Decision Intelligence

Decision Intelligence (DI) is a multidisciplinary practice that combines data science, machine learning, behavioral science, computer science, and decision science to help organizations make quality decisions that are aligned with their business objectives. Unlike traditional decision-making frameworks, which rely on human intuition and dashboard, or static rules, the Decision Intelligence leverages advanced analytics and algorithms in combination with business rules to create better business outcomes.

Decision-Centric Approach® is the methodical implementation of Decision Intelligence. Through the Decision-Centric Approach®, the Decision Intelligence aligns people, rules, data, processes to create business values while ensuring they are aligned with business objectives and reduces organization’s operation risks and costs.

Decision Intelligence (DI) is a thinking framework while Open Decision Intelligence Platform (DIP) are the technology platforms enabling organizations to implement and practice DI.

Historically the whole journey started from the Business Rules Management System (BRMS) that was responsible for managing and automating business rules. That approach worked well for a decade, and it emerged in the Decision Management System (DMS) a.k.a. Decision Management Platform.

Decision Management Platform automates and manages the business rules, computational logic from the top-down approach by leveraging a Decision Modeling technique e.g. Decision Model and Notation (DMN).

Hence the Decision Management Platform is evolution of BRMS they deal with predefined, structured logic such as business rules, computational logic, decision flow and so on. This makes DMS very efficient to automate an isolated decision-making scenario such as pricing, eligibility and so on where business rules can be predefined based on set of policies, regulations, organization procedures, structures and guidelines.

The challenge is with all the advancement of AI/ML, data access and queries, explode of system development in organizations and use of various sources and types of data within enterprise, use and integrating BRMS and DMS become more and more complex and more reliance on IT and software development practices and teams.

Although the journey of the DI initiated pretty much from evolution of Business Rules Management System (BRMS) to Decision Management System or Platform (DMS). This was not the final branch that had inputs to evolution of Decision Intelligence.

Data-Driven Thinking

Other technologies and practices such as Data Science platforms as well as Analytics and business intelligence (ABI) platforms have contributed a lot to Decision Intelligence. The idea of data-driven decisions and the dogma of organizations can make better decisions using data also evolved. It turned out that data is not everything organizations need for making better decisions, although it is an important part of it but not the only component.

The constant failure of data-driven dogma to deliver business value either from analytics viewpoints (dashboard, reports, etc.) or from data science practices to develop and train machine learning models with the hope of enabling organizations to make better decisions has constantly failed during the last decade.

“Data and analytics leaders are investing heavily in analytics, machine learning, data science, AI and other related technologies. The belief is that such technologies can drive better decision making and thus business outcomes. However, there is a real gap between this belief and the idea about what a decision really is.”

– Andrew White
Distinguished VP Analyst, Chief of Research, Gartner

Although there were small wins for sure, but if we look at the ROI of enterprise organizations investing in technologies for the hope of better outcomes using Data and Analytics the value is not there. Therefore, we cannot summarize it as a win-win position for both technologies (product categories) and industries.

Process Oriented Approach

Standardization of processes is a very important part bringing efficiencies to a large-scale organization. The process-oriented approach has still in fact a very dominant way of implementing efficiencies however, there are multiple approaches to the business processes.

Process Mapping

known as a practice that process professionals (e.g. SIX SIGMA, etc.) go to organizations and start capturing value stream and processes using a form of process modeling notation such as BPMN, UPN, Flow Charts and etc.

Business Process Automation

Once the business processes are captured, some organizations use a form of process execution engine to automate the processes without relying on custom system development using IT and software development capacity. Even some would use Robotic Process Automation (RPA) to automate repetitive workflows and tasks as part of a business process. In these approaches automation team leverage workflow (RPA) and process automation (BPMS) tools to build forms, capture user inputs and kick-off workflows and processes and progress them toward and endpoint to create the outcomes in an automated or semi-automated manner.

Software System Development

Some organizations for standardizing the process and ensuring the outcome they leverage IT capacity to build custom software solutions in-house for bits and pieces of the captured business processes. In this approach they use Agile development practice to increase agility and transparency to business stack holders. The Process Mapping documents become the inputs for specification and requirement documents of what to build and how to build it.

Any of these three approaches work to some extent and ensures consistency of outcomes, but they all lack the flexibility required when facing changes that require quick response to market, regulation or drive innovation. Especially when it comes to changing and regulated environments around business decisions they make every day, keeping up with the pace is critical to the success of organizations.

Every single product category working in isolation failed to some extend in enabling organizations to make quick, accurate and consistent business decisions in changing and regulated environments. (Remember all enterprise organizations are regulated, some more than the other.)

The Open Decision Intelligence Platform intends fill this gap and to make decision automation, decision augmentation and decision support possible at enterprise level: cross-function, cross-team, cross-department, cross-system, and cross-process to drive business outcomes.

