“Garbage in, garbage out” (GIGO) is more than a cliché—it’s a daily reality for teams working with complex data pipelines. Poor data quality leads to flawed reports, misinformed decisions, and a serious loss of trust in analytics.
But what if your data pipeline could learn from its mistakes?
With Datagaps DataOps Suite, data reconciliation becomes more than just a mismatch detector. It evolves into a continuous feedback loop that drives the automatic creation of custom data quality rules, improves your data quality scores, and prevents future errors—turning every mismatch into a smarter rule.
Close the Loop: Reconciliation to Rule Creation to Results
Traditional reconciliation stops after finding mismatches. But what if every discrepancy could teach your system to improve?
With DataOps Suite, reconciliation is the starting point—not the end. Here’s how the feedback loop works:
- Reconcile Data between systems like Snowflake and Databricks.
- Identify Issues like missing values, format inconsistencies, or delayed updates.
- Generate Targeted Rules that detect and fix the root causes.
- Improve Data Quality Scores using these new rules.
- Reduce Future Mismatches, making pipelines smarter with every run.
- Repeat the Cycle, driving continuous quality improvements.
This loop transforms your data pipeline into a self-healing system.
From Mismatches to Rules (Automatically)
Let’s walk through a real-world example:
You run a reconciliation between Snowflake and Databricks and find customer ZIP codes missing in one system.
Using that insight, you create a custom rule:
“ZIP code must be 5 digits and not null.”
You deploy it in the pipeline, and on the next run, the bad records are automatically flagged.
Result? Your data quality score jumps from 75.71% to 89.53%. Fewer errors, better trust.
That’s the loop in action. And you don’t need to be a SQL expert to make it happen.
Rule Creation Made Simple (Even with AI)
DataOps Suite includes a powerful set of tools to define and deploy data quality rules:
- No-code rule builders (SQL, Duplicate Check, Attribute Check)
- Clone and reuse existing rules
- Assign rules to dimensions like Accuracy, Completeness, Validity, and more
- Set severity levels and success thresholds
- Filter, test, and preview output instantly
And with OpenAI integration, just describe your issue in plain English, and the Suite generates the rule for you.
Prompt: “Find duplicate records with the same email but different customer IDs.”
Result: Auto-generated SQL rule, ready to deploy.
Here is a screenshot of how a SQL query rule looks like
Track Your Data Quality Over Time
Every rule you apply contributes to a Data Quality Score—giving you quantifiable insight into how well your data is performing.
Use the Data Quality Dashboard to:
- View pass/fail status per rule
- Monitor good vs. bad record counts
- Filter results by dimension or severity
- Track improvements over time
These scores give you a data-driven way to manage data trust across your organization.
More Than Just Data Compare
Beyond basic data reconciliation, the DataOps Suite supports:
- Metadata Compare – Ensure schemas match
- Metrics Comparison – Validate aggregates and KPIs
- Multiple Data Compare – Reconcile across multiple datasets and systems
Each type of reconciliation can lead to new DQ rules and better quality pipelines.
Transform Reconciliation into Results
Most platforms stop at pointing out problems. The DataOps Suite solves them—automatically.
With this continuous feedback loop:
- Every mismatch becomes a teachable moment
- Every rule strengthens your pipeline
- Every run builds trust in your analytics
Your data pipeline gets smarter, cleaner, and more reliable—with less manual effort.
Ready to Close the Loop?
Reconciliation isn’t just about catching errors. It’s about learning from them to build a better, more intelligent data ecosystem.
Transform Your Data Ecosystem
Reconciliation isn’t just about catching errors. It’s about learning from them to create a smarter, more efficient data ecosystem With Datagaps DataOps Suite, every run is a step toward improvement.
