CDQ Rule Logic and Dictionary: Why They’re Essential in Real Data Projects

When you’re working with Cloud Data Quality (CDQ), it’s easy to assume the job is done once you've profiled the data—that is, identified what’s missing or wrong. But identifying issues is only half the battle; the real power lies in automatically fixing inconsistent data using two built‑in CDQ assets: Rule Logic and Dictionary.

If you're working in real-world data implementations, these two tools are what move you beyond reporting errors and into cleaning and validating them at scale.

What Is Rule Logic?

Rule Logic is how CDQ applies business rules automatically to incoming records. Instead of manually checking every row, you define a rule once and let CDQ do the work.

Example rule:

• IF status = "Active" AND country = "USA" → mark as Valid

• ELSE → mark as Invalid

Once defined, this rule runs consistently across every record that passes through the pipeline. No need to repeat logic in every mapping or script.

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What Does the Dictionary Asset Do?

A Dictionary is a standardized reference for known values—anything outside that list gets normalized before logic is applied.

Imagine country codes in your data are inconsistent:

• “US”, “United States”, “America”, “USA”, “U.S.A.”

Your dictionary defines these as a single standardized output:

US, U.S.A, America, United States → USA

This mapping ensures your rule logic only needs to check for “USA”, regardless of how the country was originally entered.

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How They Work Together

These two assets operate in a sequence:

1. Dictionary cleans the data by standardizing values.

2. Rule Logic then evaluates those clean values against business rules to validate or flag records.

Together, they form a robust, repeatable system that automatically normalizes and validates data — without manual intervention.

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Real-World Scenario

Scenario: You’re handling customer data with status field variations:
“Active”, “active”, “ACT”, “In Progress”, “pending”, etc.

Solution steps:

• Use the dictionary to map all variations to “Active” or “Inactive”

• Define rule logic:
  IF status = “Active” AND country = “USA” → Valid
  ELSE → Review or cleanse

This approach handles chaos at the source and ensures your data is clean and consistent for downstream analysis or reporting.

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Why It Matters

In real CDQ delivery projects, these assets bring:

• Predictability — the same logic applies every time

• Reusability — use the same dictionary or logic across multiple mappings

• Scale — it works reliably across millions of rows

• Efficiency — no repeated manual cleanup

Without them, you risk inconsistent outputs, manual rework, and unreliable data quality.

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Implementing This in CDQ Flow

A simple pipeline flow might be:

1. Raw data enters CDQ

2. Dictionary cleans key fields (e.g., status, country)

3. Rule Logic validates the standardized fields

4. Records marked valid move forward; others get flagged

This sequence ensures that every downstream process works with clean, trustworthy data.

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Key Takeaway

Profiling might help you see where the issues are—but Rule Logic and Dictionary are how you fix them.

These two CDQ assets are foundational—not advanced extras. They define what’s valid, remove inconsistency, and apply rules automatically across all records. Use them correctly, and your CDQ project becomes scalable, reliable, and truly automated.

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Contextual Recommendation:

For hands-on walkthroughs, real project use-cases, and deeper understanding of how to implement Rule Logic and Dictionary together, this is a core topic in cloud data quality training.

This is a core topic we cover in our Informatica CDQ Training using hands-on, real project flows.