Validio 5.0 adds BI tool extensions, enhanced segmentation filtering, and improved windowing, boosting data observability in your analytics workflow.

Validio 5.0: BI monitoring and improved search and filtering

Unlock the power of unstructured data validation with Validio. Learn how to leverage LLMs within your data warehouse to detect anomalies, standardize text, flag PII, and elevate master data management, securely and at scale.

Bringing data quality to unstructured data with LLMs and Custom SQL

Discover why data quality ownership is one of the biggest unresolved challenges in modern data teams. Learn what’s holding organizations back, and what successful ones do differently to drive real impact with AI and analytics.

One of the biggest problems with solving data quality: No one wants to own it.

Learn how Validio's new Adaption Rate setting for Dynamic Thresholds gives you more control over how quickly anomaly detection responds to changes in your data.

Adapting fast & slow: tailoring dynamic thresholds with adaption rate

Valido 4.6: new integrations and algorithm updates

Stepping into a data leadership role? Learn key strategies for the first 90 days to establish your team as a value center and drive business impact.

First 90 days as a data leader: Establishing data teams as value centres

Prevent stale dashboards and hidden data errors with data observability. Learn how automated detection, incident management, and data lineage help teams maintain trust in data-driven decisions.

Sneaky data quality issues, chaotic aftermath

In this article, we’ll explore Looker’s alerting limitations and how Validio fills the gaps—so you can move beyond just dashboards and into proactive, intelligent data monitoring.

Amplifying Looker dashboards with machine learning based data observability alerts

When dashboards aren't enough: amplifying Looker with machine learning based alerts

The first step to any successful data observability initiative is to set up the right validations. In our 4.5 release, we’ve made several updates to the workflow for setting up validators to make this even easier.

Feature update: Easier validator setup for improved data quality

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

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Dema uses Validio to ensure the data quality for their prescriptive analytics

Validio 4.6 now available: see the latest updates

Sophia Granfors

Marketing Manager

The Validio 4.6 release is now generally available! This release makes the setup of sources and validators more scalable, introduces new integrations, and the option to decide the adaptation rate for dynamic thresholds. You can find all the details in the changelog, but here’s a snapshot:

One-click setup for sources and validators

Following the improved validator workflow in the 4.5 release, we’ve made additional improvements to streamline, speed up, and scale the process of creating sources and validators. 

Automatically create sources per table from data warehouse sources, and add metadata validators as part of the source creation flow.

Bulk-convert catalog assets: convert multiple catalog assets into sources and metadata validators at the same time.

Bulk-create validators: for example, add null count validator and then multi-select from a list of fields in the schema to apply null checks to each selected field.

If you prefer email over chat, you can now receive the latest notifications about incidents over email. The email can include graphs or just text, and be sent directly to the stakeholder who needs to know about data issues.

Validio's integration with Sigma’s business intelligence tool enables organizations to seamlessly validate and monitor data from Sigma's BI platform. Validio fetches catalog and lineage information from Sigma to display directly in Validio's data insights and lineage.

Atlan stores tables, views, columns, and lineage as assets in its data catalog. The Validio integration sends data quality metrics at regular intervals to Atlan, where you can view the metrics alongside each asset.

Flexible adaption rate for dynamic thresholds

The dynamic thresholds are a core part of Validio’s data observability, using a combination of algorithms to automatically detect anomalies in your data. The threshold model infers trends, seasonality, and also adapts to shifts in your data. Based on customer feedback from using Validio to monitor business metrics, we’ve added the option to configure how quickly the dynamic threshold's bounds adapt to changes in your data or metrics. This is controlled by the adaption rate parameter, which offers two settings:

Fast: The bounds of the dynamic threshold will adapt quickly to recent changes in the data.

Slow: The bounds of the dynamic threshold will adapt more gradually, placing more emphasis on historical data.

The best choice between Fast and Slow depends on your unique use case and the characteristics of your data, but the best part is that you can easily switch between the two. 

Bounds of dynamic threshold with different adaption rates. Bounds for Fast widen and narrow more rapidly with changes in data, while the bounds for Slow are more persistent in following the downward trend and triggering alarms when the trend changes.

Curious about automating your data observability?

All things data trust operational use cases, and abstracting complexity away from data engineering.

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

Valido 4.6: new integrations and algorithm updates

Discover the latest news and data trends

Validio 5.0: BI monitoring and improved search and filtering

Bringing data quality to unstructured data with LLMs and Custom SQL

One of the biggest problems with solving data quality: No one wants to own it.

Adapting fast & slow: tailoring dynamic thresholds with adaption rate

Valido 4.6: new integrations and algorithm updates

First 90 days as a data leader: Establishing data teams as value centres

Sneaky data quality issues, chaotic aftermath

When dashboards aren't enough: amplifying Looker with machine learning based alerts

Feature update: Easier validator setup for improved data quality