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What is Decision Intelligence and How To Adopt it In Your Organization

Yang Li
Chief Technology Officer
Oct. 26, 2023

Decision intelligence combines artificial intelligence, machine learning and other algorithms into a robust decision-making process. But what's this decision all about? Why is it part of Gartner's top strategic trends 2022, and how can your company adopt it for growth? These are some questions you will get answers to in this article. You will also learn the benefits, use cases and how Kyligence supports businesses' decision intelligence drive.


What is Decision Intelligence?


According to Gartner, "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.


Those disciplines include decision management (including advanced nondeterministic techniques such as agent-based systems), decision support, and techniques such as descriptive, diagnostics and predictive analytics."


What is the Difference between Business Intelligence and Decision Intelligence?


Decision intelligence is a data analytics approach that merges business intelligence with data science and other disciplines. It helps businesses investigate “the why” behind  business performance and find solutions faster.


For example, if you were analyzing data with legacy Business intelligence tools, you would only be able to get information like total sales in Q1. But with Decision Intelligence, you can dig deep to understand why the sales were less than Q1 in the previous year and how you arrived at that position.


It's a full-cycle decision process that merges descriptive, diagnostic and predictive analysis with consistent improvement with every new decision.


In contrast, business intelligence focuses on visualizing aggregated data. To extract -why & how -using legacy BI, expert analysts must review and interpret the analysis. This process is often time-consuming, slows the decision process, and impedes less-skilled business users from easily extracting insights. 


What's the Difference between Decision Intelligence and Artificial Intelligence (AI)?


Artificial Intelligence differs from Decision intelligence in terms of scope and application. For instance, AI is used in different fields as a broad technology involving machine learning, traditional rules-based systems, optimization techniques, natural language processing, and graph technologies. Similarly, AI has applications in different fields like generative AI tools, customer care bots, financial fraud detection, etc.


In contrast, Decision Intelligence uses AI, automation and other disciplines to generate deeper insight from data. It aims to deliver intelligent insight and a system that learns from previous decisions and improves with subsequent ones. Simply put, AI is a Technology, while Decision Intelligence is one of the many use cases where AI finds relevant application in the data analysis industry.


What Are the Benefits of Decision Intelligence?


Businesses can benefit from adopting Decision Intelligence for faster insights generation at top quality and flexible processes. Using the illustrative explanation of Wayne Eckerson, founder and principal consultant of Eckerson Group, "Decision intelligence platforms are like hiring an army of data analysts without spending any extra money, other than on a software license."


“Decision intelligence platforms are like hiring an army of data analysts without spending any extra money, other than on a software license.”


Wayne Eckerson

Founder and principal consultant, Eckerson Group


Improved Decision Quality


Business intelligence involves manual processes prone to error or other limitations. However, with Decision intelligence systems, businesses can consider all relationships that may have been ignored if Business Intelligence tools analyzed data. This in-depth analysis helps companies to access data-based insights, recommendations, and predictions. With this information, you can make better decisions considering all touchpoints, relationships and historical outcomes.


Additionally, Decision intelligence systems can learn from previous results using closed-loop learning to optimize future decisions that guarantee better performance. 


Enhanced Efficiency


With decision intelligence systems, you can save time and resources by automating decision-making and execution. Additionally, with systems automation, artificial intelligence and other features, you can quickly react to changing trends. You can use real-time analytics to track trends, identify root causes and promptly assess before they escalate. 


Competitive Advantage in The Market


With changing customers' needs, you must always be on top of your game. Decision intelligence systems offer agility that allows them to identify and take advantage of opportunities faster. These tools come with AI features that help analyze extensive data of different types, identifying relationships, patterns and trends. Once you have this information, you can create strategies, set goals and track them in real time.


Similarly, you have the insights to optimize your strategy on the go without waiting for an expert to create dashboards and analytics reports, which take hours, not days. Consistently delivering efficient products or updates that meet the user's needs will help you maintain a competitive advantage made possible with decision intelligence.


What are the Use Cases of Decision Intelligence?


Decision intelligence has use cases in many industries, helping transform how businesses make corporate decisions for growth. For example, the retail sector has benefited from adopting decision intelligence.


Retailers often struggle with optimizing pricing and marketing strategies in an unstable, uncertain market. A lot can change within a year, from political unrest to natural hazards, pandemic, and more. Using decision intelligence tools and real-time analytics, retailers can now identify trends quickly, update strategies and optimize methods to meet customers' expectations.


Imagine a retailer facing a perplexing challenge: sales in the East region have plummeted between October 3rd and 4th, 2018. Instead of relying solely on traditional data analysis, they employ Decision Intelligence tools. By inputting the query, "What strategic decisions could have influenced the sales drop in the East region from October 3rd to 4th, 2018?", the tool quickly evaluates a myriad of factors, from marketing decisions, inventory choices, to external market influences. It then provides a holistic view, suggesting that a combination of a discontinued marketing campaign and a competitor's aggressive promotion during those dates might have swayed customers. This Decision Intelligence insight not only identifies the issue but also recommends proactive strategies, such as launching a counter-promotion or revisiting marketing tactics. This example highlights the profound impact of Decision Intelligence in the retail sector, turning challenges into actionable strategies.

Fluctuations of total sales in retail generated by Kyligence Copilot

Similarly, healthcare businesses are using decision Intelligence to identify new markets. For example, the ambulatory and outpatient care sector can apply decision intelligence to determine where the market is moving. With decision intelligence tools, they can understand, measure, observe, and discover, facilitating shifting care. This insight helps them to identify methods to monitor the trends, develop new territories and strategies and other vital parts of the service process. Other use cases can be found in the trucking, finance, e-commerce and manufacturing industries, where data types often vary between structured and unstructured.


How to Get Started with Decision Intelligence?


Adopting decision intelligence is no longer a nice to have; it's a necessity for business growth. Here's a simplified process to adopt it in your business:


Step 1: Connect and upload your data to our secure platform


Sign up on Kyligence Zen and connect your various data platforms to your account. You can connect data from cloud storage, APIs, spreadsheets and other data repositories. Additionally, connecting to a source allows you to upload relevant data to the Kyligence Zen cloud system. Subsequently, you can access data in real time in a centralized location for analysis and decision making.


Step 2: Creating intelligence on top of your data


Start to define business objectives, insights, and more. You can use the Kyligence Zen features like copilot or goals to extract insights, use templates to analyze specific data or monitor particular metrics. You can now create intelligence from your data in the platform. Additionally, you can visualize your data or create automated reports from the different information.


Step 3: Personalize the platform by defining your unique business metrics


You need to set specific business goals to get the best from using Kyligence. Proceed to choose metrics you want to monitor from different departments like marketing, general business objectives accessible by all departments and more.


Step 4: Chat to your metrics... and more


Use the plain English queries feature in Kyligence Copilot to ask more profound questions about specific performance or insights. You can also use the root cause analysis to dig deeper and identify relationships beyond the regular reports. The AI powered decision intelligence tools enable you to dive deeper, get insights and investigate the why behind your business performance.




The business landscape changes daily, and you need proactive insights to maintain your competitive advantage. Decision Intelligence is a solution that combines artificial Intelligence, machine learning, business Intelligence and other disciplines to provide more intelligent quality insights. It allows you to go deeper and explore the why and how of your data performance. Start generating relevant insights for better decisions with Kyligence Zen. Sign up for a free trial to experience AI-powered personalized data analytics.