6 common data quality issues and how to fix them

Data quality issues don’t always start in the dashboard or the model. A dropped column, a partial sync, or a stale record can enter the pipeline at any stage between the source system and the warehouse, then degrade every report and AI model built on the affected tables. In fact, Gartner estimates that companies will discontinue 60% of AI projects because they lack AI-ready data.

Most data quality failures occur during data movement, when connectors mishandle syncs or schema changes. Here’s a breakdown of the common data quality failures, what each one does to your reports and models, and how Fivetran prevents them.

Types of data quality issues and how they show up in your pipeline

Data quality is the degree to which data is accurate, complete, consistent, and timely enough for its intended use. Any flaw that makes a data set unfit for reliable analysis, reporting, or AI modeling counts as a data quality issue. These issues could be duplicated records, missing fields, conflicting formats, or stale data arriving hours late. These problems often show up in dashboards and models long after the original failure occurred during data movement.

The challenge for data engineering teams is connecting visible symptoms to the pipeline stage where the breakdown actually happened. A broken metric might trace back to manual data entry errors at the source, while a sudden spike in null values could originate in the loading stage when connectors fail to handle API rate limits, schema changes, or network interruptions.

When you conduct a root cause analysis of recurring data quality failures, most of them point to one of six common pipeline stage problems.

Data quality software and data observability platforms help teams catch common data quality issues after data enters the warehouse. Automated quality checks on warehouse tables alert when values fall outside expected ranges or when row counts drop unexpectedly between syncs.

Data profiling and data validation rules add another line of defense by flagging records with invalid values, incorrect data types, or missing required fields. But these visible issues rarely reveal the root cause: An upstream pipeline failure that corrupted, duplicated, or dropped the data before it ever reached validation. Duplicate data in a warehouse table may trace back to a connector that lacks idempotent loading, and missing records may be coming from a sync that failed without triggering an alert and never retried.

Fixing the symptom in the warehouse with data quality tools that run deduplication queries or null-filling transformations addresses the immediate concern. But it doesn’t eliminate the underlying cause. Unless the pipeline handles retries, schema propagation, and sync state correctly, the same failure will recur on the next sync.

Teams that build custom connectors without production-grade infrastructure often find these recurring failure modes only after incorrect or incomplete data reaches production. And once bad data spreads, the cost of cleanup multiplies, because every bad record propagates across the dependent tables and reports built on the affected source. This makes remediation far more expensive than preventing the problem at the loading stage.

How Fivetran helps prevent pipeline-related data quality issues

Some data quality issues can’t be fixed in the pipeline. A misspelled address in a CRM or a misconfigured pricing rule in an ERP will still produce bad records no matter how the data moves. These source-system errors require fixes at the application level, not downstream.

But many other concerns are preventable, and they start in the part of the pipeline your data engineering team controls: the loading step. When connectors lack automatic retries, schema detection, and deduplication, they let upstream errors pass straight into the warehouse on every sync. Over time, those small failures accumulate into recurring data quality problems that surface long after the original pipeline error.

Fivetran operates at the data movement stage and addresses the most common ingestion-related failures directly. With more than 750 pre-built connectors, the platform automates data integration from SaaS apps, databases, ERPs, and file systems into warehouses and data lakes. 

Fivetran prevents the most common ingestion-related quality failures through:

• Idempotent replication: Fivetran applies each sync result exactly once, even after retries or interruptions. Duplicate records caused by rerunning the same batch don’t reach the destination, removing one of the most frequent sources of inflated metrics.
• Automatic schema change handling: When a source system adds, removes, or renames a column, Fivetran detects the change and propagates the updated schema to the destination.
• Retry and re-sync behavior: If a sync fails due to network timeout or an API rate limit, Fivetran retries automatically and resumes from the last successful checkpoint. Partial loads aren’t marked complete, preventing the incomplete data set problem that leads to biased models and undercounted metrics.
• Metadata and sync logs for investigation: Fivetran’s platform connector syncs metadata and row-level operation logs directly into the destination, along with timestamps for every completed sync. Your data engineering team can query these logs to check when a specific record was last updated and whether a sync completed fully.

Preventing quality failures at the data movement stage reduces pipeline debugging and the manual data cleaning that delays every project built on warehouse data.

Okta’s data engineering team spent months building custom pipelines that left other departments waiting for data access. After adopting Fivetran’s managed pipelines, Okta saved over 1,000 engineering hours and redirected that time toward data governance and removing data silos across the organization.

Start a free trial with Fivetran to see how automated, governed data movement keeps warehouse data accurate from the first sync.

FAQ

What are the dimensions of data quality?

The most widely referenced data quality dimensions include accuracy, completeness, consistency, timeliness, uniqueness, and validity. Each dimension measures a different property of whether a data set meets the requirements of its intended analytical or operational use.

What causes data quality issues?

Data quality issues result from manual data entry errors, source-system changes, poorly configured integrations, and missing data validation rules. As organizations connect more data sources with varying formats and update schedules, the risk of inconsistencies and incomplete records increases with every new data pipeline.

How can teams prevent data quality issues from reaching analytics and AI?

Teams can prevent quality failures by automating data movement with managed connectors that handle retries and schema changes at the loading stage while deduplicating records automatically. Pairing automated ingestion with data quality software that runs validation checks on warehouse tables helps teams catch problems that originate outside the pipeline, like source-system inaccuracies and business-rule violations.

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