Picture this: A logistics executive gets an alert about port congestion in Shanghai. Within minutes, not days, their Decision Intelligence (DI) system has already analyzed alternate routes, calculated cost impacts, and suggested optimal responses. That’s not science fiction – it’s the new reality of agile Decision Intelligence. 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 agile Decision Intelligence delivers.
Indeed, Decision Intelligence can transform how organizations make choices, but many struggle to grasp its real potential. 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 DI system: raw data, pre-processed information, and organizational knowledge. Finally, I’ll examine what makes a DI platform truly agile and adaptable. Namely, it is real-time analytics, AI-powered analytics, and learning-based feedback loops.
- 1. What is Decision Intelligence (DI)?
- 2. Harnessing the Business Analytics Continuum: Descriptive, Diagnostic, Predictive, and Prescriptive Analytics Working in Concert.
- 3. DI Analytics Needs the Right Inputs: Data, Information, and Knowledge.
- 4. Making Decision Intelligence More Agile: On-Demand Analytics, AI-Powered, and the Feedback Loop.
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 marking term. In fact, unlike traditional business intelligence that simply presents data, DI actively combines artificial intelligence, Data analytics, and managerial expertise to guide decision-making. To better explain, let’s start with a definition for Decision Intelligence.
“Decision intelligence is a practical domain framing a wide range of decision-making techniques bringing multiple traditional and advanced disciplines together to design, model, align, execute, monitor and tune decision models and processes.”
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.
What makes Decision Intelligence so powerful is how it seamlessly integrates different types of analytics into a single coherent system. Indeed, traditional analytics serves a crucial role in DI: descriptive analytics shows what’s happening now (like current inventory levels), diagnostic analytics explains why it happened (such as identifying demand pattern shifts), predictive analytics forecasts what could happen next (potential stockouts), and prescriptive analytics suggests what actions to take (optimal reorder points). Thus, these components don’t work in isolation – they form an interconnected web that turns raw data into actionable Decision Intelligence.
Now, Gartner’s Analytics Continuum, see below, provides an excellent depiction of how analytics works within a Decision Intelligence framework. Specifically, this chart shows how each type of analytics supports both business decision-making and implementation as well as how they can mutually support each other, tied together by a common goal.

Next, let’s discuss these different types of analytics types and how they work within a Decision Intelligence platform and directly with decision-makers.
“I am not a product of my circumstances. I am a product of my decisions.”
Stephen Covey
a. How Do Different Business Analytics Types Work Within a Decision Intelligence Platform.
Below is a breakout and description of traditional business analytics types and how they are used within a Decision Intelligence platform.
1) Descriptive Analytics – What Happened?
In this day-and-age, descriptive analytics more and more uses digital input in order to scour the business environment to confirm the status quo, identify trends, and discover anomalies. For instance, businesses routinely use Business Intelligence (BI) reports and dashboards for this type of analytics. As a result of a descriptive analysis, a decision-maker may identify items of interest such as a developing trend or anomaly. Subsequently, this type of analysis can trigger other types of analytics such as diagnostics.
2) Diagnostics Analytics – Why Did This Happen?
In many cases, diagnostic analytics is triggered as a result of descriptive analytics such as the identification of something out of tolerance within the business environment. As a result, a decision-maker can initiate a diagnosis of the situation. Also, automation or even AI agents can trigger this type of analytics based on a pre-defined key performance indicator (KPI) or some other type of measure.
Once triggered, a diagnostics analytics focuses on such things as identifying root causes, determining the “why” behind a trend, or validating hypotheses. As a result of a diagnostics, a decision-maker or even automation may trigger further analytics such as predictive or prescriptive. On the other hand, a diagnosis may support a decision not to pursue a particular issue and move on to more pressing matters. For more information, see my article, The Truth About Diagnostic Analytics: A Forgotten Way To Better Business Performance.
“If a tree falls in a forest and no one is around to hear it, does it make a sound?”
George Berkeley
3) Predictive Analytics – What Is Most Likely To Happen?
Predictive analytics does make forecasts about the future, but it is not a crystal ball. Indeed, its purpose is to help executives make better decisions now in order to favorably affect desired outcomes in the future. Again as with other types of analytics, it can be initiated by a decision-maker, standard operating procedures, or even AI agents. What’s more, predictive analytics can be triggered by other analytics types such as descriptive, diagnostics, or even prescriptive analytics. For more information, see my article, Predictive Analytics Types: The Best Opportunities For Supply Chains.
4) Prescriptive Analytics – What Action Should We Take?
Prescriptive Analytics is a lot more than a recommendation engine. To illustrate, most of us are familiar with recommenders that make product buy suggestions on an ecommerce site. On the other hand, Prescriptive Analytics does much more. Specifically, it can use advanced algorithms to recommend a specific course of action, explain why it is the best, and provide details on how to implement it.
Additionally, it is a forward looking analytics like predictive, but instead of forecasting likely outcomes, it helps you choose the best option along with an action plan to implement. Again, prescriptive analytics also works in concert with other types of analytics. For more information, see my article, Prescriptive Analytics in Supply Chains: It Advises What’s the Best Thing to Do, Why, and How to Make It Happen.
b. Decision Intelligence: Analytics Directly Supports All Decision-Makers Across the Organization.