DI creates a holistic decision-making ecosystem within an enterprise organization. Based on an explicitly defined business decision leveraging a Decision Modeling technique, it combines

• Business rules and computational logic
• Advanced Machine Learning and analytics
• Data connection, integration, ingestion
• Insights, dashboards, IT Systems and processes

DI combines the power of explicitly modeled business decisions with Business Rues, Machine Learning, Data, and other AI techniques to build a Composite AI model. Then Open Decision Intelligence Platform (DIP) will orchestrate people and systems and processes around it to deliver the objectives of the business decisions in real-time at scale.

DI platforms require a wide range of capability, not all platforms provide everything needed for DI platforms. At a very high level there are several requirements to be identify as a Open Decision Intelligence Platform or DIP:

• Decision modeling and execution
• Orchestration design and execution
• Composite AI to combine multiple AI techniques
• Decision audit

Based on the above requirements we can breakdown the Decision Intelligence key components that creates a robust and scalable decision-making and execution platform:

Implementing Decision Intelligence offers numerous benefits that can enhance organizational performance, efficiency, and competitiveness:

1. Decision Clarity and Transparency: Decision Intelligence leverages a top-down approach and Decision Modeling such as Decision Model and Notation (DMN) with Conformance Level 3. This will increase the level of clarity and transparency of the decisions.
2. Increased Agility and Responsiveness: Decision Intelligence enables organizations to make quick, informed decisions based on real-time data, which is essential in fast-changing markets like finance, retail, government, and healthcare.
3. Optimized Outcomes: By simulating potential outcomes, DI helps businesses choose the most effective strategies. This capability is valuable for resource allocation, risk assessment, and performance optimization.
4. Cross-Team Collaboration: DI platforms facilitate collaboration between data scientists, analysts, and business users, creating a shared understanding of decisioning scenario. This collaborative environment helps break down data silos and ensures decisions align with business objectives.
5. Reduced Bias in Decision-Making: Decision Intelligence minimizes human ensuring the decisions are explicitly defined. This ensures more transparent and fairer, consistent decisions, particularly in critical areas like hiring, pricing, and resource allocation as well as regulated environment such as banking, finance and health.
6. Utilizing AI in Decision-Making: Decision Intelligence uses decision modeling as the basis for designing composite AI models. It means each node on the model (i.e. Decision Graph) can leverage a relevant AI approach and algorithm and decision model as a holistic view of business decision will leverage multiple techniques depending on the nature of the decision units.
7. Adaptive Decisions: Decision Intelligence enables organizations to build situation-aware decisions where part of the model is activated based on certain events and circumstances. Additionally, DI platform can retrain models based on new data points or adjust to new conditions for new scenarios automatically or via an approval workflow in a change management process.

Decision Intelligence is the catalyst for transforming data and analytics functions' cost-center to profit-center nature. The Data and Analytics function is usually a cost center in organizations due to the cost of infrastructure, staff salaries, and operational expenses for maintaining data governance, compliance, and security.

However, this will change by just shifting the focus to business decisions by leveraging DI. Decision intelligence empowers the Data and Analytics function to be a profit center rather than a cost center by shifting the focus from data to decision.

• Aligning D&A with Business Outcomes — Focusing on decision models that demonstrate the map between data and business decisions will improve customer retention by personalization, reduce cost via decision automation, and increase revenue by optimizing pricing.
• Operationalizing Analytics — Decision Intelligence will embed analytics into workflows and eliminate the disconnect between the D&A team and operations. As a result, it will reduce the time-to-value for analytics investment.
• Enhancing ROI of D&A Investments — By optimizing the decision outcomes and leveraging the DI orchestration, D&A can directly contribute to operational performance and reduce ineffectiveness across organizations.
• Improving Agility and Adaptability — Decision-centric organizations can respond to changes quicker by leveraging DI; market dynamics, regulations, and customer demands can become the source of innovation and competitive advantage rather than frustration and delays.
• Creating New Revenue Streams — By leveraging DI in the personalization space, organizations can unlock new opportunities as the engagements with clients and prospects focus on their goals rather than traditional marketing product pushes and irrelevant promotions.

Decision Intelligence is versatile and can be applied across industries and business functions.

