Data Quality Rules That Actually Work (Part 2)
How to Implement, Enforce, and Manage Exceptions
In last week’s article, Data Quality Rules That Actually Work (Part 1), we focused on how to design business-aligned data quality rules that actually stick. We broke down what separates enforceable rules from vague preferences and gave you a framework (“IF / THEN / BECAUSE”) for connecting each rule to a meaningful business outcome. Rule design, we said, should begin with business pain, not just schema reviews.
This week, we’re moving from design to execution. Because great rules don’t matter if they live in a PDF. You have to implement them in the right place, enforce them without wrecking your processes, and handle exceptions in a way that builds trust. Let’s talk about how to make your rules real.
Where Should Rules Live?
Not every rule belongs in the same layer of your stack. Some need to fire at the UI. Others belong deep in the warehouse. You have to match each rule to the point in the data lifecycle where it makes the most sense to apply it.
Here’s a quick cheat sheet to help you decide:
| Rule Type | Best Fit For | Example |
|---|---|---|
| Completeness | UI, APIs, ETL staging layers | A product without a description is rejected at point of entry |
| Consistency | ETL layer, data quality platform, stored procs | If Country = US, then State must not be null |
| Valid Values | Lookup tables, dropdowns, reference tables | Payment Term must exist in the ValidPaymentTerms table |
| Uniqueness | Primary keys, match/merge logic | Prevent duplicate customers by email and region |
| Timeliness | Scheduled audits, dashboards | Last activity date must be within 90 days |
Think of this like plumbing: install your valves where the pressure hits hardest.
How to Enforce Rules
Rule enforcement doesn’t have to be a one-size-fits-all approach. Here are four practical strategies, each with its own use case:
1. Hard Enforcement (Strict Blocking)
Use this when bad data will immediately cause downstream failures.
Example: Reject a supplier record if the W-9 form is missing.
How to implement it:
- UI validation
- SQL
NOT NULL - Stored procedures that prevent insert/update
Outcome: Data doesn’t enter the system until it passes the rule. No gray areas.
2. Soft Enforcement (Warnings and Flags)
Use this when you need visibility but don’t want to block the data flow.
Example: Billing address looks suspicious, but you don’t want to delay onboarding.
How to implement it:
- Log violations to a centralized “data exceptions” table
- Send alerts to stewards
- Create an “exception reason” field users can fill out
Outcome: The data flows through, but someone is now responsible for reviewing it.
3. Scheduled Audits
Use this for rules that don’t need to fire in real-time.
Example: Vendors missing insurance documentation should be flagged monthly.
How to implement it:
- SQL jobs or data profiling scripts
- Exception logs
- Dashboard feeds
Outcome: Stewards receive a clean list of records to review on a regular cadence.
4. Dashboard Monitoring
Use this when you want to track trends and patterns, not just one-off violations.
Example: Track what percentage of new customers have valid region codes.
How to implement it:
- Power BI, Tableau, or other visualization tools
- Exception scans feeding dashboards
Outcome: Business and governance teams can monitor progress and spot regressions.
Real-World Enforcement Examples
Let’s bring those strategies to life with real examples:
- Block in UI Rule: Account name cannot be blank System: Customer portal Action: Form validation stops submission
- Flag in Data Pipeline Rule: If Customer_Tier = Enterprise, then SLA contract must be present System: SSIS or ADF Action: Row logged to an Exception_Log table and an email sent to the steward
- Audit in Batch Rule: No orders allowed without a customer email System: Nightly SQL Server job Action: Query runs to detect issues and create a resolution queue
Each approach fits a different operational need, and the best programs use a mix of all four.
Managing Exceptions
Let’s be honest. Every rule will be broken at some point, and sometimes, it’s the right call.
That’s why you need a framework to manage exceptions that doesn’t involve chaotic emails or mystery workarounds.
1. Create an Exception Registry
This is a structured way to track every override:
| Field | Value |
|---|---|
| Rule ID | DQ-CUST-004 |
| Entity | Customer |
| Exception Type | Manual Override |
| Approved By | jane.steward@example.com |
| Expiration Date | 2025-12-31 |
No more shadow approvals. Every decision is visible and traceable.
2. Set Automated Expiration
Every exception should have a shelf life. It’s too easy to let temporary exceptions become permanent liabilities.
3. Tag the Root Cause
Whenever an exception is logged, tag it with a reason:
- Training issue
- Process gap
- Legacy system constraint
- Business logic not yet implemented
This gives you an improvement roadmap as well as a way to prioritize fixes over time.
Make Rules Visible and Accountable
Data quality rules are more than code. They are policy decisions that impact the business. Treat them that way.
Here’s how to elevate them:
- Store rules in a searchable, shareable repository
- Assign a named owner and steward to each rule
- Include version history as rules evolve
- Link rules to the fields, entities, and systems they affect
- Publish dashboards that track rule violations and trends
The more visible the rule, the more likely it is to be followed.
Avoiding Common Pitfalls
Here are three traps to watch for and how to sidestep them:
- Don’t hardcode every rule deep in your pipelines Do centralize logic so it’s visible, flexible, and easier to audit
- Don’t manage violations in spreadsheets Do use exception tables and alerting systems
- Don’t enforce rules without context Do show the “why” behind the rule so people understand the value
Final Thought
Writing data quality rules is the easy part. The hard part, the part that actually creates value, is what comes next.
You have to implement them where they matter, monitor them over time, and give your team the tools and structure to manage exceptions without losing momentum.
Start small. Pick one rule that matters. Make it visible. Measure it. Improve it. Then move to the next one.
That’s how a real governance program grows.
