Not always. In the beginning, you can implement DQC using simple SQL rules, dbt tests, or data warehouse procedures. As your data scale grows, it is highly recommended to invest in dedicated Data Observability tools.
Data Quality Control (DQC) is the operational process of detecting, blocking, and correcting data errors before they enter target systems and business reports. While Data Quality Management sets the theoretical rules, DQC executes physical procedures, such as quarantining invalid records or automatically standardizing text formats. Implementing an effective DQC framework prevents organizations from making strategic decisions based on corrupted or inaccurate data.
Key takeaways
- Unlike general data quality management, DQC actively executes physical procedures to detect, block, and fix data errors in real-time.
- Effective DQC operates at the point of entry (source prevention), during data transformations (in-stream), and before final reporting (output).
- Instead of freezing the entire pipeline for minor errors, modern DQC uses data quarantine (Dead Letter Queues) to isolate flawed records while healthy data flows continuously.
- DQC acts as a critical firewall, protecting AI/ML models, regulatory compliance efforts, and strategic management reports from the risks of “garbage in, garbage out.”
Data quality control vs. management, dimensions, and checks
At Murdio, we often see businesses confusing Data Quality Control with other data governance stages. To build a reliable data pipeline, you must understand the exact role of each component. Here is how these concepts work together in practice:
| Concept | Role in the process | Practical business example |
| Data Quality Dimensions | Theoretical criteria for data health. | Defining that every corporate client must have a valid VAT number. |
| Data Quality Assessment | A one-time or periodic audit of the current state. | A report stating: “20% of our CRM records are duplicates.” |
| Data Quality Checks | The technical measurement tool or rule. | An SQL script validating if the email address contains an “@” symbol. |
| Data Quality Control (DQC) | The operational reaction to an error. | Blocking a form submission missing a VAT number and alerting the admin. |
How it works – data quality control mechanisms in data pipelines
Effective quality control operates like a system of locks in a data pipeline. We implement this process across three key checkpoints (gates) within our clients’ environments:
- Entry Control (Source Prevention): Data validation occurs at the exact moment of entry, such as within a CRM form or an API endpoint. The system immediately rejects erroneous data before it ever reaches the database.
- In-stream Control (On-the-fly Validation): This control happens during ETL/ELT processes (e.g., within dbt or Snowflake). At this stage, automated scripts execute Data Quality Checks to validate the data as it transforms.
- Output Control (Final Gate): The system verifies aggregated data right before displaying it on business dashboards like Power BI or Tableau. This final check prevents stakeholders from seeing or using “dirty” data in their reports.
Practical applications – error handling strategies
When a quality check detects an anomaly, the system must trigger a specific reaction.
“From our experience at Murdio, the hardest part of DQC isn’t finding the error, but making the business decision on what to do with it. Stopping an entire data pipeline because of a single typo is simply a bad strategy.” – XXX, Job Title at Murdio.
We typically apply four main error-handling strategies tailored to the severity of the issue:
1. Data Quarantine
The system routes invalid records (e.g., transactions missing a payment amount) to a separate Dead Letter Queue table. This ensures that the flow of healthy data continues to process without interruption or performance bottlenecks, while flawed records are safely stored for later investigation by data stewards.
2. Auto-Correction
DQC scripts automatically fix minor, predictable defects on the fly. Examples include trimming unnecessary spaces, standardizing text case (e.g., converting “london” to “London”), or aligning date formats. This eliminates manual cleansing for trivial errors and speeds up data availability.
3. Graceful Degradation
The system allows a slightly incomplete record to pass but explicitly tags it with a “Data Warning” flag. This ensures downstream data analysts are fully aware of the missing context and can adjust their reports accordingly, preventing silent failures without breaking the pipeline.
4. Hard Stop
This strict blockade is reserved for highly critical information, such as financial data regulated by compliance laws or severe security breaches. To prevent corrupted data from spreading, the entire pipeline halts instantly and alerts the team, remaining paused until a data engineer manually resolves the underlying issue.
What are the core benefits of implementing data quality control?
- Drastic reduction in manual work: Data engineers no longer need to manually clean and fix databases at the end of every month.
- AI/ML model protection: DQC acts as a firewall that blocks “garbage” data from entering analytical models. For example, by establishing a centralized golden source, we successfully helped in strengthening AI governance for a global bank, drastically reducing their regulatory risk.
- Reliable management reports: Business leaders eliminate the risk of making strategic financial decisions based on duplicated or outdated records.
- Regulatory Compliance: DQC enforces the strict data consistency required by frameworks like GDPR, HIPAA, or DORA.
How to design and implement a data quality control process?
Building a DQC framework is not about buying a new software tool; it is about establishing a robust operational process. To implement it correctly, you need a clear strategy that aligns technical capabilities with your business goals.
Key questions to answer before implementation
Before writing a single line of validation code, your data governance team must answer three critical questions:
- What are our Critical Data Elements (CDEs)? You cannot control everything. Identify the specific data points that directly impact revenue, compliance, or customer experience.
- Where is the best interception point? Decide if the error should be blocked directly at the source (like a CRM input form) or caught later during the transformation phase (ETL).
- Who owns the resolution process? When an invalid record hits the quarantine table, there must be a designated Data Steward responsible for reviewing and fixing it.
What are the common mistakes in DQC implementation?
At Murdio, we regularly audit failing data pipelines. Here are the most frequent pitfalls we observe during DQC implementation and how to avoid them:
| Common Mistake | The Consequence | Murdio’s Recommended Solution |
| “Boiling the ocean” | Trying to validate 100% of data columns exhausts the budget and slows down system performance. | Focus exclusively on Critical Data Elements (CDEs) first. Scale to other data later. |
| IT owning business rules | Data engineers guess what a “correct” record looks like, leading to false alerts. | Business Data Owners define the requirementsrules (RulesDimensions); IT translates them into code (Checks). |
| Overusing “Hard Stops” | The entire data warehouse freezes and stops refreshing because of one empty, non-essential column. | Implement a Quarantine (Dead Letter Queue) for minor errors to keep the main pipeline flowing. |
| Lack of feedback loops | The same errors are corrected in the data pipeline every day, but the root cause is never fixed. | Use DQC metrics to force changes at the source system (e.g., adding a mandatory field in a broken CRM form). |
Summary
Data Quality Control is fundamentally different from theoretical data dimensions or one-time data assessments. It is a continuous, operational defense process that stands between your raw data and your business intelligence. By utilizing automated checks, DQC decides in real-time whether to pass, correct, or block incoming information before it can cause operational damage. Implementing a robust DQC framework transforms passive error observation into active data health management, directly protecting your critical business decisions, regulatory compliance, and bottom line.
If your organization is struggling with poor data quality, broken pipelines, or inefficient governance workflows, our team is ready to help. Our Technical Implementation Teams and Collibra experts can design, configure, and scale a reliable DQC process tailored to your business needs. Partner with Murdio to turn your data into a trusted, strategic asset.
Frequently asked questions (FAQ)
A well-designed data quality control framework does not block system performance. By using quarantine architecture (Dead Letter Queues), erroneous data is isolated, allowing the main data stream to flow seamlessly.
You should start by identifying your Critical Data Elements (CDEs). Controlling every single data point is expensive and inefficient. Choose 5-10 highly sensitive columns (like client tax IDs or payment statuses) and build your first DQC gates there. You can read more about this approach in our case study on managing and cataloging sensitive CDEs in a Swiss private bank.
