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). Indeed, DI is a unique concept that can easily be mistaken for other IT terms. So in this article, I’ll explain what Decision Intelligence is—and what it is not. Further, I’ll look at how DI platforms empower data-intensive industries like supply chains. In fact, DI can dramatically improve decision-making, and even automate decision cycles. Additionally, I will clarify how Decision Intelligence stands apart from its counterparts—decision science, expert AI, data science, and business intelligence (BI).
Decision Intelligence (DI) Defined And Its Application To Supply Chains.
Decision intelligence (DI) is a fairly new term. Its modern formulation goes back to 2008 where Quantellia began to offer DI solutions to its clients. It wasn’t till 2019 that DI was popularized with the publication of Lorien Pratt’s book, How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World. Also during this same period, Google launched a decision intelligence department. Subsequently, in October 2021 Gartner analysts identified DI as one of the most impactful tech trends for 2022.
To get a better understanding of what decision intelligence is, below are a couple of definitions. The first one is just a simple definition of DI. While the second definition specifically addresses Decision Intelligence for supply chains.
1. Decision Intelligence Definition.
“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
2. Description Of Analytics and Decision Intelligence (A&DI) Technology For Supply Chains.
“… 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
3. Decision Intelligence Is More Than Just A New IT Marketing Term.
Positively, the term “decision intelligence” is more than just an IT marketing term. In fact, it is very useful in re-focusing analytics tools, both existing and emerging, or directly supporting decision-making. This is because Decision Intelligence leverages a diverse array of analytical tools such as business intelligence, data science, decision science, and expert AI systems. Additionally, Decision Intelligence capabilities are best deployed as a software platform exclusively focused on decision-making.
Another aspect of Decision Intelligence is that it is closely akin to expert systems and business process automation. One reason that I say this is that Decision Intelligence (DI), just like Business Process Automation (BPA), can have three modes of processing: support, augmentation, and automation. Thus, businesses can deploy decision intelligence platforms for decision support, decision augmentation, or even autonomous decision-making. At the same time, it is different from BPA in that DI supports decision-making versus task-based business 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.
4. Decision Intelligence Directly Supports The Flow Of Information Within the Decision-Making Cycle.
Full-fledged decision intelligence platforms are just starting to emerge. Now in the past, we did have software to support decision-making such as expert systems. However, it was more ad hoc and reactive such as an expert system that responds to a user question. On the other hand, DI is capable of supporting the entire decision cycle and working within a corporate environment. Specifically, Decision Intelligence platforms are designed to sense, analyze, make recommendations, and even directly support rapid decision-making, and implementation of decisions.
Further, these DI systems also have the potential to explain and justify their recommendations to business users. Even better, these DI platforms do not necessarily require decision-makers to work through IT departments, analysts, or data scientists. For example, a DI system could analyze both real-time and historical sales data, market trends, and customer behavior to help businesses optimize their pricing strategies and improve profitability. Also, these DI systems have the capability to measure and learn from previous decisions to improve decision effectiveness. For more on Decision Intelligence Platforms, 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.
a. 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
b. 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 focuses on understanding the decision-making process and applying statistical models to make informed choices. While Decision Intelligence takes it a step further in supporting decision-making by incorporating advanced technologies and algorithms. Moreover, decision science primarily relies on human expertise and statistical analysis.
Whereas decision intelligence leverages machine learning algorithms, advanced analytics and automation to provide rapid insights and recommendations. For instance, in the field of healthcare, decision science may involve analyzing patient data to identify risk factors for certain diseases. While decision intelligence can 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. To sum it up, the following description from TechTarget embodies how developers built traditional AI expert systems.
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 systems, built around a knowledge base of human expertise. Specifically, they use logical rules to solve problems, can explain their reasoning, and are designed for specific domains.
On the other hand, Decision Intelligence platforms are newer, data-centric systems that use machine learning to analyze data and support decision-making. Indeed, with the advances in AI, DI platforms can learn and adapt over time, consider a broader range of factors, and can scale to handle large amounts of data, making them useful in complex, dynamic environments. Further, DI platforms are designed to leverage advanced cloud-based IT infrastructure. Thus, Decision Intelligence can rapidly support decision cycles and even automate complete decision flows. In essence, while expert systems follow predefined rules, DI platforms use data and learning algorithms to provide dynamic, scalable decision support.
3. Data Science Vs. Decision Intelligence.
Here, data science involves extracting insights from large volumes of data using statistical analysis and machine learning techniques. It focuses on uncovering patterns, trends, and correlations in data to support decision-making. On the other hand, DI combines data science with domain expertise, advanced analytics, and advanced computing networks to provide rapid, actionable recommendations for decision-making.
Decision intelligence platforms not only analyze data but also incorporate business rules, expert knowledge, and rapidly assimilate contextual information to generate 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.
Another way to compare data science with decision intelligence is that a data scientist uses data to improve and develop new IT products. Whereas, a decision scientist uses data as a tool to support decision-making. Further, a data scientist sits hip-to-hip with data and statistical rigor. On the other hand, a decision scientist sits hip-to-hip with decision-makers and management to help them make the best decisions for the business. See quotes below from Chris Dowsett in regard to how data scientists and decision scientists view and use data.
a. Data Scientist’s View Of Data.
“Data is the Tool for Improving and Developing New Products Based on Robust Statistical Methods”
Chris Dowsett
b. Decision Scientist’s View Of Data.
“Data is the Tool to Make Decisions”
Chris Dowsett
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?
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.
DI goes beyond BI by incorporating diagnostic, predictive and prescriptive analytics within a software platform. As a result, decision platforms directly support decision-makers to make the best decisions. While BI provides historical and current data analysis, decision intelligence platforms use advanced algorithms to forecast future outcomes and recommend optimal actions for a specific decision. For example, BI may provide a report on sales performance by region. On the other hand, Decision Intelligence can predict future sales trends and suggest targeted marketing campaigns for each region on demand.
b. Static BI Platforms Vs Dynamic DI Platforms.
Another key difference between BI and DI platforms is the way that businesses will ask questions and interact with these platforms. With BI, a business person or analyst will use static or ad hoc BI reports to gather information on a series of questions, and then the analyst or decision-maker will come to a conclusion. This process is followed because of the nature of the BI static infrastructure and capabilities. Thus, business persons will usually need to break down their business questions into a number of static questions to support their decisions. Now as mentioned above, they can use BI ad hoc queries, but these queries are just variations of these static questions.
On the other hand, DI is decision focused where ideally a business person just makes a decision based on the system’s recommendations. So as DI platforms evolve, they have the capability to proactively provide relevant, actionable insights based on the data available. For more on BI versus DI, see Tellius’ article, Decision Intelligence vs Business Intelligence.
More References.
For more references on what is Decision Intelligence, see Pascal Bornet’s article, Is Decision Intelligence The New AI? And Paretos’ Decision Intelligence vs. Decision Automation. Also, see my 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, to see how a Decision Intelligence platform can provide agility in executive-level decision-making, 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
Imagine if corporate executives could become extremely agile in their decision-making using software to maximize supply chain performance. Indeed, this platform would collect targeted data, provide insights, options, and supports decision communications without middlemen.
Click here to find out how advances in AI and analytics are making this possible. Also, this article discusses the growing need for agile decision-making in digital supply chains, current software limitations, and key attributes needed in new decision tools for executives to include CEOs, COOs, and CFOs.
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 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.