Stop firefighting bad data with the
next generation data quality platform
Averages are dangerous and can often hide the truth. With partitioning, you can compare apples to apples by looking at anomalies in individual sub-segments of the data.
Set up validation on single dimensions, as well as on dependencies between dimensions. Because let’s be honest—real data has dependencies in it.
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.
Validio can be used for analyzing both data in motion (streaming data) and data at rest (batch data in data lakes, lakehouses and warehouses). This enables proactive data quality management, as mitigations can be undertaken at the source of an error as soon as it occurs.
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.
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, fixing 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.
On top of analyzing just one feature/column at a time, Validio also supports multivariate analyses. This allows for detecting more complex data quality issues that are multivariate in nature.
This allows users to version control the configuration of Validio’s data quality monitoring and automates and increases the speed of setting things up and making changes in the configuration
Validio can partition (based on other variables) a dataset into many different sub-datasets to be analyzed. This allows for more relevant and meaningful analyses.
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.
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.
“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"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."
Arun Swami Principal Staff Software Engineer @ Linkedin"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."
Jonathan Parks Chief Data Architect @ AirBnB"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."
Ying Zou Engineering Manager @ Uber"I moved to data engineering from software engineering, and honestly, I did not like my day-to-day job at the beginning. I loved doing data architecture and modeling, but firefighting on data quality issues took 70%+ of my time."
Subrata Biswas Engineering Manager @ AirBnB“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"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."
Arun Swami Principal Staff Software Engineer @ Linkedin"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."
Jonathan Parks Chief Data Architect @ AirBnB"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."
Ying Zou Engineering Manager @ Uber"I moved to data engineering from software engineering, and honestly, I did not like my day-to-day job at the beginning. I loved doing data architecture and modeling, but firefighting on data quality issues took 70%+ of my time."
Subrata Biswas Engineering Manager @ AirBnB“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"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."
Arun Swami Principal Staff Software Engineer @ Linkedin"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."
Jonathan Parks Chief Data Architect @ AirBnB"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."
Ying Zou Engineering Manager @ Uber"I moved to data engineering from software engineering, and honestly, I did not like my day-to-day job at the beginning. I loved doing data architecture and modeling, but firefighting on data quality issues took 70%+ of my time."
Subrata Biswas Engineering Manager @ AirBnB“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"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."
Arun Swami Principal Staff Software Engineer @ Linkedin"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."
Jonathan Parks Chief Data Architect @ AirBnB"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."
Ying Zou Engineering Manager @ Uber"I moved to data engineering from software engineering, and honestly, I did not like my day-to-day job at the beginning. I loved doing data architecture and modeling, but firefighting on data quality issues took 70%+ of my time."
Subrata Biswas Engineering Manager @ AirBnB“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"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."
Arun Swami Principal Staff Software Engineer @ Linkedin"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."
Jonathan Parks Chief Data Architect @ AirBnB"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."
Ying Zou Engineering Manager @ Uber"I moved to data engineering from software engineering, and honestly, I did not like my day-to-day job at the beginning. I loved doing data architecture and modeling, but firefighting on data quality issues took 70%+ of my time."
Subrata Biswas Engineering Manager @ AirBnB“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"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."
Arun Swami Principal Staff Software Engineer @ Linkedin"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."
Jonathan Parks Chief Data Architect @ AirBnB"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."
Ying Zou Engineering Manager @ Uber"I moved to data engineering from software engineering, and honestly, I did not like my day-to-day job at the beginning. I loved doing data architecture and modeling, but firefighting on data quality issues took 70%+ of my time."
Subrata Biswas Engineering Manager @ AirBnB“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"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."
Arun Swami Principal Staff Software Engineer @ Linkedin"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."
Jonathan Parks Chief Data Architect @ AirBnB"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."
Ying Zou Engineering Manager @ Uber"I moved to data engineering from software engineering, and honestly, I did not like my day-to-day job at the beginning. I loved doing data architecture and modeling, but firefighting on data quality issues took 70%+ of my time."
Subrata Biswas Engineering Manager @ AirBnB“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"Many organizations process big data for important business operations and decisions. As a metric of success, quantity of data is not enough - data quality must also be prioritized."
Arun Swami Principal Staff Software Engineer @ Linkedin"In early 2019, the company made an unprecedented commitment to data quality and formed a comprehensive plan to address the organizational and technical challenges we were facing around data. We knew we had to do something radically different, so we established the data quality initiative."
Jonathan Parks Chief Data Architect @ AirBnB"Without data quality guarantees, downstream service computation or machine learning model performance quickly degrade, which requires a lot of laborious manual efforts to investigate and backfill poor data."
Ying Zou Engineering Manager @ Uber"I moved to data engineering from software engineering, and honestly, I did not like my day-to-day job at the beginning. I loved doing data architecture and modeling, but firefighting on data quality issues took 70%+ of my time."
Subrata Biswas Engineering Manager @ AirBnB 
