The pandemic delivered an immediate and decisive blow to the global economy. At first, businesses were scrambling to keep afloat and survive. Now, many companies have entered the stabilization stage, where objectives center on getting close to pre-COVID-19 operations. Once stabilization is achieved, organizations will once again set their sights toward strategic growth.
A strong enabler that’s getting the business community through the crisis is data science. Businesses are increasingly waking up to the value of data science when applied to core business functions in innovative ways. In fact, AI augmentation is expected to create $2.9 trillion in business value by 2021, and 6.2 billion hours of worker productivity globally.
Let’s take a look at how companies leveraged data science when the crisis hit, the ways in which it is now driving them through the stabilization phase, and how they can use it to power strategic growth in the future.
When the Crisis Hit
Prior to the crisis, many businesses saw data science as something that was “nice to have” or an investment that was used primarily to solve aspirational future business problems. However, when the pandemic hit, they realized that data science would become necessary for their survival. No longer were they using it just for ambitious challenges, but it became vital in driving operational efficiencies in areas such as supply chain and inventory management.
For example, many retailers were starting to use AI to attract potential customers through accurate online targeting. After the crisis hit, these same companies found they could leverage AI to maximize operational efficiencies. AI-powered solutions use data to provide insights to companies on how best to meet changing consumer demands in different areas and optimize supply chain management to minimize disruption. For example, Procter and Gamble, the largest toilet paper manufacturer in the U.S., used supply chain modeling software to quickly respond to changing consumer demands and alter delivery models.
In addition, data science played - and is still playing - a key role in contact tracing to prevent the spread of the virus. Analyses conducted with modeling techniques like SEIR have assisted policymakers in getting a better understanding on how the virus spreads, allowing them to act accordingly. Furthermore, data science models like those being used by the Centers for Disease Control and Prevention (CDC) are providing the forecasts necessary for hospitals to be able to plan according to expected capacity needs.
Data Science Powers Stabilization
The initial survival stage is largely over for the majority of businesses, and they have entered the stabilization phase. Here, companies are finding new applications for data science in the current stage of COVID-19 economic recovery.
One area where AI can play a key role in a return to “normality” is helping businesses welcome their employees back to the workplace. AI algorithms can be used to create optimal schedules on who should go into the workplace, when, and for how long. These algorithms are fed with data on things like where team members live, pre-existing health conditions, and whose presence is required at which time. With all of these data points, the solution produces a schedule for the most optimal times for certain employees to be physically present.
Another use of AI here is computer vision programs, which can detect when someone is not wearing a mask or breaching social distancing rules and warn employees via wearable devices or flag these instances with the relevant person. One example of a company doing this is LandingAI, which has developed an AI-enabled tool that analyzes real-time video streams and accurately measures the distance between people and detects protocol breaches.
AI is also helping businesses maintain infosecurity standards while employees are at home. Cybersecurity solutions that are powered by AI allow teams to proactively monitor the network traffic across VPNs and networks and help them identify potential points of infringement and breaches in real-time. These solutions enable businesses to comply with IT security mandates even when employees are working remotely.
How Data Science Can Enable Strategic Growth
Like every recession, this one will eventually pass, and economies will recover. Once business operations and customer buying power have stabilized to pre-pandemic levels, companies can start to look ahead towards the stage of strategic growth. There are a number of areas where organizations can leverage data science to drive growth in this period.
Prior to the pandemic, many companies using data science to expand their product offerings would focus on pilots and falter in moving data science projects forward to production. Going forward, organizations 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 scale the solution to be external-facing and answer customer questions.
Another way in which businesses must leverage data science if they want to scale effectively is by ensuring that learnability is built into its solutions. Algorithms must be optimized and automated so they can augment the work of data scientists without the need to conduct manual training. Algorithms embedded with active learning and reinforcement learning capabilities will be key in speeding up processes and amplifying product efficiencies.
With manual processes automated, data scientists can dedicate their time to building new, innovative solutions that they need to grow the business. In the strategic growth phase, companies shouldn't wait for ready-made solutions to come to market. In order to stay ahead of competitors, data science teams should address the complexity and create the solutions themselves, according to their own business needs.
If more expertise is required in these cases, businesses should focus on upskilling their existing teams rather than simply adding in more data scientists that do not yet understand the business’ domains, functions, and workflows. Not to mention, there is already a shortage of quality data scientists in the labor market and IBM predicts that demand for data scientists will soar by 28% in 2020. Investing in upskilling existing employees is more likely to generate business value and fuel growth.
Finally, companies using data science to accelerate growth must make sure that they find ways to evaluate AI system performance. Never before have AI investments been held to such scrutiny in terms of the business value they deliver. Businesses must prove that they are holding AI solutions accountable according to their decision-making power and the amount of the budget allocated to them. Organizations that create a formalized evaluation system that demonstrate the performance and ROI of data science investments will be the ones that can scale most effectively.
Before the pandemic, data science use cases within businesses were expanding rapidly. Now, they have become non-negotiable for many organizations that are focused on maintaining operational efficiency and setting their sights on future growth. Companies that 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 unforeseen levels.
About the Author:
Sundeep Reddy Mallu is the Head of Analytics and Hiring at Gramener, which solves business problems for its clients by identifying data insights and presenting them as data stories. Sundeep advises executives at leading enterprises and NGO's on data science. He helps transform the organization through an advisory in building teams and adopting a culture of data. As TA head he drives the hunt for great data science team players, across industries and campuses, and attracts them to join Team Gramener.
