The Covid-19 Effect on Data Science and Data Analytics

The enormous impact of the COVID-19 pandemic is obvious. What many still haven't realized, however, is that the impact on ongoing data science production setups has been dramatic, too. Many of the models used for segmentation or forecasting started to fail when traffic and shopping patterns changed, supply chains were interrupted, and borders were locked down.

As a Covid-19 effect, cloud-based data platforms and data & analytics have a large role to play in this—from stabilizing the business to laying the foundations of new processes and predicting what's next. Therefore, it is critical that you have short, medium and long-term data-driven plans in place as quickly as possible to help make informed decisions.

Here is how Covid-19 will change the enterprise equations for 2020 and beyond-

Data Revision

The most dramatic scenario is a complete change of the underlying system — one that not only requires an update of the data science process but also a revision of the assumptions that went into its design in the first place. This requires a full new data science creation cycle: understanding and incorporating business knowledge, exploring data sources (possibly to replace data that doesn't exist anymore), and selecting and fine-tuning suitable models. Examples include traffic predictions (especially near suddenly closed borders), shopping behaviour under more or less stringent lockdowns, and healthcare-related supply chains.

Data Prosperity

Decision-making becomes more challenging during periods of stress, especially where there is uncertainty about the future. To remain successful, data must underlie every aspect of the business, providing critical input to readjust plans and predictions, as well as guide and automate decision-making.

Data has already played a significant role in the response to the crisis. Healthcare providers are leveraging data from countries that were affected earlier by the pandemic to forecast needs for hospital beds, masks and ventilators. Business retailers are utilizing point-of-sale (POS) data to help distributors identify and ship the items most important to their customers. And Telco's are using network traffic data to monitor and manage network capacity, build predictive capacity models, identify bottlenecks, and prioritize and plan network expansion decisions

Data Science Evolution

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.

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.