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This Is What Decision Intelligence Technology Is And Know What Its Not

Decision Intelligence (DI) is a groundbreaking technology that can take business decision-making to unparalleled heights. While DI platforms can employ artificial intelligence (AI), there’s so much more to it than just AI. Now, the concept of decision intelligence can be easily confused with other IT jargon. So in this article, I’ll  explain what a decision intelligence platform truly is—and what it isn’t. Further, I’ll focus on business supply chains. This is because DI platforms have the potential to make a major impact in logistics decision-making. Moreover, I’ll break down the distinctions between decision intelligence and its counterparts: decision science, expert systems, data science, and business intelligence (BI). 

Decision Intelligence (DI) Defined And Application To Supply Chains.

decision intelligence

Decision intelligence (DI) is a fairly new term. For instance, just in 2018 Google launched a decision intelligence department. Then the term was popularized in Lorien Pratt’s 2019 book, How Decision Intelligence Connects Data, Actions, and Outcomes for a Better World. Lastly in October 2021, Gartner analysts identified decision intelligence 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 decision intelligence. While the second definition specifically addresses decision intelligence platforms 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.” 

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).”

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. At the same time, it has many of the same characteristics as other related technology terms such as business intelligence, data science, decision science, and expert systems.

Now as the definitions state above, decision intelligence leverages many disciplines to include decision science, data science, and AI. Furthermore, decision intelligence capabilities are usually deployed as software automation platforms. Lastly as with expert systems and business process automation, it has three modes of operations: support, augmentation, and automation. Thus, businesses can deploy decision intelligence platforms for decision support, decision augmentation, or even autonomous decision-making. See my article, Business Automation AI Remake: First Just Tech To Empower Processes And Now Operate Autonomously, for a more detailed discussion on the nature and evolution of automation.

4. DI Platforms Automate The Flow Of Decision-Making.

Decision intelligence platforms are just starting to emerge. Now, we have had automation in the past to support decision-making. However, the difference is decision intelligence has the potential to sense, analyze, make recommendations, and even act real-time. Indeed, automation is no longer limited to streamlining linear business process flows.

Further, these DI systems also have the potential to explain and justify its recommendations to business users. Even better, these DI platforms do not require business users to work through IT departments, analysts, data scientists. For example, a DI system can 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.

In the following paragraphs I’ll explain what decision intelligence is not and how it compares to other technical disciplines such as decision science, expert system, data science, and business intelligence. In many respects, decision intelligence is a new type of automation that is leveraging the best of previous automation initiatives. Most noteworthy, DI systems leverage recent advances in AI to provide an automated decision-making workflow that is exclusively geared toward optimizing business decision-making.

Decision Science Vs. Decision Intelligence.

Decision-makers have relied upon decision science long before there was automation. At first, decision science relied on math and statistics to support decision-making. Nowadays with computers, decision intelligence as well as other types of decision support automation is rooted in decision science. Below are two definitions that are relevant to both decision science and decision intelligence.

1. 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
2. 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 by incorporating advanced technologies and algorithms. Moreover, decision science primarily relies on human expertise and statistical analysis.

Whereas decision intelligence leverages machine learning algorithms and automation to provide real-time 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.

Expert System Makeover – The New DI Platform.

For several decades, we have used expert systems with great success. For example, an expert system in finance may provide investment advice based on predefined rules. In years past, 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 expert systems than today. To sum it up, the following description from TechTarget embodies how developers built traditional expert systems.

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.”


Data Science Vs. Decision Intelligence.

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, decision intelligence combines data science with domain expertise and automation to provide real-time actionable recommendations.

Decision intelligence platforms not only analyze data but also incorporate business rules, expert knowledge, and real-time 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 automate 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 make decisions. 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 Chis Dowsett in regard to how data scientists and decision scientists view and use data.

1. Data Scientist’s View Of Data.

“Data is the Tool for Improving and Developing New Products Based on Robust Statistical Methods”

Chris Dowsett
2. 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 Chis Dowsett’s article, Data Science vs. Decision Science: What’s the Difference?

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. Decision intelligence goes beyond BI by incorporating predictive and prescriptive analytics to support decision-making in real-time. While BI provides historical and current data analysis, decision intelligence platforms use advanced algorithms to forecast future outcomes and recommend optimal actions in real-time. For example, BI may provide a report on sales performance by region. While decision intelligence can predict future sales trends and suggest targeted marketing campaigns for each region on demand.

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 ask a series of static questions to come to a conclusion. This is because of the nature of the BI static infrastructure and capabilities. Therefore, business persons will usually need to break down their business questions into a number of static questions to support their decisions. Now, in some cases with BI they can use 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 needs to ask for decision recommendations. So as DI platforms evolve, they have the capability to automatically in real-time provide relevant, actionable insights based on the data available. For more on BI versus DI, see Tellius’ article, Decision Intelligence vs Business Intelligence.

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.

Decision Intelligence Tech To Empower Logistics: The Ways This New Automation Is Better.

Discover how decision intelligence technology is poised to transform the logistics industry. Indeed, it’s here to dramatically automate decision-making flows, even real-time if needed. Click here for 10 unique examples on how DI software can support the full range of supply chain functions from planning and sourcing to final delivery and customer service.

For more related articles from SC Tech Insights, see my articles on decision science.

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