Data Science in the Post COVID-19 World

Mavis LohUncategorized

It is evident that Data Science has played an instrumental role in detecting, predicting and mitigating COVID-19. For instance, organisations like Tableau offer trackers and visualisations that provide intuitive and insightful information about the pandemic. However, the impact of the pandemic indicates that uncertainty would be a significant hallmark of a post-COVID world. Moving forward, data science would consider the ambiguous nature of the environment to play a key role in the coming times. Let’s take a look at how the world has altered hints at the changes to come.

Data variety

Since the beginning of the COVID-19 pandemic, people are practicing social distancing and that the lockdowns in numerous states ensured that everyone stayed in as much as possible. No doubt, this brought about significant shifts in consumer behaviours as we witness consumers gradually adopted digital and online channels to interact with firms. Over-the-top (OTT) and video-on-demand (VOD) markets are witnessing high growth as media consumption is on the rise. Consumers are also increasingly looking to gaming for entertainment.

Businesses have begun to respond by investing in technologies like artificial intelligence (commonly used in customer service chatbots) and virtual reality to engage consumers. Additionally, companies would also look at using IoT devices that would gather real-time data. This including having a remote sensor at home gathering pulse rates or glucose levels for intelligent health care systems or a sensor monitoring water or electricity consumption. These changes would provide greater access to a variety of data formats like location, video, voice and image.

The variety of data being collected pose as a great asset for business to better understand the behaviours and lifestyle of their consumers. As a result, this provides them with the ability to make a consumer-centric business decisions.

Prescriptive modelling

2020 has been a year of uncertainty, and we do expect the post-COVID world to be nothing less. Thus, we can also anticipate a rise in prescriptive modelling in the future. Coupling simulation or logic-based models with machine learning would factor in the uncertainty involved in decision making. Even with the lack of on-demand concrete information, prescriptive models would be able to handle the risks of uncertainty. The lack of clear-cut remedies to COVID would impel healthcare modelling to implement “what-if” scenarios to choose the best course of action.

Data science model management

If data is the new oil, then managing this strategic resource is imperative before conducting analyses. COVID exposed the uncomfortable truth about model drifts where the accuracy of models suffered from changes in time and data. Monitoring models to improve the resilience of AI solutions would, therefore, be a definite focus of the future. Having a well-defined and documented data science process would become mandatory in most organisations.