How Businesses can Successfully Implement Automation and Data-Driven Decision-Making Tools

Georgetown launches AI, Analytics, and the Future of Work Initiative

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Automation and data-driven decision-making are often associated with tech giants like Amazon and Google. The success of these corporations often rests on the efficiency of their operations. Over the years, these companies have perfected their decision-making so that all the tasks that are too mundane to be performed by a human are automated. Every time a new product or process is put in place, it is tested thoroughly to ensure it improves the firm’s overall efficiency.

Small businesses have historically been more reluctant to adopt data-driven decision-making, not realizing that this choice can be very costly when it comes to the profitability of their venture. While it is difficult to fully explain businesses’ hesitancy to adopt new forms of automation, there are at least three underlying motives.

First, business leaders are often praised for their instincts, gut feelings, and creative thinking. Second, business schools have not traditionally trained their students according to the scientific method, whereby new knowledge is created through experimentation. Third, there is a widespread belief that automation and data-driven decision-making requires an army of programmers and data scientists to be implemented and is prohibitively expensive for small corporations.

To address the second point, Georgetown University’s McDonough School of Business, recently established an AI, Analytics, and Future of Work Initiative to address how the rapid pace of technological change is driving both economic and social transformation in the workforce. We are committed to preparing students for the emerging complexities related to the future of work, as well as collaborating with business leaders and policy makers to devise solutions that both benefit their business and the common good.  

As part of our work, we aim to help debunk the myth that automaton and data-driven decision-making is out of reach for most organizations. Data-driven decision-making is the key to successful companies because it leads to better decisions and allows for a greater degree of automation within the firm.

What is data-driven decision-making?

Data-driven decision-making is how firms collect data and use it to validate their decision-making process. It often involves creating multiple versions of the product launched and engaging in randomized control trials to determine its final version. Even post-launch, data-driven decision-making entails monitoring how the customer population and the competition evolve to constantly update the product sold with novel features.

What is automation?

Automation is the process through which some of the tasks that humans historically performed are now delegated to machines. While it is common to think about warehouse robots substituting factory workers, other forms of automation are bots that replace call center workers or apps that interact with clients without requiring the interaction of customer representatives. Automation tends to be successful when the machine learning algorithms it is based on are fed very detailed data. The algorithms underlying automation are constantly updated to reflect changes in the behavior of the company customers.

Why are data-driven decision-making and automation closely intertwined?

Because they require the same inputs, they need detailed data regarding customers’ and competitors’ behavior. They are both based on the scientific method: the idea that through data collection and experimentation, better product specifications and more efficient processes can be implemented within the firm.

What are the biggest barriers to adopting automation and data-driven decision-making for small businesses?

In some instances, the most significant barrier is philosophical. Many business leaders think that the majority of the customers have similar preferences to theirs. So the product specification they come up with is bound to be the most promising one. In other cases, the problem rests in an inadequate infrastructure to collect and process data regarding customer choices and purchasing decisions.

A common misconception is that the actual experimentation and automation implementation are expensive. Still, more and more companies are now providing such services at affordable costs for small businesses. For example, there are organizations that provides clients with integrated solutions to automate tasks like invoice processing. Others allows its customers to conduct randomized control trials to test which versions of their app or website maximize customer satisfaction and engagement. Finally, there are companies that offer human resources automation services, ranging from employee onboarding to performance management.

To maintain competitiveness, businesses are bound to improve the efficiency of their operations and product offerings constantly. Automation and data-driven decision-making are potential vital tools to help small businesses compete with larger and more sophisticated corporations. So far, small companies are shying away from wholeheartedly adopting these tools. I recommend they embrace them and join the analytics revolution.

To learn more about the work of the AI, Analytics, and the Future of Work initiative, visit our website. We engage with organizations that are embracing analytics and data-driven decision-making, as well as to help businesses optimize their operations and embrace the future of work through research, student consulting engagements, or professional events.

Alberto Rossi is director of the AI, Analytics, and Future of Work Initiative and the Provost’s Distinguished Associate Professor of Finance at Georgetown University’s McDonough School of Business.