Validio is named a Representative Vendor in the 2026 Gartner® Market Guide for Data Observability Tools - one of only eight vendors covering all five observation categories. Here's what the report tells us about where the market is heading.

What the 2026 Gartner® Market Guide for Data Observability tells us about where the market is heading - and where it still needs to go

"Data quality is a key reason why up to 95% of AI projects fail."

Legacy data quality wasn't built for AI. Discover why rule-based, reactive approaches break down when models depend on your data — and what agentic data quality does differently.

Legacy vs. agentic data quality: why 95% of AI projects fail

Validio, the agentic enterprise data management platform, has raised $30 million in Series A funding led by Plural.

Validio raises $30M Series A to fix enterprise data quality for the AI era

The Validio 7.5 release brings AI recommendations to accelerate setup, Referential Integrity Validator and expands our integration ecosystem with ServiceNow and Amazon Bedrock support.

Validio 7.5: AI recommendations and integrations to ServiceNow, and Amazon Bedrock

Validio 7.5: AI recommendations and integrations to ServiceNow and Amazon Bedrock

Referential integrity ensures reliable data relationships. Learn why it breaks, how it impacts reporting, and how Validio automatically validates it.

Referential integrity: the backbone of data

Why referential integrity is the backbone of trustworthy data

Game Lounge lives and breathes data and depend on reliable data for efficient operations. By implementing Validio, they managed to turn their data management from reactive to proactive.

Building real-time data reliability in affiliate marketing

From reactive to proactive: building real-time data reliability in affiliate marketing

Connect data quality and BI with the new Validio × Omni integration. Monitor dashboards, trace lineage, and ensure trusted insights in every report.

Validio × Omni BI: Bringing data quality and observability to your dashboards

Validio × Omni: Bringing data quality and observability to AI and BI

Discover how Validio’s new MCP server connects AI assistants like Claude with your data stack to automate data quality monitoring and root-cause analysis.

Of course, we built an MCP server for data quality

Validio’s model retraining uses feedback on false positives and negatives to refine anomaly detection, boost precision, and minimize alert fatigue.

Validio 7.0: Smarter anomaly detection with AI feedback and retraining

Detect anomalies anywhere in your data, in real time

Get to the root cause and resolve issues quickly

Discover data assets and understand how they are used

The latest whitepapers, reports and guides

AllianceBernstein drives data trust and accurate reporting

Sophia Granfors

Marketing Manager

Referential integrity is what keeps your data relationships logical and reliable. When it fails, you end up with orphaned records and mismatched reporting. Here is why it happens, and how Validio’s new functionality lets you validate it without writing custom SQL.

Data is rarely isolated. It is distributed across dozens of tables, schemas, and platforms. Referential integrity is about ensuring that the relationships between these datasets make sense.

While metrics like freshness or null values are about individual fields, referential integrity is about the structure of your data model. If the relationship between tables is broken, you will produce inaccurate results, regardless of how "clean" the individual rows are.

Referential integrity ensures that the relationships between your data tables remain consistent. It enforces the rule that a "foreign key" in one table must point to a valid, existing "primary key" in another table.

If an order record references customer_id: 123, referential integrity is important to ensure that Customer 123 actually exists in your customers table. If that customer is missing, the order is "orphaned": it has no parent, and queries linking these tables will yield incomplete data.

Integrity failure examples and their impact

Referential integrity issues are often silent and go unnoticed until someone investigates a discrepancy, but the downstream impact can be large:

Inaccurate reporting: Inner joins automatically drop orphaned records. You might have 1,000 sales in your source system, but if 50 of them link to missing product IDs, your BI tool will only report 950.

Application errors: Operational applications may fail or throw exceptions when attempting to fetch details for a reference that doesn't exist.

Inconsistent metrics: Different stakeholders may report different totals depending on whether they are counting raw rows or joining across tables.

Referential integrity issues aren't always caused by bad database design, but are instead often a side effect of distributed data pipelines. Some example cases when referential integrity issues happen are:

Hard deletes: A GDPR request comes in to delete a user. You delete the user from the users table, but the deletion logic fails to cascade to the subscriptions table. You are left with transaction records pointing to a user that no longer exists.

Pipeline timing: If your orders table updates at 8:00 AM, but your reference payments table doesn't update until 8:30 AM, a validation check at 8:15 AM will flag thousands of records as missing their reference data, even though it’s just a latency issue.

Migration gaps: Moving data from legacy systems to a cloud warehouse (like Snowflake or Databricks) can sever dependencies, leaving you with historical records pointing to entities that weren't migrated.

The challenge with referential integrity validation

In transactional databases, you can rely on database constraints (FOREIGN KEY) to block bad data. However, in analytical warehouses like Snowflake or BigQuery, these constraints are generally not enforced to prioritize write performance.

This forces data teams to write custom SQL monitors to find orphans, like

SELECT count(*) 
FROM orders o 
LEFT JOIN customers c ON o.customer_id = c.id 
WHERE c.id IS NULL 

While this works, maintaining custom SQL for every relationship in your warehouse is hard to scale and creates clutter in your observability setup.

Validating referential integrity in Validio

Validio provides native support for referential integrity validation to remove the need for custom SQL scripts. You can configure these checks directly in the Validio UI with the referential integrity validator (or, as always with the Validio IaC).

Select the source: Choose the child table (e.g., order_table).

Select the reference: Choose the parent table (e.g., customer_table).

Map the keys: Select the source field and reference source field.

Validio generates the query to detect orphaned records automatically.

In addition to the simple set up, you also get all the other validation functionality in Validio, including segmentation and windowing. This means that you can directly validate integrity by dimensions like market or region, with just one validator. Maybe referential integrity is perfect in Norway but broken in the UK. 

This helps not only detecting referential integrity issues, but also understand what's causing them - ultimately reducing the time and effort in solving issues.

So now we've detected a mismatch between the primary source (the order_table) and the reference source (the customer_table). Great, but we also want to understand what caused the issue so we can fix it.

By segmenting the data, we can directly understand in, for example, which country the issue happened. By generating a debug query, we can get a sample of records causing issues and understand wether they happened because of a value mismatch, a missing value, or another issue.

To set up your first referential integrity validator, check out the documentation for more details.

Curious about automating your data observability?

The Validio blog covers data observability, data quality monitoring, anomaly detection, data lineage, and AI data reliability - for data engineers and data teams.

Stay on top of your data and metrics with automated monitoring, to boost data team productivity and make your data AI-ready.

Why referential integrity is the backbone of trustworthy data

Discover the latest news and data trends

What the 2026 Gartner® Market Guide for Data Observability tells us about where the market is heading - and where it still needs to go

Legacy vs. agentic data quality: why 95% of AI projects fail

Validio raises $30M Series A to fix enterprise data quality for the AI era

Validio 7.5: AI recommendations and integrations to ServiceNow and Amazon Bedrock

Why referential integrity is the backbone of trustworthy data

From reactive to proactive: building real-time data reliability in affiliate marketing

Validio × Omni: Bringing data quality and observability to AI and BI

Of course, we built an MCP server

Validio 7.0: Smarter anomaly detection with AI feedback and retraining