Data dictionaries are structured repositories of standardized values like vehicle types, industry codes, or geographic zones. They are fundamental building blocks in any business rules engine (BRE). For industries such as insurance and finance, where consistency and control over business inputs is paramount, data dictionaries make rule logic cleaner, faster, and easier to govern.
This article explores what data dictionaries are, how they fit into decision automation, and how the Higson business rules engine uniquely supports their use at scale.
What Are Data Dictionaries?
In the context of business rules engines, a data dictionary is a centralized collection of reference data used in rule logic. These are not transactional values but standardized, controlled vocabularies that feed into decisions. For example:
- Vehicle classification codes (used for underwriting and pricing)
- PKD codes (industry classification for business risk segmentation)
- Address dictionaries (used for territorial logic or regional risk factors)
These dictionaries ensure that rule authors use consistent values across the organization. When integrated into decision logic, they reduce errors, simplify rule maintenance, and enable non-technical users to build or modify logic confidently.
The Role of Data Dictionaries in Decision Automation
Data dictionaries are crucial for scaling rule logic across large and complex organizations. Here are some reasons why:
1. Consistency and Reusability
Data dictionaries allow companies to define once and use everywhere. A well-managed list of vehicle classes or client types, for instance, can be reused in pricing, eligibility, and fraud detection rules.
2. Faster Rule Creation and Updates
Rather than hardcoding values into multiple decision tables, rule authors can reference a dictionary. Updates to values (e.g., adding a new vehicle category) can be done centrally, without having to touch every rule.
3. Better Governance and Auditability
With centralized data dictionaries, compliance teams can trace how reference data is used across systems. This improves audit readiness and simplifies regulatory reporting.
4. Business Empowerment
In systems like Higson, where business users can update logic without IT involvement, data dictionaries make it easier for non-developers to manage rules based on approved, pre-defined values.
How Higson Implements Data Dictionaries
Higson is built to support high-scale, low-latency rule execution across domains like insurance, banking, and telecom.
Here's how it handles data dictionaries:
Domain Model Reflection
Higson allows organizations to mirror their domain structure in the logic layer. Data dictionaries such as vehicle types or policy segments are part of this model, making them natively available to rule authors in the Studio interface.
Efficient Data Loading and Memory Use
Thanks to recent optimizations (v4.1.0), Higson supports large in-memory dictionaries — even those with 1 million+ entries — without excessive memory usage. Benchmarks show up to 47% memory reduction compared to earlier versions, making it ideal for reference-heavy industries.
Import/Export via CSV/XML
Business and technical teams can import or export dictionary data in simple formats like CSV or XML, ensuring compatibility with legacy systems and external databases.
Versioning and Change Management
Every change to a dictionary can be versioned, tracked, and scheduled for deployment. This ensures stability and traceability when reference data evolves over time.
Conclusion: Why It Matters
In modern business environments, automation is only as good as the data it runs on. Data dictionaries are the invisible foundation that make logic understandable, scalable, and safe. In Higson, their implementation is not an afterthought. It's a core design feature that empowers users, improves performance, and ensures decision accuracy.