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 on-demand analytics, AI-powered 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 guide 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 Intelligence Platform.

Gartner’s Analytics Continuum illustrates how different types of analytics support business decision-making, and 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. Can trigger other types of analytics such as diagnostics. 
• Diagnostic Analytics. Identifies root causes, determining the “why” behind a trend, or validating a hypothesis. Can trigger further analytics such as predictive or prescriptive.
• Predictive Analytics. Makes forecasts about the future. 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 and make informed decisions that drive 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.  

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). 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 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

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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 on-demand 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 on-demand 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. On-Demand, Real-Time 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.

“[On-Demand], 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.

How Data And AI Work Together To Better Empower Analytics

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 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 agile Decision Intelligence 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.