Home Solutions Big data Data quality

5 PB

covered data

1, 000s

covered datasets


reduction of data defects




anomaly detection


Increase confidence in data and insights

As companies become more data-driven, the cost of errors due to bad data increases. Corrupted data leads to poor quality reports and disastrous AI decisions, eroding business stakeholders’ confidence in data and the accuracy of insights. Adding data quality monitoring and management to the production data lake helps detect, prevent, and auto-correct data defects and ultimately leads to data quality assurance. Data quality assurance means reaching more relevant insights, better decision making, boosted business values, and increased trust from stakeholders.


Detect data corruption and prevent it from spreading

Automatic business rules validation is the most reliable way of achieving good data quality. The best data quality tools will integrate with any data engineering technology stack by injecting business rules enforcement jobs between critical data processing and transformation jobs. A convenient graphical interface will decrease implementation costs by reducing the amount of coding the team has to do and empowering your data analysts.


Rely on AI to find unusual patterns

It’s not always possible to define business rules to enforce data quality at every step of the data processing pipeline. AI-powered anomaly detection automates data quality and helps scale it to thousands of data pipelines with minimal effort. From basic statistical process control to deep learning algorithms, AI learns the relevant data profiles in real-time, uncovering hidden defects or unusual patterns. A rich user interface helps tune and monitor data quality metrics and profiles, allowing data scientists to achieve a deeper understanding of the data. 


Ensure consistency with systems or record

Poor data quality is often caused by issues with data ingestion. Common issues include missing, corrupted, or stale data. Stream ingestion and processing can increase the chances of sourcing inconsistent data due to missed events. Adding consistency and completeness checks between raw datasets and systems of record improves data quality early in the pipeline, preventing corruption from entering into the system.


Autocorrect data defects

In some cases, it’s possible to achieve automatic correction of data issues. Similar to the business rules that detect data corruption, injecting auto-correcting rules helps self-heal and avoids downtime.

Data Observability Starter Kit

The Data Observability Starter Kit simplifies data quality onboarding for modern businesses by offering checks for tabular, structured, and unstructured data. It includes built-in quality assessments for null/missing values, statistical distributions, data freshness, volume, and anomaly detection through unsupervised learning models. Easily integratable into data platforms and modern data warehouses, this starter kit ensures a swift time-to-market for monitoring data quality across all data types.

Our clients

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Paypal logo
macy's brand logo


Neiman Marcus logo
Grandvision logo
macy's brand logo
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IAS logo
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curiositystream brand logo


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Stanley Black&Decker logo
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Paypal logo
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logo of travelers brand
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Vertex logo
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Get to market faster with our data quality starter kit

We have helped Fortune-1000 companies improve their data quality in the most demanding data platforms. This includes platforms holding 5+ petabytes of data, processing hundreds of thousands of events per second, across thousands of datasets and data processing jobs. This provided us with the expertise to develop a complete set of data quality management tools as part of the development of our starter kit. The starter kit is based on an open-source cloud-native technology stack and is infrastructure agnostic – with the ability to deploy in AWS, Google Cloud, or Microsoft Azure. It integrates best with Hadoop and Spark-based data lakes with Apache Airflow orchestration, but also supports integration with SQL-based data sources out of the box and integrates with any other analytical data platforms, data warehouses, databases, and ETLs.

Validate simple or complex business rules

There’s a variety of data quality checks that can be implemented as business rules. With our solution, data analysts and engineers can create rules to ensure that certain data columns don’t exceed pre-defined ratios of nulls, validate that data falls into certain ranges, or check that a data set complies with a certain profile. The tool assists with data profiling, measuring data quality metrics, cleansing and auto-correcting data, and alerting the support team when something goes wrong.

Uncover hidden anomalies with AI

If your data analytics platform already has thousands of data processing jobs or the business rules being used aren’t detecting complex data defects, anomaly detection can help build a more comprehensive data quality solution. Data scientists can configure automatic data profiling to collect key data metrics, use statistical process control techniques, and configure deep learning anomaly detection to uncover suspicious patterns and alert the support team if predefined levels of confidence are reached.

Ensure completeness and consistency

Good data quality starts with ensuring that the raw data imported into the data analytics platform is done correctly and completely, is consistent, and not stale. With our solution, we can configure various types of checks that integrate with data sources in data lakes or SQL-based databases. Measuring and improving data completeness is critical for streaming use cases such as clickstream processing, order processing, payment processing, or Internet of Things applications, when events can be dropped or processed more than once.

Data quality industries

We develop data quality management solutions for technology startups and Fortune-1000 enterprises across industries including media, retail, brands, gaming, manufacturing, and financial services.

Read more about data quality

Stop inventing excuses for poor quality data

We provide flexible engagement options to improve the data quality of your data lake, EDW, or analytics platform. We use our cloud-agnostic starter kit to decrease implementation time and cost so that you can start seeing results in just weeks.

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