In today’s fast-paced business environment, data is abundant, but the ability to derive actionable insights and make informed decisions is what sets successful companies apart. A shift from Business Intelligence (BI) to Decision Intelligence (DI) marks an evolution in decision-making, enabling organizations to move beyond historical analysis to real-time, context-driven insights. Such a transformation, supported by strong data governance practices, empowers businesses to make decisions that are not just informed by data but actively guided by it.
What is Decision Intelligence and “Decision Model & Notation”?
Decision Intelligence (DI) is an emerging discipline that combines data science, social and environmental data and science, and managerial expertise to design, model, align, execute, and track decision models and processes within an organization.
DI seeks to overcome the “complexity ceiling” in decision-making, where traditional methods struggle to handle increasingly complex situations. By integrating machine learning, AI and business intelligence, DI enables organizations to treat with these complexities better and make more informed, formalized and hence effective decisions.
The Decision Model & Notation (DMN) is a formalization published by The Object Management Group (https://www.omg.org/spec/DMN), while the “Constraint Decision Model and Notation” (cDMN ) defines a notation for expressing knowledge in a tabular, intuitive format. Both notations can be integrated in Business Process Model and Notation-based process models, also as defined by the OMG (https://www.omg.org/spec/BPMN).
“The primary goal of DMN is to provide a common notation that is readily understandable by all business users, from the business analysts needing to create initial decision requirements and then more detailed decision models, to the technical developers responsible for automating the decisions in processes, and finally, to the businesspeople who will manage and monitor those decisions. DMN creates a standardized bridge for the gap between the business decision design and decision implementation. DMN notation is designed to be usable alongside the standard BPMN business process notation.”
This standard encompasses three key components:
- Decision Requirements Diagrams: These diagrams illustrate the interconnections between various elements of decision-making, forming a network of dependencies.
- Decision Tables: These tables outline the specific criteria and rules used to make each decision within the network.
- Business Context: This includes considerations such as the roles and responsibilities within organizations and the potential impact on performance metrics.
Additionally, the standard includes a user-friendly language called Friendly Enough Expression Language (FEEL), which is used to evaluate expressions within decision tables and other logical structures.
Some Use Cases and Examples of Decision Intelligence
- Strategic Resource Allocation in Complex Environments: A global energy company uses DI to optimize resource allocation across regions with varying risks and market conditions. By integrating diverse data sources into a unified decision model, the company can also simulate scenarios, predict outcomes, and recommend optimal strategies that align with long-term goals.
- Real-Time Crisis Management: In a crisis, such as a market turmoil or a significant natural disaster, DI systems integrate real-time data to assess the situation and recommend immediate actions. This structured approach allows the organization to respond swiftly and effectively, ensuring that decisions are based on the most current and relevant information.
- Optimizing Complex Supply Chains: A manufacturing company uses DI to model disruption scenarios and predict their impact on the supply chain. DI systems recommend preemptive actions, such as diversifying suppliers, helping the company maintain resilience in the face of dynamic challenges.
The Role of Governance and Semantic Survival
Effective DI requires more than just advanced analytics and AI; it depends on a robust data governance framework that ensures data quality, consistency, and trustworthiness across the organization, especially in heterogeneous and distributed data frameworks. Governance encompasses the policies, procedures, and standards that manage data throughout its lifecycle, ensuring that the right data is available at the right time and in the right context.
One of the critical aspects of governance in DI is semantic survival—the preservation of the meaning and context of data as it moves through various systems and is used in different decision-making scenarios. Strong semantics survival ensure that data remains consistent and meaningful over time, even as it is transformed, combined with other data sources, or used in different business contexts.
In larger and more complex scenarios, where data is drawn from numerous sources and used across various data repositories, strong governance and semantics are of even bigger importance. Without it, there is a risk that data may lose its intended meaning or being erroneously reinterpreted, leading to flawed insights and potentially damaging decisions.
Governance practices that focus on maintaining semantic integrity help to build a solid informational foundation, enabling DI to function effectively and ensuring that the decisions made are based on reliable and contextually accurate data.
The Role of Process Intelligence in Decision Intelligence
Process Intelligence (PI), a slightly younger discipline in business analytics and which can be executed also in an independent way, is focusing on analyzing and improving how business processes are executed, identifying inefficiencies, and optimizing workflows. When combined with Decision Intelligence, PI ensures that data-driven decisions are grounded in a deep understanding of existing operational processes.
This integration helps organizations execute decisions effectively, aligning them with the operational realities of the business and enabling continuous improvement over time.
By providing insights into how decisions impact ongoing processes, PI supports DI in closing the gap between decision-making and process execution. This ensures that decisions and the corresponding effects on processes are not only well-informed but also practically implementable, contributing to overall organizational effectiveness.
Technical and Organizational Aspects of Decision Intelligence
DI is both a technical and organizational approach. It relies on advanced technologies like AI and machine learning to model and predict decision outcomes, while also requiring a shift in organizational culture towards data-driven decision-making.
This involves strong data governance to ensure data quality and consistency, as well as the integration of process intelligence to align decisions with operational realities.
Conclusion
The evolution from Business Intelligence to Decision Intelligence represents an evolutionary shift in how organizations approach decision-making.
By integrating real-time, context-driven recommendations with additional insights from non-classical sources, DI empowers businesses to make more informed and executable decisions. This approach not only enhances decision-making but also ensures that these decisions can be effectively implemented, turning data into a sustained competitive advantage.
Author: Philipp Nell, Solution Architect Data Management and AI, August 2024