Here are some common applications:

• Customer Experience Optimization: In retail, e-commerce and financial services, DI helps personalize customer interactions by analyzing purchase behavior, preferences, and browsing patterns. This enables businesses to deliver tailored promotions, enhancing customer loyalty and lifetime value.
• Financial Services: In banking and insurance, DI assists with credit risk assessment, fraud detection, and loan approvals. By combining predictive analytics with business rules, financial institutions can improve accuracy in risk assessments and optimize financial decisions for quality and speed.
• Supply Chain Management: For manufacturing and logistics companies, DI aids in demand forecasting, inventory optimization, and logistics planning. By analyzing past and real-time supply chain data, organizations can reduce costs, avoid stockouts, and improve efficiency.
• Healthcare and Diagnostics: In healthcare, DI supports decision-making in patient care, treatment planning, and diagnostics. By analyzing patient data and treatment outcomes, healthcare providers can improve care quality, reduce errors, and optimize treatment plans.
• Human Resources: DI helps HR departments with workforce planning, talent acquisition, and employee retention strategies. By analyzing performance data and market trends, organizations can make data-driven hiring and retention decisions that align with business goals.
• Energy and Utilities: In the energy sector, DI optimizes resource allocation, forecasts demand and manages grid operations. This is crucial for improving sustainability, reducing operational costs, and ensuring reliable service delivery. Or for instance helps the companies with onboarding the new accounts and decide on the required documents, procedures and even a local energy distributor.

Across all the industries, the core use cases for DI is to empower organizations for quick adaptation to changes while reduce the operation cost using decision automation, decision augmentation and provide decision support across teams, functions and groups.

Implementing Decision Intelligence requires a shift of mindset from data, dashboard, algorithm, rules to business decision. This is done through the methodology called Decision-Centric Approach®

The Decision-Centric Approach® is a methodology that brings people, rules, data, and processes together to ensure organizations can consistently make optimized, customer-centric, and situation-aware business decisions while they meet the objectives on revenue, costs, and mitigating risks.

The Decision-Centric Approach® ensures decisions are accurate, consistent and transparent while they can be adjusted quickly and adapted to changing environments whether those changes are forced by regulatory requirements or other factors such as competitive marketplace, or customer demands.

One of the significance of Decision Intelligence is its ability to support the full decision cycle rather than just one part of the small and isolated scenario. This holistic approach is designed by Decision-Centric Approach® and the ability of DI platform to automate, augment and support decisions and integrate them inside operational workflows directly.

This is why Decision-Centric Approach® provides the Decision Intelligence Architecture to ensure organizations that deal with variety of changes from different sources in their operating environment can implement DI successfully.

Decision Intelligence Architecture enables organizations to gradually and incrementally achieve higher degrees of intelligence that improve and influence business operations and change organizational behaviors.

Decision Intelligence aims to apply AI in the context of business decisions and create intelligence that not only can support humans in making decisions but also can make, execute, and automate decisions on their behalf in a controlled and reliable manner.

Decision Intelligence Architecture defines five layers of intelligence in organizations, each of which is a prerequisite of the next layer.

• Knowledge Core: This is the core foundation of organizational knowledge. It captures the domain terminologies, language, definitions, concepts, facts, and, more importantly, business decisions that organizations make and execute.
• Intelligence Core: This layer is a Symbolic AI that allows the buildup of core intelligence that is 100% explainable, reliable, and safe to deploy to critical operations. It is based on the open standard Decision Model and Notation (DMN).
• Expert Intelligence: On top of the previous layer, it enables organizations to build intelligence that interacts with their environment, senses events, captures data, and processes them through the Intelligence Core while ensuring the decisions' outcomes are optimized based on business objectives.
• Cognitive Intelligence: It is based on Neuro-symbolic AI, which allows organizations to build a composite AI model based on a previous layer that uses machine learning, deep learning, and symbolic reasoning to take advantage of the best of both worlds.
• Autonomous Intelligence: This enables organizations to build intelligence on top of the previous layer, enabling systems to learn and adapt to changing and dynamic environments with a full capacity of reasoning, planning, etc., inherited from previous layers.

As organizations face growing data volumes and increasingly complex decision requirements, Decision Intelligence is emerging as a transformative solution for the future. With advancements in AI, machine learning, and predictive analytics, DI is set to become even more accurate, intuitive, and scalable. The future of DI may include:

• Greater Integration with AI and Machine Learning: Enhanced AI capabilities will allow DI systems to make more nuanced, autonomous decisions, minimizing human intervention for routine decisions.
• Increased Adoption Across Industries: As more organizations recognize the value of DI, it is likely to be adopted in sectors ranging from government and education to real estate and public safety.
• Improved User Interfaces: Future DI platforms will prioritize user experience, with intuitive interfaces that make it easier for non-technical users to leverage advanced decision models and analytics.
• Real-Time Decision-Making: As data collection and processing speeds increase, DI will enable organizations to make decisions in real time, allowing them to respond instantly to changing conditions.

Decision Intelligence shifts the view of organizations from data, ai, process, rules to how organizations make decisions by combining data science, analytics, and automation to recommend and deliver actions and optimize decision outcomes.

By leveraging the Open Decision Intelligence Platform and applying Decision-Centric Approach®, enterprise organizations can enhance their decision-making scenarios, respond rapidly to market changes, and drive sustainable growth. DI provides the foundation for decision agility, accuracy, and strategic foresight, empowering organizations to stay competitive and resilient.