Decision Intelligence: The New Era of Data-Driven Decision-Making

What’s better than making a good decision? Having a reliable process for making more of them in the future. The question is how you would judge whether your process is reliable — or if previous good results were purely due to luck. Decision intelligence can provide the answer. This article explores what decision intelligence is and the foundational data capabilities necessary for achieving it. 

What is Decision Intelligence? 

Decision intelligence is about leveraging data on how decisions are made to continue improving the decision-making process. It involves turning data into data-driven insights, making decisions with those insights, and understanding how those decisions were made. Essentially, it treats decision-making as a concrete, measurable business process. The goal is to support, automate, and augment human decisions by identifying the "why" behind business performance and using AI-powered insights to make more informed decisions. 

Decision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. Its application provides a framework for best practices in organizational decision-making and processes for applying computational technologies such as machine learning, natural language processing, reasoning, and semantics at scale. 

The basic idea is that decisions to act are based on our understanding of how these actions will lead to outcomes. Decision intelligence involves analyzing this chain of cause and effect, and decision modeling is a visual language for representing these chains. 

Traditional BI Tools and Their Role in Decision Intelligence 

Traditional BI tools play a crucial but limited role in the decision intelligence framework, specifically with respect to decision support, i.e. presenting data and insights to support data analysis, trend identification, and strategic decision-making within an organization. 

These tools enable organizations to transform raw data into valuable insights, leading to improved decision-making, optimized operations, and the ability to stay competitive in today’s data-centric markets.  

However, such BI tools do not recommend what action or decision to make. Their current capabilities are very limited with respect to decision augmentation and decision automation, but we look forward to new developments as decision intelligence technology matures.  

Moving Towards a Decision-Centric Future with AI 

The future of enterprises is still data-driven, but their next steps are towards becoming decision-centric. Decision-centricity is all about linking data-driven insights to actions — repeatably, measurably, and effectively. Decision intelligence is the key to creating these links.   

As advances in data, analytics, and AI technologies roll on, decision intelligence is set to reshape how organizations approach decision-making. With that said, the goal of decision intelligence is not to replace human decision-makers but rather to provide a structured and rigorous approach to making better decisions. 

While the concept of decision intelligence is not new, it has gained renewed significance over the past few years largely thanks to the expanding capabilities of AI. Below are three use cases for AI-powered decision intelligence. 

 

Decision Support 

Decision support is the most common application of AI-powered business intelligence (BI) tools. Decision support essentially provides pictures of the past to help people make decisions in the present or near-future.  

Decision Automation

AI can be used to automate some routine decisions involved in business processes. Given that many of today’s AI models can be prone to hallucinate, it will take some time before AI is fully entrusted with making all the decisions necessary to complete a given process.   

Decision Augmentation 

Decision augmentation involves using AI to discern patterns from past decisions, the scenarios that brought about such decisions, and the consequences of those decisions. Armed with these patterns, the AI recommends options for decision-makers and couples — or augments — each option with a projected outcome. Unlike decision support, decision augmentation provides decision-makers with pictures of the foreseeable future. Moreover, decision augmentation is a huge leap forward from predictive analytics, which has users manually setting the conditions of their desired option for the analytics to produce a prediction.

Technological Capabilities Required to Build Decision Intelligence

To gain decision intelligence, organizations need a combination of capabilities to both obtain data-driven insights and understand how those insights are translated into action. These capabilities fall under three categories:  

1. Foundational Capabilities 

These capabilities enable organizations to make data usable for a wide variety of business purposes, including decision intelligence: 

• Data Collection: Gathering relevant data from various sources, including internal systems, external databases, and real-time data streams. Relevant data that enables decision-making intelligence includes: 

• Decision logs: Records containing details surrounding decisions, such as: 

• Decision-makers involved  
• Date and time of the decision  
• Relevant data points considered  
• Final decision made  
• Reasons for the decision   

• Data Integration: Combining data from different sources into a unified dataset for analysis.
• Data Processing: Storing, cleansing, transforming, and managing data effectively to ensure quality data and insights for decision making.
• AI decision traces: Leverage multiple aspects of artificial intelligence (AI) and machine learning (ML) to enhance decision-making processes such as Cognitive Science, Behavioral Analytics and LLMs.
• Data & AI Governance: Implementing policies and procedures to ensure data security, privacy, and compliance with regulations.
• Data Visualization: Presenting data in a clear and understandable way to facilitate analysis and decision-making. 

2. Capabilities Needed To Derive Valuable Insights from Data 

These capabilities involve employing various analytical techniques to uncover meaningful patterns, trends, and insights from diverse data sources: 

• Data Analysis: Applying statistical methods, machine learning algorithms, and other analytical techniques to extract insights from data.
• Predictive Analytics: Using historical data to forecast future trends and outcomes.
• Prescriptive Analytics: Suggesting optimal actions based on data analysis and predictive modeling.
• Natural Language Processing: Analyzing unstructured text data to extract insights. 

3. Capabilities Needed To Effectively Analyze and Understand Decision-Making Processes 

These capabilities involve utilizing techniques to examine how decisions are made and how those decisions can be improved: 

• Decision Modeling: Representing decision-making problems in a structured and quantifiable way. It involves defining the decision objectives, identifying the available options, and assessing the potential outcomes and consequences of each option. Decision models can be used to analyze different scenarios, evaluate the trade-offs between competing objectives, and make informed decisions.  
• Process Mining: Analyzing historical data to identify patterns and inefficiencies in decision-making processes.
• Behavioral Analytics: Studying the behavior of decision-makers to understand their decision-making styles and preferences.
• Cognitive Science: Seeking to understand how humans make decisions and identify cognitive biases.
• Simulation Modeling: Creating models of decision-making processes to test different scenarios and evaluate potential outcomes. 
• AI and Machine Learning: Creating recommendations and spotting patterns in how decision-makers act upon these recommendations. In the case of AI-driven recommendations, decision intelligence would involve tracking how the AI "decided" to make that recommendation. This might involve:  

• Examining the AI's logic: Understanding the rules, algorithms, or models used to reach the decision.  
• Analyzing the data used: Identifying the data points that influenced the AI's decision.  
• Identifying potential biases: Recognizing any biases that might be inherent in the AI's algorithms or the data it uses. 
   

Takeaway

While successful enterprises will continue investing in gaining more high-quality data and better analytics, it is increasingly important to understand how decisions are made based on data-driven insights. Decision intelligence can help them gain this understanding. Organizations that invest in the necessary capabilities will be well-positioned to use decision intelligence to navigate the complexities of today’s business environment and achieve long-term success.