An automated data reliability platform designed for operational use cases. Abstracting complexity away from data engineering.
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The Validio platform is built to directly eliminate bad data through monitoring, validation and filtering of data in real-time streams and batches.
Don’t just monitor pipeline metadata, monitor the actual data too. Don’t just alert upon bad data, resolve it as well.
Proven ML algorithms to automatically detect data failures on a datapoint level.
Robust and proven statistical tests and methods to detect data failures on a dataset level.
Supporting hard-coded rules to leverage human domain knowledge for detecting data failures.
Validio’s real-time data reliability platform is purpose-built for continuously monitoring and validating data in the age of modern cloud data infrastructure and big data with high cardinality of data & data sources and constantly changing properties.
Validio can be used for analyzing both data in motion (streaming data) and data at rest (batch data). This enables proactive data quality management, as mitigations can be undertaken at the source of an error as soon as it occurs.
When data failures are identified, alerts are sent to relevant stakeholders (through e.g. Slack, email, PagerDuty), enabling them to take timely and corrective action. Alerts can also e.g. trigger retraining of machine learning models in production in response to e.g. data drift.
Validio automatically monitors for data failures using statistical tests and machine learning algorithms, while also supporting hard coded rules to transfer specific human domain knowledge. To get up and running requires minimal time investment.
Validio is deployed in the customers’ cloud environment (meaning that no data is sent to external environments) with an entirely automated and remotely managed in-VPC/Cloud-prem deployment. Validio also offers a fully managed SaaS version with general availability to be announced later. We understand the importance of deployment optionality, especially for sensitive data use cases.
Tests are performed in real-time, enabling a proactive approach to data quality management as users are notified about potential issues as soon as they emerge and can act on them before causing significant havoc in downstream applications
Validio is built ground-up with high-cardinality in mind. Applications now utilize thousands of data tables with hundreds or thousands of columns each that can’t be inspected manually or with manually defined data quality rules.
Make Validio a part of your pipeline operating on data in real-time, rectifying bad data before it enters the main data pipeline and affects data consumers and data products. Use automated real-time data filters and imputations to keep downstream applications safe from data failures.
Validio uses machine learning algorithms to detect patterns in the datasets. This way, thresholds are automatically defined, flagging unusual data points. As data changes, the alert thresholds are automatically updated based on data quality metrics forecast.
The vast majority of current data quality tools focus on monitoring (through SQL queries) data stored in warehouses (e.g. data at rest). Consequently, they cannot identify issues that occur before, during or after the data has been stored in e.g. Snowflake, BigQuery or Redshift.
The vast majority of data quality issues - especially unknown data failures - occur before, during or after data has been stored. Today, automated ELT pipelines are made up of several best-of-breed tools working together at different stages in the data pipeline. This results in new and unique forms of data failures that are harder to pinpoint and can propagate quickly, especially if they're not catched at the source.
We add new integrations continuously. If you don't see a technology in our integrations, contact us. We might already work on it or we can prioritize it.
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Good data trumps more data in almost every single case. Want to assess a company's data maturity? Ask how they evaluate the quality of their data, rather than how much data they have.
“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”
Sudhir Tonse Director of Data Engineering @ Doordash“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”
Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”
Eli Collins VP of Product @ Google"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."
Dustin Lange ML Science Manager @ Amazon“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”
Sudhir Tonse Director of Data Engineering @ Doordash“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”
Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”
Eli Collins VP of Product @ Google"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."
Dustin Lange ML Science Manager @ Amazon“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”
Sudhir Tonse Director of Data Engineering @ Doordash“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”
Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”
Eli Collins VP of Product @ Google"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."
Dustin Lange ML Science Manager @ Amazon“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”
Sudhir Tonse Director of Data Engineering @ Doordash“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”
Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”
Eli Collins VP of Product @ Google"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."
Dustin Lange ML Science Manager @ Amazon“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”
Sudhir Tonse Director of Data Engineering @ Doordash“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”
Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”
Eli Collins VP of Product @ Google"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."
Dustin Lange ML Science Manager @ Amazon“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”
Sudhir Tonse Director of Data Engineering @ Doordash“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”
Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”
Eli Collins VP of Product @ Google"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."
Dustin Lange ML Science Manager @ Amazon“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”
Sudhir Tonse Director of Data Engineering @ Doordash“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”
Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”
Eli Collins VP of Product @ Google"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."
Dustin Lange ML Science Manager @ Amazon“Trust in data is essential. If people suspect the quality is faulty, that will likely translate downstream to lack of trust in the models and analytics the data produces.”
Sudhir Tonse Director of Data Engineering @ Doordash“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.”
Andrew Ng Founder & CEO @ Landing AI / Adjunct Professor @ Stanford University“Data quality and anomaly detection should be some of the first things we think about when we design data pipelines and we consume data. Not an afterthought.”
Laura Pruitt Director of Streaming Data Science & Engineering @ Netflix“It doesn’t matter how advanced your data infrastructure is if you can’t trust your data.”
Eli Collins VP of Product @ Google"Modern companies and institutions rely on data to guide every single business process and decision. Missing or incorrect information seriously compromises any decision process downstream."
Dustin Lange ML Science Manager @ Amazon
The advent of big data and modern cloud data infrastructure has fundamentally changed the way organizations work with data. It’s time for data quality solutions to catch up with this new reality.
Download our latest whitepaper "Data quality in the era of Big Data and the Modern Data Stack" to read about how data infrastructure has changed during the past decade and the requirements for a future-proof data quality solution.
Reliable data pipelines are as important for the success of analytics, data science, and machine learning as reliable supply lines are for winning a war. We believe that you shouldn’t have to be an AirBnB, Uber or Netflix in order to have advanced ML-based data quality technology in place. We also believe that modern data teams and data engineers get better ROI by spending their time on other business-critical tasks rather than building and maintaining their own data quality infrastructure.
Join the waitlist and get notified of our self-service release.
Join the waitlist