The Key to Businesses’ Post-COVID-19 Stability and Strategic Growth: Data Science


The Key to Businesses’ Post-COVID-19 Stability and Strategic Growth: Data Science


COVID-19 has disrupted fundamental business practices. With restrictions easing, and a return to a normality coming sooner rather than later, businesses are entering a stabilisation phase, before trying to get back to their pre-COVID-19 standards and working towards their strategic growth. Data science should be an essential tool for each phase of a business’s response to COVID-19.

Data science, particularly AI, has a variety of uses to contribute to a business’s stability, enabling them to cope with new procedures and standards because of the pandemic:
            1) AI algorithms can be programmed to optimise shift schedules. Employees’ data – for example, where employees live, how many people they live with, pre-existing health conditions – can be inputted and the algorithm can assign appropriate shifts to employees. Ensuring that the employees are in the right place, at the right time, for the right amount of time.
            2) Computer vision programs can detect when an employee or customer is not wearing a mask or breaching social distancing rules, alerting employees or management via wearable devices or alerts. Companies are doing this already, having developed AI-enabled tools which analyses real-time video streams to help maintain safety protocols.
            3) Businesses are moving their employees to remote workspaces. In such cases, AI can help businesses maintain infosecurity standards. Cybersecurity solutions, powered by AI, mean IT security mandates can be met, as teams are able to proactively monitor the network traffic across VPNs and networks, and help them identify potential points of infringement and breaches in real-time.

Once stabilised, and a level of pre-COVID-19 normalcy is achieved, businesses will look ahead towards strategic growth. Leveraging data science further, after its use in helping create stability, is a logical and necessary step.

Before the pandemic, businesses were using data science mostly for ambitious challenges. However, as the pandemic hit, data science began to be used for vital, everyday operations. AI enabled more accurate online targeting to attract potential clients, to effectively manage inventory, and, also, solve how best to meet changing consumer demands and optimise supply chain management to minimise disruption, which Proctor and Gamble have been utilising.

Going forward, organisations must scale their data science use cases and put them into action in other areas. For example, a company using AI chatbots for internal HR queries could adapt the solution to be external-facing and answer customer questions. Algorithms should be optimised and automated to augment the work of data scientists without the need to conduct manual training. This allows data scientists to reprioritise – enabling them to build and research new, innovative solutions needed to grow the business. If more expertise is required for undertakings, businesses should focus on upskilling existing employees who know the domains, functions, and workflows. This is more likely to generate business value and fuel growth, as IBM predicts that demand for data scientists will soar by 28% in 2020 and there is already a shortage of quality. Ongoing evaluation of the data science enterprises are paramount to maintain high standards of practice.

Globally, data science is playing an important role in contact tracing. Not just to prevent the spread of the virus, this data will be used in forecasts and models to, for instance, help policymakers make decisions and enable hospitals and businesses to better plan and anticipate potential future disruptions.

Companies who don’t want to get left behind in the wake of COVID-19 recovery should not neglect the potential of data science to drive business goals and augment their teams to untapped and unforeseen levels.

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