In many ways Decision Intelligence is a new frontier for data analytics. Traditionally, decision-makers have relied on analysts and planning staff for their analytics. Now with advanced, inexpensive computing power, there are more opportunities for decision-makers to work directly with analytical capabilities such as a Decision Intelligence platform. Further, due to recent advances in the tech, Decision Intelligence is not just for senior executives. Indeed, decision-makers at all levels of an organization can benefit from Decision Intelligence.
Also, this era of digitalization has changed how fast businesses must move. Today’s leaders can’t afford to wait days or weeks for analytics teams to process data and make recommendations. This is where Decision Intelligence (DI) shines – it works directly with decision-makers across all three decision horizons: operational, tactical, and strategic. At the operational level, DI provides immediate insights for day-to-day decisions. For tactical planning, it helps department heads make informed choices about quarterly goals and resource allocation. At the strategic level, DI supports executives in long-range planning and complex business decisions. Lastly, there is even the options to automate select decision flows.
For more on supply chain planning and data analytics, see my article, Supply Chain Planning: Data Analytics Advice That Will Result In A Better Way.
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). But 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)
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 data. In fact, access to quality data is a key capability for modern businesses. 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
4. Making Decision Intelligence More Agile: On-Demand Analytics, AI-Powered, and the Feedback Loop.
Agility in Decision Intelligence isn’t just about speed – it’s about adaptability and continuous learning. Ideally, a modern DI system will have a dynamic feedback loop where each decision outcome feeds back into the system, making future insights and recommendations more accurate. Also increasingly, DI requires real-time analytics to process incoming data streams instantly or on-demand. This allows for immediate course corrections when conditions change. Lastly, AI can supercharge analytics that support decision-making. Not only can AI process large data sets and provide advanced analytics, it can act as its own autonomous agent to even implement decision flows and tasks. See below for details on real-time analytics, AI-powered analytics and feedback loops that empower agility within Decision Intelligence.
“The entire effort of artificial intelligence is essentially a fight against computers’ rigidity.”
Douglas Hofstadter
a. Real-Time, On-Demand Analytics: Enabling Executives to Take Control of the Timing of their Decision Cycle.
First, real-time, on-demand analytics is a critical capability of Decision Intelligence. Further, this type of analytics can use a combination of more traditional analytics. The key need for this type of analytics is that it provides immediate results based on the decision-maker’s operational tempo and decision requirements. Below is a more formal definition.
“Real-time analytics is the discipline that applies logic and mathematics to data to provide insights for making better decisions quickly. For some use cases, real time simply means the analytics is completed within a few seconds or minutes after the arrival of new data. On-demand real-time analytics waits for users or systems to request a query and then delivers the analytic results. Continuous real-time analytics is more proactive and alerts users or triggers responses as events happen.”
Gartner
Indeed, real-time analytics is a necessity for agile decision-making. In the past, executives had to wait for monthly reports or lengthy analysis from support teams before making decisions. Not anymore. Thanks to IoT sensors, instant digital communications, 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.
b. AI-Powered Analytics – Data Analysis Supercharged and Agent-Based.
Now with recent advances in Artificial Intelligence (AI), it can supercharge both traditional data analytics and knowledge-based tools. Specifically, AI enables data analytics to work with massive data sets. What’s more, AI and knowledge-based technologies can now work with a wide range of diverse types of structured data (like shipping records and inventory counts) and unstructured data (like emails and pdf documents). Also, agent-based AI can increasingly act autonomously on data analytics tasks. For more information, see my article, How Data And AI Work Together To Better Empower Analytics.
“In a sense, artificial intelligence will be the ultimate tool because it will help us build all possible tools.”
K. Eric Drexler
c. The Continuous Feedback loop – Enabling an Agile DI that Learns and Adapts Based on Real-World Outcomes.
Also, Decision Intelligence isn’t a “one-and-done” solution like traditional business intelligence reports or decision briefs. Instead, think of it as a living, agile system that is continuously learning and improving. As decisions play out in the real world, DI captures what worked and what didn’t. Then it uses this feedback to get smarter over time. In fact, now DI can use AI like Machine Learning (ML) to better learn and adapt.
In particular, a Decision Intelligence platform needs to know how effective its insights, projected outcomes, recommendations, and action plans were. The platform needs to know exactly where there are gaps between its analysis and actual outcomes. For more information on AI feedback loops, see IrisAgent’s article, The Power of AI Feedback Loop: Learning From Mistakes.
“I think it’s very important to have a feedback loop, where you’re constantly thinking about what you’ve done and how you could be doing it better.”
Elon Musk
Conclusion.
So, Decision Intelligence has unlimited potential to transform how organizations make choices. In this article, we looked at the essential components of DI. 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 DI system: raw data, pre-processed information, and organizational knowledge. Finally, we examined what makes a DI platform truly agile and adaptable. Namely, it is real-time analytics, AI-powered analytics, and learning feedback loops. Indeed, today’s market demands split-second decisions backed by data, and that’s exactly what agile Decision Intelligence delivers.
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.
For more from SC Tech Insights, see the latest articles on Data Analysis and Decision Science.
Greetings! As an independent supply chain tech advisor with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to logistics leaders. My focus is to drive transformation within the logistics industry by leveraging emerging LogTech, applying data-centric solutions, and increasing interoperability within supply chains. I have a wide range of experience to include successfully leading the development of 100s of innovative software solutions across supply chains and delivering business intelligence (BI) solutions to 1,000s of shippers. Click here for more info.