Written by Audrey Way, Data Visualization Specialist, Illumination Works 

Despite all the data at our fingertips, businesses still struggle to turn insights into action. The problem isn’t the numbers—it’s how our brains interpret them, and without integrating decision science and psychology, even the best analytics can lead to poor decisions.

Designing BI/BA for the Way Humans Think

The Key to Effective Analytics. Business intelligence (BI) and business analytics (BA) promise to transform raw data into actionable insights. Yet, despite cutting-edge dashboards and AI-powered analytics, many organizations still struggle to translate data into better decisions. Why? Because data alone isn’t enough. In fact, 41% of business leaders cite a lack of understanding due to data complexity or inaccessibility (1).

Article Highlights

• Intelligent Analytics
• Setting the Foundation
• Factoring in the Human Element
• Data & Human Judgement
• Anatomy of a Decision
• Bridging Data & Decision Science
  • Pattern Recognition
  • Rational
  • Bounded Rationality
• Contextualizing Role of BI/BA
• Emerging Trends

BI and BA are increasingly converging as businesses demand more intelligent, real-time, and forward-looking insights. While BI focuses on monitoring, reporting, and diagnosing, BA expands into predicting and optimizing outcomes. Together, they create a powerful ecosystem for data-driven business strategies.

However, answering these critical questions also requires robust data science and modeling techniques operating behind the scenes. These underlying methodologies form the foundation for BI/BA tools, which then translate complex computations into accessible visualizations for reports and dashboards.

Traditional BI/BA tools focus on delivering data, but without considering how users interpret and act on it, insights can be misused or ignored. By leveraging cognitive science, we can design analytics that align with human decision-making patterns, reduce bias, and drive smarter business outcomes. Effective BI/BA isn’t just about reports and dashboards; it must also account for how humans process information and make decisions.

“Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI solely about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights by streamlining the effort needed to search for, merge, and query the data necessary to make sound business decisions.” (3)

To build truly effective BI/BA solutions, we must integrate principles from decision science and human psychology.

The Hidden Forces Driving Our Choices

Where Data Meets Human Judgement. Decision making isn’t simple any more. There is consensus that decision making is more complex than it has ever been, which makes it all the more important to consider how humans make decisions when presenting BI/BA solutions (4).

In the article Bringing Advanced Technology to Strategic Decision-Making: The Decision Intelligence/Data Science (DI/DS) Integration framework, Pratt et al. consider the many factors that come into play when making a decision, some conscious and some unconscious (4). There are internal and external influencers, assumptions, choices/actions, intermediaries, goals/outcomes, and dependencies and all of these can change to a certain degree. Furthermore, there is the decision-making thought process versus the implementation process, which may lead to disconnects between the predicted decision or outcomes and the actual decision, forcing the decision maker to pivot mid process.

Choices are the thoughts that become actions once the choices are executed. Intermediaries are the consequences of an action, while dependencies are how one factor influences another. All these decision parts lead to goals or outcomes.

Goals are conditions of an outcome, while outcomes are the accumulation of actions associated with a decision. Outcomes can be measured, while goals produce a true/false result. (5)

Ultimately, analyzing the anatomy of a decision helps BI/BA developers break down the factors that shape business choices, leading to more insightful analysis and impactful reporting. By distinguishing between decision influencers, a BI/BA can better contextualize decision making environments, identifying which factors can be controlled and which must be accounted for as assumptions. Understanding how choices, intermediaries, and dependencies interact ensures that reports go beyond surface-level trends and observations, revealing deeper patterns and relationships that drive business outcomes.

Decision-Making Models Supported by BI/BA

Bridging Data and Decision Science. Decision making can be approached in different ways, with three primary models supported by BI/BA—pattern recognition, rational, and bounded rationality—each offering a unique perspective on how choices are made in various contexts. In the article titled The Psychology of Decision-Making: How We Make Choices, Psychreg helps us define and understand these models. (5)

BI/BA tools play a critical role in supporting the pattern recognition decision-making model by analyzing large volumes of data to identify recurring trends, allowing decision makers to make quicker, more effective decisions. These tools help highlight meaningful insights that might otherwise go unnoticed, accelerating the decision-making process.

For example, BI/BA can enhance fraud detection in financial services. By analyzing historical transaction data, BI systems can identify patterns of behavior that are consistent with fraudulent activity. When these patterns are recognized, the system flags suspicious transactions for further review, enabling financial institutions to act quickly and prevent losses.

For example, BI/BA can support inventory management in retail. By analyzing sales data, market trends, and historical demand, BA systems can predict future inventory needs, allowing businesses to make data-driven decisions on stock levels. This helps reduce over or understocking, optimizing supply chain efficiency and minimizing operational costs.

These tools help decision makers navigate the complexities of uncertainty and incomplete information, offering insights and transparency where biases or gaps might exist. While BI/BA cannot completely eliminate the constraints of human cognition, it aids in mitigating those limitations by presenting data clearly and acknowledging the uncertainties inherent in decision-making processes.

For example, in financial forecasting, BI/BA can provide confidence intervals around projected revenue, showing decision makers the range of possible outcomes based on historical data and trends. This transparency helps decision makers understand the potential risks involved and make more informed decisions, even when perfect information is unavailable.

Regardless, there is no denying that decisions now are complex. Therefore, a key role of a BI/BA developer is to simplify the decision-making process by clearly outlining the different data and key performance indicators (KPIs) that will drive decisions. Contextualizing and translating raw data into meaningful narratives are arguably some of the most important roles of BI and BA.

In the evolving landscape of analytics, Decision Intelligence represents the next step, one that blends technology, data science, and human psychology to empower us beyond traditional decision-making, fostering a more holistic and dynamic approach.

Special thanks to the contributors and technical reviewers of this article:

• Brandon Hurt, Senior Data Visualization Specialist
• Kristen Workman, Senior Software Developer
• Janette Steets, PhD, Director of Data Science
• Cathy Claude, Director of Marketing

If you liked this article, you might also like:

• Maximizing the full potential of AI with performance and scalability Read article
• Revolutionizing the AI landscape with retrieval-augmented generation (RAG), knowledge graphs, and quantized large language models (LLM) Read article
• Navigating the maze of AI cyber risks Read article

Sources referenced in this article:

1. 73% of Business Leaders Believe Data Reduces Uncertainty and Drives Better Decisions — So Why Aren’t They Using It? – Salesforce
2. What Is Business Intelligence (BI)? | IBM
3. What is business intelligence? Turning data into business insights | CIO
4. Pratt, L., Bisson, C., & Warin, T. (2023a). Bringing advanced technology to strategic decision-making: The Decision Intelligence/Data Science (DI/DS) integration framework. Futures, 152, 103217. https://doi.org/10.1016/j.futures.2023.103217
5. The Psychology of Decision-Making: How We Make Choices | Psychreg

Careers