Validio helps leading data-driven companies solve problems caused by bad data. Whether it be bad decision-making caused by incorrect numbers or unhappy customers that receive faulty invoices makes no difference. Bad data is everywhere, impacting market leaders of all sizes in all industries.
Validio’s next generation data quality platform is
- Built for massive scale
- Real-time, for real
- Fully secure (deployed in VPC or fully hosted)
It gives you and your data team complete trust in all your data so you can stop firefighting data quality failures in the dark and instead spend more time building robust and scalable systems. Validio is the next generation data quality platform built for the needs of the modern data stack, designed to handle data systems of all complexities and sizes.
Validio connects to all your data, regardless of location in data warehouses, data lakes or streams. This means you can validate data without it necessarily entering your data warehouse like other off-the-shelf data quality solutions require. This in turn unlocks powerful machine learning-, real-time- and operational analytics use cases where data never touches the data warehouse.
Validio is a breeze to get started with: rule-based and smart auto-thresholding monitors that adapt to trends and seasonality in your data over time are readily available in an intuitive UI. Taken together, this allows you to spend less time setting up and maintaining data quality. Flexible deployment in a fully hosted fashion or in your own VPC means no data ever has to leave your environment if you don’t want it to.
With powerful partitioning as well as uni- and multivariate monitors that work on both metadata, dataset, and data point level, you’ll be in full control of catching data quality failures before downstream consumers do.
Partitioning enables you to properly compare “apples to apples” in your data. Let’s say you’re validating price data across multiple product categories and markets with different currencies. Then it makes little sense to look at the price column overall because of differences in price levels across product categories and differences in currencies. If you really want to catch data quality failures specific for each product category and currency, you need to look at the price data for each unique combination of product category and currency in isolation. This is virtually impossible to do manually if you—like many of our customers—have hundreds of partitions.
Univariate and multivariate monitors mean you can set up validation on both single dimensions, as well as on dependencies between dimensions. Because let’s be honest, real data has dependencies in it and therefore, many data quality failures are multivariate in nature. For example, you might want to check that covariance between two dimensions doesn’t change abruptly. Validio suggests both numerical (mean, max, relative entropy, …) and categorical (mode, cardinality, …) monitors out of the box with new ones being added on a running basis.
Metadata, dataset and data point level validation mean you can validate your data from a bird’s eye view (like freshness and schema changes) as well as the nitty gritty details (like each individual data point meeting domain-specific rules). With Validio, you can even write out bad data points in real time to the data destination of your choice so you can take them into account as part of your data orchestration flow. In addition, this makes it easy to inspect bad data in the visualization tool of your choice, and thus resolve issues faster. As a bonus, you can avoid getting notified for every single bad data point which would surely result in alert fatigue.
With Validio, data quality has never been simpler or more comprehensive—soon available at your fingertips in the cloud marketplace of your choice.
Updated 19 days ago