Picture this: A logistics executive gets an alert about port congestion in Shanghai. Within minutes, not days, their Decision System powered by Decision Intelligence (DI) has already analyzed alternate routes, calculated cost impacts, and suggested optimal responses. That’s not science fiction – it’s the new reality of what is possible with a Decision System. Gone are the days of just business intelligence, sifting through static reports and gut feelings. Today’s market demands split-second decisions backed by data, and that’s exactly what an agile Decision System delivers.

Indeed, Decision Intelligence can transform how organizations make choices. In this article, I’ll break down DI into its essential components. First, I’ll look at how DI leverages the full spectrum of analytics – from basic descriptive analysis to sophisticated prescriptive recommendations. Then, I’ll identify for you the three critical elements that fuel any Decision System: raw data, pre-processed information, and organizational knowledge. Finally, I’ll examine what makes a Decision System truly agile and adaptable. Namely, it is on-demand analytics, AI-driven analytics, and learning-based feedback loops.

1. What is Decision Intelligence (DI)?

Imagine a world where software not only speeds up business decisions but makes them exceptional. That’s what Decision Intelligence (DI) is all about. It’s not just another kind of artificial intelligence (AI) or new tech marketing term. In fact, unlike traditional business intelligence that simply presents data, DI actively combines artificial intelligence, data analytics, and managerial expertise to supercharge decision-making. To better explain, let’s start with a definition for Decision Intelligence.

“Decision intelligence (DI) is a practical discipline that advances decision making by explicitly understanding and engineering how decisions are made and how outcomes are evaluated, managed and improved via feedback.”

Gartner

For more on how DI is different from other IT concepts and disciplines, see my article, This Is What Decision Intelligence Technology Is And Know What Its Not. Now, let’s look at how DI harnesses both traditional and emerging business analytics.

2. Harnessing the Business Analytics Continuum: Descriptive, Diagnostic, Predictive, and Prescriptive Analytics Working in Concert.

Decision Intelligence is powerful because it integrates different types of analytics into a single coherent system. This includes descriptive, diagnostic, predictive, and prescriptive analytics, which work together to turn raw data into actionable insights for corporate decision-makers. Below, I’ll Introduce you to the Business Analytics Continuum and how decision-makers can synergize their analytics capabilities to mutually support analyses across the organization to provide better and timely insights.

a. How Do Different Business Analytics Types Work Within a Decision System.

Gartner’s Analytics Continuum illustrates how different types of analytics support business decision-making. Most importantly, it shows how these analytic types mutually support each other, tied together by a common goal. To detail these types or pillars of the Business Analytics Continuum, see below:

• Descriptive Analytics. Confirms the status quo, identifies trends, and discovers anomalies. Moreover, it can trigger other types of analytics such as diagnostics.
• Diagnostic Analytics. Identifies root causes, determining the “why” behind a trend, or validating a hypothesis. Also, it can trigger further analytics such as predictive or prescriptive.
• Predictive Analytics. Makes forecasts about the future. Additionally, it can trigger other analytics types.
• Prescriptive Analytics. It uses advanced algorithms to recommend a specific course of action, explain why it is the best, and provide details on how to implement it. Works in concert with other types of analytics.

So in this digital age, it is critical that corporate executives have access to a Business Analytics Continuum capability that enables a seamless decision-making process, driven by data and insights. This synergy between analytics and decision-making enables businesses to respond quickly to changing market conditions. Moreover, it enables informed decision-making that drives long-term success. For a more detailed explanation of the Business Analytics Continuum, see my article, Exploit The Business Analytics Continuum For Awesome Data-Driven Decision-Making Results.

b. Decision Intelligence: Analytics Directly Supports All Decision-Makers Across the Organization.  

Decision Intelligence (DI) enabling decision-makers at all levels to work directly with analytical capabilities. With advanced computing power, DI can benefit decision-makers across operational, tactical, and strategic decision horizons. Moreover, DI provides immediate insights for day-to-day decisions. Also, it supports decision-makers to make informed choices about quarterly goals and resource allocation, and aids executives in long-range planning and complex business decisions. Lastly, it has the capability to automate select decision flows.

3. DI Analytics Needs the Right Inputs: Data, Information, and Knowledge.

Building effective Decision Intelligence requires more than just collecting data – it demands the right data from the right sources at the right time. This means tapping into both internal systems (ERP, TMS, WMS) and external feeds (market indicators, weather patterns, social media sentiment). However, raw data alone isn’t enough. Unquestionably, we must transform raw data into contextual-based information. For instance, when analyzing delivery performance, DI systems don’t just look at transit times – they factor in weather conditions, traffic patterns, customer receiving hours, and historical performance data to build a complete picture of what’s really happening on the ground. Below, I describe the key inputs needed by Decision Intelligence.

