I’ve seen firsthand how businesses can struggle with decision-making, and I’m convinced that Decision Intelligence (DI) is the key to unlocking better outcomes. DI is more than just a buzzword – it’s a game-changer that can transform the way organizations make decisions. In this article, I’ll cut through the noise and clarify what DI is, what it’s not. Most importantly, I’ll share with you how DI can be applied to data-intensive industries like supply chains to drive dramatic improvements in decision-making and automation. Lastly, I’ll also help you understand how DI differs from other analytical domains such as decision science, expert systems, data science, and business intelligence (BI). By fully understanding Decision Intelligence, you can harness its full potential.
Decision Intelligence (DI) Defined And Its Application To Supply Chains.
Decision Intelligence (DI) is a relatively new field that has gained significant attention in recent years. Its modern formulation began in 2008 with Quantellia’s DI solutions, and it gained popularity in 2019 with Lorien Pratt’s book, How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World. Also during this same period, Google’s launched a Decision Intelligence department. Subsequently, Gartner analysts further recognized DI as a key tech trend for 2022. To better understand DI, let’s look at some Decision Intelligence definitions and how it applies to supply chains.
1. Decision Intelligence Definitions.
To better understand DI, let’s look at some Decision Intelligence definitions, including a general definition and one specifically tailored to supply chains.
“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
“ [Analytics and Decision Intelligence (A&DI)]... market spans capabilities that provide different types of analytics, focusing on predictive and prescriptive ones. Many of these offerings have been enhanced with AI and Data Science & Machine Learning (DSML) capabilities to support supply chain decision making. These capabilities could either be part of a broader supply chain application/suite or a separate encompassing A&DI platform. Such a platform consists of existing and emerging technologies, including: Graph technology, Advanced analytics, AI, DSML, Model development & Digital supply chain twin (DSCT).”
Gartner
2. Beyond Marketing Hype: Decision Intelligence as the Convergence of Analytics and Automation.
Decision Intelligence (DI) is more than just an IT marketing term. In fact, it is a very useful capability that refocuses analytical tools to directly support decision-making. This includes business intelligence, data science, and decision science. Also, DI includes automation tools and are best deployed as a software decision platform. Moreover, DI is similar to Business Process Automation (BPA) in that it can operate in three modes: support, augmentation, and automation. Thus, Decision Intelligence is a very versatile capability in improving decision-making across the organization. At the same time, DI is not BPA because it supports decision-making directly versus providing task-based automation.
For a more detailed discussion on the nature and evolution of business automation, see my article, Business Automation AI Remake: First Just Tech To Empower Processes And Now Operate Autonomously.
3. Decision Intelligence Directly Supports The Flow Of Information Within the Decision-Making Cycle.
Full-fledged Decision Systems powered by Decision Intelligence (DI) are just starting to emerge. Further, they are unique, capable of supporting the entire decision cycle within a corporate environment. Specifically, these systems can sense, analyze, make recommendations, and directly support rapid decision-making and implementation. They can also explain and justify their recommendations to business users without necessarily requiring IT departments, analysts, or data scientists. For instance, a Decision System can analyze sales data, market trends, and customer behavior to help decision-makers to optimize pricing strategies and improve profitability. Moreover, these systems learn from previous decisions to enhance decision effectiveness.
For more on Decision Systems powered by DI, see my article, An Agile Decision Platform to Empower Executives For Superior Supply Chain Performance: Here Are The Best Attributes.
Decision Intelligence vs. Other Analytical Disciplines
In the following paragraphs I’ll explain what decision intelligence is not and how it compares to other analytical disciplines such as decision science, expert AI, data science, and business intelligence.
1. Decision Science Vs. Decision Intelligence.
Indeed, decision-makers have always used decision science to guide their choices. While early decision science was based in math and statistics, the advent of computers has enhanced its application in fields like Decision Intelligence and decision support analytics. Below are two definitions that are relevant to both Decision Science and Decision Intelligence.
Decision Science Definition
” the study of how to make decisions based on the information you have and on judging what risks, changes, etc. are likely in the future.”
Cambridge Dictionary
Decision Definition (vs a calculation)
“Not all outputs/suggestions are decisions. In decision analysis terminology, a decision is only made once an irrevocable allocation of resources takes place. As long as you can change your mind for free, no decision has been made yet.”
Cassie Kozyrkov
So, Decision Science and Decision Intelligence are related but distinct concepts. First, decision science focuses on understanding the decision-making process and applying statistical models to inform choices, relying heavily on human expertise and statistical analysis. In contrast, Decision Intelligence takes a more advanced approach by leveraging machine learning algorithms, advanced analytics, and automation to provide rapid insights and recommendations. For example, in healthcare, decision science might analyze patient data to identify disease risk factors. On the other hand, Decision Intelligence could use predictive models to recommend personalized treatment plans based on individual patient characteristics.
