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⭐ Read our latest report: How to choose a data quality platform

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ELIMINATE


BAD DATA BROKEN PIPELINES UNKNOWN DATA FAILURES ANOMALIES BROKEN DASHBOARDS
BROKEN ML-MODELS OUTLIERS BAD DATA

Stop firefighting bad data with the
next generation data quality platform

Get a demo

Or sign up for free community version waitlist


WE'RE A GROUP OF AMBITIOUS BUILDERS WHO GOT TIRED OF BAD DATA.




GET STARTED WITH NEXT GENERATION
DATA QUALITY TODAY

Validio is the only data quality platform that scales with modern cloud-first
organizations
as they become increasingly data-driven.

Learn more


HEAR FROM OUR CUSTOMERS


> "WE USE VALIDIO TO VALIDATE DATA FOR OUR MOST CRITICAL APPLICATIONS. SINCE WE
> INVEST MILLIONS OF EUR IN ALGORITHMIC PERFORMANCE MARKETING, VALIDIO BECOMES A
> CENTRAL PART IN OUR LINE OF DEFENSE AGAINST BAD DATA."

Marcus Svensson

Head of Data Science at Babyshop Group

Data stack




TRUST YOUR DATA IN WAREHOUSES, DATA LAKES AND STREAMS

Get complete trust in your data for any use case, whether it's classical BI or
more advanced machine learning and operational real-time analytics where data
might never touch the warehouse.




SAVE TIME WITH SMART ALERTS AND AUTO-THRESHOLDS

Choose between rule-based or auto-threshold monitors in an Intuitive UI that
adapt to trends and seasonality over time.

Overall, this enables you to spend less time setting up and maintaining data
quality over time.




CREATE BETTER DATA PIPELINES BY WRITING OUT BAD DATA IN REAL-TIME

Validio enables bad data to be written to a data destination of your
choice—effectively filtering it out. This enables data to be fully
operationalized. Even if some percentage of bad data is expected, the pipelines
won't break.

If major bugs appear, bad data can be manually inspected in a data visualization
tool of your choice—leading to faster resolution.

Spend more time building robust and scalable systems instead of firefighting bad
data.



POWERFUL PARTITIONING

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.

--------------------------------------------------------------------------------

UNIVARIATE AND MULTIVARIATE

Set up validation on single dimensions, as well as on dependencies between
dimensions. Because let’s be honest—real data has dependencies in it.

--------------------------------------------------------------------------------

METADATA AND ACTUAL DATA

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.

--------------------------------------------------------------------------------


COMPREHENSIVE DATA QUALITY VALIDATION AND MONITORING

DATA IN MOTION AND AT REST

ANALYZE BOTH REAL-TIME STREAMS AND BATCH DATA DEPENDING ON YOUR DATA PIPELINE
SETUP

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.

--------------------------------------------------------------------------------

STATISTICAL AND ML-BASED

UTILIZE ADVANCED STATISTICAL TESTS AND MACHINE LEARNING ALGORITHMS

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.

--------------------------------------------------------------------------------

REAL-TIME

BATCH OR STREAMING PIPELINES, TESTS ARE PERFORMED IN REAL-TIME, ENABLING A
PROACTIVE APPROACH TO DATA QUALITY

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

--------------------------------------------------------------------------------

HIGH CARDINALITY MANAGEMENT

BUILT GROUND-UP WITH HIGH-CARDINALITY IN MIND THROUGH HANDS-ON EXPERIENCE

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. 

--------------------------------------------------------------------------------

REAL-TIME AUTO-RESOLUTIONS

OPERATE ON DATA IN REAL-TIME, FIXING BAD DATA BEFORE IT DOWNSTREAM CONSUMPTION

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.

--------------------------------------------------------------------------------

MULTIVARIATE ANALYSIS

FOR DETECTING MORE COMPLEX DATA QUALITY ISSUES THAT ARE MULTIVARIATE IN NATURE

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.

--------------------------------------------------------------------------------

INFRASTRUCTURE AS CODE

BESIDES AN INTUITIVE GUI VALIDIO ALSO SUPPORTS INFRASTRUCTURE AS CODE

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

--------------------------------------------------------------------------------

DATA PARTITIONING

COMPARE APPLES TO APPLES BY VALIDATING INDIVIDUAL DATA SEGMENTS

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.

--------------------------------------------------------------------------------

DYNAMIC AUTOTHRESHOLD MONITORS

MACHINE LEARNING ALGORITHMS DETECTING PATTERNS IN DATASETS DYNAMICALLY

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.

--------------------------------------------------------------------------------

CUSTOMIZABLE ALERTS

SEND ALERTS TO RELEVANT STAKEHOLDERS E.G. VIA SLACK, EMAIL AND PAGERDUTY

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.

--------------------------------------------------------------------------------


STATE-OF-THE-ART DATA QUALITY IN MINUTES

Trust the data you use to make decisions & build products in both batch and
streaming pipelines




DON’T JUST MONITOR PIPELINE METADATA, MONITOR THE ACTUAL DATA TOO. DON’T JUST
ALERT UPON BAD DATA, RESOLVE IT AS WELL.

Validio is the only data quality platform validating pipelines in real-time on
datapoint, dataset and metadata level, enabling you to write out bad data to a
data destination of your choice.

Validio integrates seamlessly with your data pipelines so you can get complete
trust in your data, knowing you will catch any data quality failures before
downstream data consumers do






INTEGRATES SEAMLESSLY WITH MODERN CLOUD INFRASTRUCTURE




MISSING AN INTEGRATION? 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.

Contact Us


MORE DATA ISN'T THE MAGICAL ASSET ORGANIZATIONS OFTEN THINK IT IS.

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.

Patrik Liu Tran CEO & Co-Founder @ Validio / Co-Founder @ Stockholm AI


DATA PIPELINES HAVE BECOME THE NERVOUS SYSTEM OF THE MODERN COMPANY AND MANAGING
DATA QUALITY IS THE BEATING HEART

“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


DOWNLOAD OUR LATEST WHITEPAPER

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.


Download whitepaper


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SOFTWARE ENGINEER (RUST)

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TECHNICAL WRITER

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VALIDIO IS USED BY LEADING DATA-DRIVEN ORGANIZATIONS

From startups to multi-billion dollar unicorns, Validio is used by data leaders
of all sizes. 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.

Request a demo and learn how fast you can get started with state-of-the-art data
quality validation and monitoring. We place a special emphasis on being a
non-nonsense data quality partner focusing on time-to-value.

Request demo

The next generation data quality platform for modern data teams.

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