Inputs for Decision Intelligence (DI)

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a. Raw Data.  

DI needs targeted access to raw data to build the information and knowledge necessary for superior decision-making. This raw data can come directly from observation and unstructured digital sources such as images. But increasingly, it comes from many different systems and input devices such as the Internet of Things (IoT). Also in a real-world operational environment, DI is often constrained from accessing needed data in a timely manner. Thus, DI routinely works with incomplete and ambiguous data. Without a doubt, modern businesses must be able to exchange data that is up-to-date, complete and understood. This is what is called data interoperability.

b. Information.

Here, information comes from the analysis of raw data. As a result, relevant data can be provided in context and structured based on decision requirements. Indeed, it is through analytics that critical information orients the decision-maker on the problem at hand. A key objective of developing this information is to provide decision-makers situational awareness. 

c. Knowledge.

Also, Decision Intelligence relies on organizational knowledge. This knowledge is based on past decisions, policies, and best practices that the organization and decision-makers possess. More and more, organizations are using knowledge tools such as graph tech for digital storage and rapid access. For more on knowledge management and tools, click here.

“Data is not information, Information is not knowledge, Knowledge is not understanding, Understanding is not wisdom.”

Clifford Stoll

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4. Making Decision Systems More Agile: On-Demand Analytics, AI-Powered, and the Feedback Loop.

Without a doubt, traditional business systems, including enterprise systems, are not designed to support high-velocity decision-making. A modern Decision System requires three essential elements. This includes a dynamic feedback loop that refines future insights and recommendations and on-demand analytics for immediate course corrections. Also, what is needed is AI-powered analytics that can process large data sets and implement decision flows autonomously. By incorporating these elements, a Decision System can provide executives with timely, data-driven insights for informed decision-making. See below for a breakout of these agile capabilities for high-velocity, executive-level decision-making.

Elements Needed for a High-Velocity Decision System

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• On-Demand, Real-Time Analytics. First, because of the availability of Internet of Things (IoT) sensors, instant digital communications, AI, and cloud computing, decision-makers can now access insights exactly when they need them. This means no more delayed decisions or missed opportunities – leaders can act quickly with data-backed confidence.
• AI-Driven Analytics. In this case, AI enables analytics that work with massive data sets to uncover more insights. As a result, AI can analyze data in ways that humans can’t, revealing new insights and answering unforeseen questions.
• A Continuous Feedback Loop. Lastly, executives need a high-velocity Decision System that continuously learns and improves based on real-world outcomes, leveraging emerging tech such as Machine Learning (ML). Moreover, this feedback loop capabilities to capture what works and what doesn’t, refining its insights and recommendations to support better decision-making.

For more on making Decision Systems more agile, see my article, High-Velocity Decision Systems for Executives: The Three Ways To Best Exploit AI Tech And Data Analytics.

“The entire effort of artificial intelligence is essentially a fight against computers’ rigidity.”

Douglas Hofstadter

Conclusion.

So, Decision Intelligence has unlimited potential to transform how organizations make choices. In this article, we looked at the essential components of a Decision System powered by Decision Intelligence. First, we examined how DI leverages the full spectrum of analytics – from basic descriptive analysis to sophisticated prescriptive recommendations. Also, there are three critical elements that fuel any Decision System: raw data, pre-processed information, and organizational knowledge. Finally, we examined what makes a Decision System truly agile and adaptable. Namely, it is on-demand analytics, AI-powered analytics, and learning feedback loops. Indeed, today’s market demands split-second decisions backed by data, and that’s exactly what an agile Decision System delivers.

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More References.

• For more on business agility, see my article, Business Agility: The Best Way For Leveraging Digital Tech To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles.
• Also, see Imam Hoque’s article, What is Decision Intelligence?, for a comprehensive discussion on Decision Intelligence from Quantexa.
• See ThroughPut Inc’s article, ThroughPut AI in Review for more on what Decision Intelligence and Analytics Platforms are.
• Article from Chris Walker of Tellius on Decision Intelligence: What It Is and Why It Matters.
• For more on what is needed in a Decision Intelligence Platform, see my article, An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes.

An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes

Lastly, if you are in the supply chain industry and have a need to supercharge your decision-making cycles, please contact me to discuss next steps. I’m Randy McClure, and I’ve spent many years solving data analytics and decision support problems. As a supply chain tech advisor, I’ve implemented hundreds of successful projects across all transportation modes, working with the data of thousands of shippers, carriers, and 3rd party logistics (3PL) providers.  I specialize in launching new analytics-based strategies, proof-of-concepts and operational pilot projects using emerging technologies and methodologies. To reach me, click here to access my contact form or you can find me on LinkedIn.

For more from SC Tech Insights, see the latest articles on Data Analysis and Decision Science.