Decision Intelligence: Definition, Functionality & Use Cases
Decision Intelligence combines data integration, AI analytics, and a structured decision lifecycle. The approach aims to systematically understand how decisions are made and to specifically improve them. This involves both analytical support for decision-makers and the automation of recurring decision processes.
What is Decision Intelligence?
Decision Intelligence refers to an approach that combines data, analytical methods, and technology-supported systems to make decisions faster, more transparent, and of higher quality. From a platform perspective, this approach forms the basis for company-wide analytics built on "connected and contextualized data."
The core lies in the transition from raw data to actionable steps. Relevant data is collected from various sources, from which insights and proposed actions are derived. Decisions made are then tracked through monitoring, allowing organizations to learn from past decisions and refine future ones.
How does Decision Intelligence work?
Decision Intelligence platforms are built on three central pillars.
Trusted Data forms the foundation: Decisions are only as good as the data they are based on. This includes the consolidation of multi-source data – internal and external, structured and unstructured. Through "Entity Resolution," separate, inconsistent datasets are consolidated into a correct, unified data picture, known as "single views."
Composite AI is the second pillar: Since no single model or method technique is suitable for every decision, multiple AI and analytical approaches are combined.
Contextual Analytics forms the third pillar: Context is what transforms raw data into practically relevant insights.
Platform implementation also includes decision execution – in the form of real-time or batch execution, by human and machine actors. Depending on the use case, decisions are either supported (augmentation) or fully automated. Platforms are intended to extend, not replace, the existing system landscape – with connections to ERP, APS, and BI systems, ensuring data access and accuracy are maintained.
Practical Examples and Use Cases
Decision Intelligence is primarily applied in strategic and operational areas.
In the Supply Chain Planning demand planning, inventory management, transport planning, supply network planning, and production planning are cited as typical application areas.
In the Retail the principle can be described as follows: Inventory data is analyzed to identify which products are selling more or less. This leads to data-driven decisions for inventory optimization.
Other use cases include:
- Fraud detection and financial crime with continuous monitoring
- Creation of a 360-degree customer view for prospecting and support
- Transparency and explainability of decisions in public and private organizations
Decision Intelligence vs. Business Intelligence and AI
Decision Intelligence is not the same as Business Intelligence (BI). BI is classified as 'context-free': It presents information to decision-makers without being directly aimed at decision support or automation. Decision Intelligence, however, uses AI-powered data processing and a 'single analytical view' to directly support decisions or – with suitable processes – to automate them.
The difference from AI is also clear: AI is a field of computer science. Decision Intelligence specifically uses AI to build a trustworthy data foundation and to develop models and analyses for concrete decisions based on it.
Conclusion
Decision Intelligence combines data integration, AI analytics, and a decision-oriented lifecycle including monitoring. This makes decisions more comprehensible, faster, and – for recurring process steps – more automatable. Two prerequisites are crucial: a trustworthy data foundation and integration into existing IT landscapes instead of a complete system replacement.