2. AI Expert System Makeover – The New DI Platform.
For several decades, we have used expert systems, a subset of AI, with great success. For example, an expert system in finance may provide investment advice based on predefined rules. In years past, AI expert system developers did not have access to fast computers, high-speed internet, and advanced artificial intelligence such as machine learning and neural networks. As such, developers took a different approach to developing AI expert systems than today. Below is a definition of an Expert System.
AI Expert System Described.
“An expert system is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expertise and experience in a particular field. … Expert systems accumulate experience and facts in a knowledge base and integrate them with an inference or rules engine — a set of rules for applying the knowledge base to situations provided to the program.”
TechTarget
So, traditional AI expert systems are rules-based, built on a knowledge base of human expertise and using inferences and axioms to solve problems within specific domains. In contrast, DI-powered Decision Systems are data-centric, using machine learning to analyze data, learn, and adapt over time, making them suitable for complex, dynamic environments. While expert systems follow predefined rules, Decision Systems leverage advanced cloud-based infrastructure to provide dynamic, scalable decision support and even automate decision flows. As a result, DI can rapidly support decision cycles and even automate complete decision flows.
3. Data Science Vs. Decision Intelligence.
In this case, Data Science extracts insights from large datasets using statistical analysis and machine learning, focusing on uncovering patterns and trends. In contrast, Decision Intelligence (DI) combines data science with domain expertise, advanced analytics, automation, and computing networks to provide rapid, actionable recommendations to directly support decision-making. Hence, DI-powered Decision Systems analyze data, incorporate business rules and expert knowledge, and assimilate contextual information to enable optimal decisions.For instance, data science can be used to analyze customer behavior and identify potential churners. On the other hand, Decision Intelligence can directly support the decision cycle to provide personalized retention strategies based on the identified patterns.
Differentiating Views of Data
A key distinction between data science and decision intelligence lies in their applications. To further clarify, see quotes below from Chris Dowsett in regard to how data scientists and decision scientists view and use data.
Data Scientist
“Data is the Tool for Improving and Developing New Products Based on Robust Statistical Methods”
Decision Scientist
“Data is the Tool to Make Decisions”
For more on the difference between data science and data intelligence, see Chris Dowsett’s article, Data Science vs. Decision Science: What’s the Difference? Also, for more on what is Data Science, Click here.
4. BI Vs. DI – There Is A Difference.
Business intelligence (BI) involves collecting, analyzing, and visualizing data to provide insights into business operations and performance. It primarily focuses on descriptive analytics and reporting.
a. BI Focused On Insights Vs DI Focused On Making Decisions.
Decision Intelligence (DI) goes beyond Business Intelligence (BI) by utilizing a broader range of data analytics, including descriptive, diagnostic, predictive, and prescriptive analytics. As a result, this enables Decision Systems to support the entire decision-making process, providing decision-makers with the insights they need to make informed choices. In contrast, BI is primarily focused on historical analysis, whereas DI uses advanced algorithms to forecast future outcomes and recommend optimal actions. For instance, while BI might report on past sales performance by region, DI can predict future sales trends and suggest targeted marketing strategies for each region.
For more detailed breakout and descriptions of Data Analytics types to support decision-making, see my article, Supply Chain Analytics Types and The Way They Work To Better Empower Decision-Making.
b. Static BI Dashboards Vs Dynamic DI-Powered Decision Systems.
Another key difference between BI Dashboards and DI-Powered Decision Systems lies in how decision-makers interact with these systems. First, BI requires users to gather information from static or ad hoc reports, breaking down complex questions into multiple static ones. Without a doubt, this is a time-consuming and cumbersome process. In contrast, a Decision System is focused on the decision-maker where on-going interactions occur where humans and machines converse to arrive at the best decisions. So as Decision Systems evolve, especially with recent advancements with AI, these systems’ capabilities continue to evolve, proactively providing relevant, actionable insights based on the data available.
For more on BI versus DI, see Tellius’ article, Decision Intelligence vs Business Intelligence. Also, for more on AI and Decision System see my article, A Breakthrough In Decision Systems: The Need For AI Analytics To Best Empower Executive Insights.
More References.
here are more references on what is Decision Intelligence:
- Pascal Bornet’s article, Is Decision Intelligence The New AI?
- Paretos’ Decision Intelligence vs. Decision Automation
- SC Tech Insights’ article, Decision Intelligence Tech To Empower Logistics: The Ways This New Automation Is Better, for specific examples on how DI automation can benefit supply chains
- Lastly, see my article on how a DI-powered Decision System can provide agility in executive-level decision-making: 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 related articles from SC Tech Insights, see my articles on decision science.
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.