Decision tables: practical guide

Published on: 2024-08-10 18:36:09

Decision tables express decision logic in a compact grid. They replace large decision trees with a format that is easier to read, test, and audit.

A decision table is a matrix. The condition part lists inputs and allowed values. The decision part lists outcomes or actions. Each row is a rule. When the listed conditions are true, the platform returns the stated decision.

If you prefer the decision tree analogy, conditions drive the branches. The decision cell is the leaf at the end of the path.

How to design a decision table

You can define a table in different ways. These guidelines keep the logic clear and repeatable.

  1. Make sure all conditions are mutually exclusive and collectively exhaustive. This means the table covers all relevant combinations, and overlapping conditions do not create ambiguous matches.
  2. Make sure the decisions are mutually exclusive. Only one decision should be returned for each set of conditions.
  3. Make sure the decisions are collectively exhaustive. Every valid input should map to a defined outcome.
  4. Make sure the decisions are consistent. The same inputs must always produce the same decision, regardless of how columns are arranged.
  5. Make sure the conditions are listed in order of precedence. List them in the order the engine evaluates them.

Following these guidelines gives you a well-defined, testable decision table. In some cases, you cannot meet every goal at once. Document the trade-offs, and state the intent next to the table or in version notes.

Why a catch-all row is important

Sometimes you need a catch-all row in a decision table. List this row first in the table, and give it a safe default outcome for any combinations not covered by other rows. Use it to handle nulls, unexpected values, and future categories you have not modeled yet.

How to test a decision table

Testing a decision table should cover boundary values, standard paths, error paths, and interactions between conditions. Aim for pairwise coverage at minimum, and add boundary and negative tests where risk is higher.

This can take time. Generate test cases with synthetic data to speed up coverage, reproduce edge cases, and keep audits simple.

What is the difference between a rule set and a decision table

Rule sets are a collection of if-then-else rules, often evaluated in sequence. Decision tables are a grid that maps condition combinations to a single outcome. Both express business rules, but they serve different needs.

Use rule sets when order, side effects, or orchestration steps matter. Use decision tables when you need transparent mapping across many combinations, fast gap analysis, and easy auditability.

What are the use cases for decision tables

  • Business Process Automation: Route tasks, approve steps, and set SLAs based on defined inputs like customer tier, channel, and state.
  • Risk Management: Evaluate underwriting, fraud, and KYC outcomes by scoring signals and mapping ranges to clear actions.
  • Software Development: Encode business rules as decision logic to handle scenarios consistently across services and environments.
  • Healthcare: Support protocol selection using patient data such as age, history, vitals, and lab results.
  • Telecommunications: Choose throttling, rerouting, or alerts based on network metrics and customer plans.
  • Supply Chain Management: Pick carriers, reorder points, or routing rules by inventory levels, lead times, and cost targets.
  • Pricing: Define pricing tiers, discounts, and eligibility rules across products, features, and contract terms.
  • Customer Service Call Center: Segment and prioritize by profile, value, and urgency so agents focus on the right queue first.

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