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

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FAQs

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Validio is a data quality, observability, automated business metrics monitoring.

Validio is built on the belief that data quality is a problem that is best solved cross-functionally. That’s why data engineers, analytics engineers, data scientists, and data analysts alike can use Validio—even non-technical users can enjoy the graphical user interface. In short, Validio is a home for data-driven teams.

Yes, we provide different tiers of onboarding services depending on your team needs. Our team is proficient with state of the art data trust strategy and can support your team with necessary workshops and use case prioritization guidelines.

Unlike other data quality and data observability tooling, Validio processes actual data and does not focus only on metadata. This means Validio offers more built-in intelligence for your data, such as: 

  • anomaly detection
  • datadrift
  • reference data checks 
  • data-driven recommendations
  • per-segment dynamic thresholds that adapt to trends and seasonality in data

All in real-time and without adding significant cost to your cloud spend. We agree it sounds too good to be true—try it out for yourself if you don’t believe us!

We define data quality as: “The extent to which an organization’s data can be considered fit for its intended purpose. Data quality should be observed along five dimensions: freshness, volume, schema, (lack of) anomalies, and distribution. It is always relative to the data’s specific business context.” Read more about Deep Data Observability in our blogpost “The Data Leader’s guide to Deep Data Observability”.

The main difference between Validio and other data quality or data observability tooling is that Validio is specifically designed around validating the data that matters to the business. To do this, Validio is built to be fully end-to-end, which means the platform reads data from streams, lakes, and warehouses. Additionally, Validio validates actual data, and is not focused on just metadata. This makes Validio great for many different use cases, including machine learning models that depend on reliable time-series data.

Validio offers a tiered pricing model with significant flexibility within tiers. Contact us for a detailed explanation!

Validio integrates with the most common tools across the Modern Data Stack. For more information, refer to our integrations page.

No, Validio is built to be a home for data-driven teams, including non-technical users. You can control Validio from the graphical user interface, without any experience in writing code.

No, Validio is built to be a home for data-driven teams, technical users like data engineers. You can control Validio from an SDK, without using the graphical user interface—although most technical users find it very helpful as it automates many tasks.

Validio can be deployed either in your virtual public cloud, or it can be hosted by Validio as a managed service.

Yes, Validio is ISO27001- and SOC 2 Type II certified. Since the Validio system is streaming-based, no row-level data is retained in the system after calculating the validators. Usually, the validation takes less than a minute, and never more than an hour.

Validio has an internal compiler that is specifically designed to execute data validation. It happens either in a streaming-based fashion or through pushdown to your data warehouse, depending on what is most cost-effective. Our customers see a negligible impact on their cloud bill.

Yes, the Validio team offers tailored training sessions as part of deploying Validio.

You can book a demo where we will walk you through the key capabilities of the platform in 30 minutes, or request a 14 day free trial to see how Validio can help you gain trust in your data assets that matter.

Validio is easy to deploy and can be up and running within five minutes with the right access levels.

Validio’s dynamic thresholds are instantly trained through backfilling of historical data. This means that you do not have to wait for days or weeks for new data to come in before thresholds become reliable.