers’ roles are also increasing in importance since the data continues to grow exponentially. Data gathering is an essential step in a company’s data journey, and a majority of the data is dumped into databases. Sadly, in many cases, they remain there
without being mined – ever! Hence, the demand for data engineers is surging to combat this “ignored process” and to make data accessible as well as actionable. In this unprecedented time, this role is highly significant, as companies may miss out on crucial data insights from the past few months.

Clearly, data science is no longer limited to selected departments of a business to deal with. With the widespread crisis in the business ecosystem, various teams working in different industries slowly comprehend its value. As applications grow, data science will become more analogous – and more organizations are expected to realize that the accurate way to operate is by adopting data-driven approaches.