If you’ve ever sat in a boardroom waiting for a “urgent” report that’s already three days late and two days irrelevant, you know the frustration of traditional decision-making. We’ve been taught to rely on a mix of gut instinct and slow-motion analysis, but in a world moving at the speed of an algorithm, “slow and steady” just gets you left behind.The reality is that our intuition is a powerful tool, but it’s being starved of the rapid insights it needs to actually win. What is achievable and what we desperately need are Decision Systems that excel in this high-velocity world.
In this article, I’ll share with you the three high-velocity elements needed within a Decision System. Indeed, it is time for decision-makers to truly exploit AI and advanced analytics. A high-velocity Decision System isn’t about more dashboards; it’s about architecting a system that delivers the right insight at the exact moment of decision—on your schedule, not your IT department’s. To detail, I’ll break down these three essential pillars: On-Demand Analytics, AI-Powered Intelligence, and the Continuous Feedback Loops that enables rapid-decision making and turns every choice into a self-improving asset. If you’re ready to stop waiting for the “perfect” report and start making faster, smarter moves, I invite you to see how these components fit together below.
1. Decision Systems with On-Demand, Real-Time Analytics for Rapid Executive Insights.
First, real-time, on-demand analytics is a critical capability of a high-velocity Decision System. At the same time, this type of analytics is used in conjunction with more traditional analytics. The key is that On-Demand Analytics uses a “Data-Ready” framework that is prepared to provide immediate insights. Whereas, traditionally decision-makers have had to wait for their staff to prepare an analytical report, or executives suspended making a decision till the next periodic meeting. Hence, Real-Time, On-Demand Analytics totally caters to the decision-maker’s operational tempo and their immediate 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 … 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, On-Demand Analytics is a necessity for today’s 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, AI, instant digital communications, and cloud-powered computing, organizations can be “Data-Ready”, providing decision-makers Real-Time, On-Demand Analytics exactly when they need them. This means no more delayed decisions or missed opportunities – leaders can act quickly with data-backed confidence.
For more information on setting up a Data-Ready framework for On-Demand Analytics, see my article, Be Data Ready: It’s About Relevant Information — Targeted and Timely — for the Best Business Decisions.
“… Thanks to IoT sensors, AI, instant digital communications, and cloud-powered computing, organizations can be “Data-Ready”, providing decision-makers Real-Time, On-Demand Analytics exactly when they need them. ”
Douglas Hofstadter
2. AI-Powered Analytics – Data Analysis Supercharged and Agent-Based.
Without a doubt, recent AI advancements are accelerating the effectiveness of traditional data analytics and knowledge-based tools. Specifically, AI-Powered Analytics can uncover more insights through its extraordinary computing capabilities accessing massive data sets. As a result, AI can analyze data in ways that humans can’t, revealing new insights and answering unforeseen questions. What’s more, AI and knowledge-based technologies can now work rapidly with a wide range of diverse types of structured data (like shipping records and inventory counts) and unstructured data (like emails, images, 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.
“AI-Powered Analytics … through its extraordinary computing capabilities accessing massive data sets … revealing new insights and answering unforeseen questions.”
K. Eric Drexler
3. The Continuous Feedback loop – Enabling an Agile Decision System that Learns and Adapts Based on Real-World Outcomes.
Lastly, a high-velocity Decision System is not a static report; it is an agile framework that evolves alongside the business. By capturing the real-world results of every choice, both the decision-maker and a Decision System use a continuous feedback loop to close the gap between projected insights and actual outcomes. Today, Machine Learning can accelerate this feedback process, allowing the platform to diagnose exactly where its recommendations deviated from reality. As a result, this creates a self-improving cycle, ensuring that an organization’s decision-making becomes smarter, faster, and more adaptive over time. For more information on AI feedback loops, see IrisAgent’s article, The Power of AI Feedback Loop: Learning From Mistakes.
Also, to illustrate how continuous feedback loops can improve decision-making, just look at the decision cycle framework of Col. John Boyd, the OODA (Observe, Orient, Decide, Action) Loop. This decision framework is an iterative cycle that repeats itself over and over again. Inherent within this decision-making cycle is a feedback loop where both decision-makers and the organization learn. Consequently, this improves both their decision-making and their actions in the future. For more on the OODA Loop framework, see my article, OODA – Enabling Business Agility: The Best Way To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles.
“By capturing the real-world results of every choice, both the decision-maker and a Decision System use a continuous feedback loop to close the gap between projected insights and actual outcomes.”
More References.
- Data Analytics: A Data Analytics Perspective To Better Empower Supply Chain Managers
- Business Agility: Business Agility: The Best Way For Leveraging Digital Tech To Disrupt Competitors, Seize Opportunities, And Overcome Obstacles
- The Business Analytics Continuum: Exploit The Business Analytics Continuum For Awesome Data-Driven Decision-Making Results
- Decision Platforms: 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 Decision Science, AI, and Data Analytics.
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.
Greetings! As a supply chain tech advisor with 30+ years of hands-on experience, I take great pleasure in providing actionable insights and solutions to industry leaders. My focus is on supply chains leveraging emerging LogTech. I zero in on tech opportunities and those critical issues that are solvable, but not well addressed, offering industry executives clear paths to resolution. 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.