The Role of Artificial Intelligence in Data-Driven Decision Making
Jinho Kim PhD.
Representative Swiss school of Management, Liaison Office in Seoul / www.ssmseoul.kr
The adage “Knowledge is power” was first articulated by the 16th-century English philosopher Francis Bacon. Despite being a statement from long ago, it still resonates with us today—likely because it holds true. This is precisely why big data is so significant; it represents power through knowledge. Big data is a treasure trove of rich information about customers and markets. Utilizing it effectively in decision-making can provide a tremendous advantage or “power” (competitive edge). In this context, if there is a phrase that encapsulates the era of big data, it is data-driven decision-making. What does making decisions based on data mean? How data is utilized in decision-making can be categorized as shown in the following diagram.
The first approach is data denial, where one distrusts data and therefore does not use it. The second is indifference towards data, not even taking an interest in it, much less utilizing it. The third is data cherry-picking, where one only uses data that supports their preconceived decisions. Finally, data-driven decision-making involves proactively using data as the basis for all decisions.
What do Google, Amazon, and Netflix have in common? These companies are building unparalleled global competitiveness based on data analytics. Google is famous for its motto, “We never make decisions without data, especially decisions related to services and products.” Amazon’s CEO Jeff Bezos has proclaimed, “Whether a company struggles in uncharted waters or becomes globally successful depends on its use of data and information. The future masters of this world are companies excellent in analytics; companies that not only know how things are related but also understand why and how they are related.”
Netflix CEO Reed Hastings states, “We are relying on data more than ever, making rational, quantitative, data-dependent decisions across a much broader scope than before. Businesses, governments, and societies are transitioning from faith-based approaches to data-driven decision-making. Is your organization prepared for this fundamental shift?”
So what is data-driven decision-making, and what role does artificial intelligence play in it? Generally speaking, decision-making involves recognizing a problem, exploring alternatives to solve it, evaluating these alternatives, and choosing the best one. When decision-makers evaluate multiple alternatives, they consider both quantitative and qualitative information. Qualitative information takes into account factors influenced by cultural, social, legal, and political backgrounds. Quantitative information refers to insights extracted from the analysis of data related to the problem.
Which type of information is more important can vary depending on the situation of the decision. However, for transparent and rational decision-making, quantitative information tends to be more critical. Especially when the future is uncertain and the decision may have far-reaching effects, it is essential to extract insights from the analysis of actual data and utilize them effectively in the decision-making process.
If quantitative information is necessary for wise decision-making, what specific types of quantitative information are required? The information should answer six fundamental questions related to the business. These six questions can be addressed at various levels, such as the entire company, focusing on metrics like revenue and net profit, or at the functional or departmental levels within the company. Depending on the situation for the decision-making, the focus could also be on very localized areas.
Companies that rely on experience or intuition, rather than data analysis, primarily use basic reporting to understand what has happened in the past. They guess what is currently happening and what will happen in the future based on their instincts or experience. Because they do not employ sophisticated analytical techniques, they are unable to answer questions about why and how something happened, or what would be the best way to respond. Therefore, the likelihood of making wise decisions is significantly reduced when relying on this approach.
How can we make wise decisions? According to Buddhism’s Eightfold Path, wisdom or ‘prajna’ entails right view and right intention. This means properly recognizing the problem (right view) and being able to accurately assess why and how it occurs (right intention) are essential for wise decision-making. Companies that compete through analysis go far beyond simple reporting levels; they extract deep wisdom or insights for wise decision-making through in-depth data analysis.
The most important thing here is to understand the patterns of why and how certain events occur based on past data. For this, the core technologies of artificial intelligence, such as statistics or machine learning models, are utilized. The optimal models discovered in this process are used to understand what is currently happening, namely, to detect anomalies in real-time and respond immediately. They also use the optimal models to pre-emptively predict future situations and take necessary actions (like personalized recommendations or optimizations) to guide towards the most favorable outcomes.
In summary, if you can use artificial intelligence to find patterns in the data (i.e., why did something happen?), you can use these models for real-time anomaly detection and prediction. Moreover, you can make prescriptive decisions to induce the most desirable outcomes. As the level of data analysis utilization increases, a company’s competitive edge is heightened.
