Modern stacks are complicated, making it hard to keep track of data quality. But there’s a solution. Collibra Data Quality & Observability monitors data quality and data pipeline reliability to help you rapidly remediate anomalies.
Run Collibra Data Quality & Observability on any cloud and connect to more than 40 databases and file systems. Scan data where it resides via pushdown or pull-up processing.
For years data managers have painfully coded data quality rules by hand, only to find out that the rules they put in place are old, incorrect or didn't trigger when they needed them the most. With our AdaptiveRules you can instantly connect to any data source and generate thousands of monitoring controls in minutes. Instantly detect breaking trends so you can stop sending bad data downstream.
Access an out-of-the-box repository of industry-specific, auto-validation rules. Automatically discover sensitive data, enforce quality and take action on broken records.
Write your own custom rules with an intuitive, built-in SQL editor. Avoid proprietary programming language lock-in and rule rewrites as data moves across systems. Stop re-writing the same rules over and over and use our rule templates to build rules that can be re-used and shared across many business units.
Data pipeline monitoring
Never make a business decision on stale data. See inside data pipelines to reveal blindspots in your data operations. Distinguish one-off incidents from system-wide hiccups. View data health by business unit and data source.
ML-generated adaptive rules
Connect to any data source and generate thousands of monitoring controls in minutes. Instantly detect breaking trends so you can stop sending bad data downstream.
Access an out-of-the-box repository of industry-specific, auto-validation rules. Automatically discover sensitive data, enforce quality and take action on broken records.
Write your own custom rules with an intuitive, built-in SQL editor. Stop re-writing the same rules over and over and use our rule templates to build rules that can be re-used and shared across many business units.
Data pipeline monitoring
See inside data pipelines to reveal blind spots in your data operations. Distinguish one-off incidents from system-wide hiccups. View data health by business unit and data source.
Reliable and trusted data across the organization
Data steward
Data engineer
Access is everything
What’s the point of data if you can’t get your hands on (or mind around) it? By streamlining processes, simplifying quality and governance, and making sense of how data is organized, your data isn’t only easier to access, it’s easier to maintain compliance. Collibra Data Quality & Observability makes your life easier by putting trust back into data across sources to more users.
Automate rule management
Reduce manual rule writing and errors by more than 50% with ML-generated, adaptive and explainable data quality rules.
Get end-to-end visibility into the quality of your data
Mitigate compliance risk by ensuring your data is timely identified, accurate, valid and complete.
Get full visibility into data quality reporting
Democratize data quality for all technical and business users to proactively identify, assign and remediate data errors.
Everything comes down to data, and for you, it comes down to data reliability
You need automated workflows and streamlined data operations. You must trust your information, so you can provide trustworthy insights to decision makers. Collibra makes it possible to scale reliable data pipelines with a 50-70% reduction in manual rule writing.
Avoid downtime with proactive anomaly detection
Discover real-time breaking trends and prevent bad data from making its way downstream.
Get real-time visibility into the health of your data
Build reliability across datastores and business dashboards with comprehensive data quality checks every step of the way.
Deliver data pipeline observability in just one sprint
Leverage low-code, API-based integration to get up and running quickly with minimal pipeline changes.
Customer stories
Healthcare
Cambia Health Solutions improves member experience with data quality and governance
Financial services
Northern Trust aims to provide quality data with Collibra
Data quality determines if data is fit for use to drive trusted business decisions. Measuring data quality can help a business determine if data errors need to be resolved before the data can be used for its intended purpose. Data is considered high quality based on consistency, uniqueness, completeness and validity. High quality data must have standardized data entries, data sets that do not contain repeated or irrelevant entries, be representative of real-world conditions, and conform to the formatting required by the business.
Poor data quality leads to higher operational costs, inaccurate reporting, and ultimately, lower revenue for your organization. Unfortunately, incomplete, duplicate, redundant, or inaccurate data is commonplace in business, resulting from human errors, siloed tools, multiple handovers and inadequate data strategy. Gartner’s Data Quality Market Survey showed that the average annual financial cost of poor data is to the tune of $15M. The survey also revealed that poor data quality practices undermine digital initiatives, weaken competitive standing, and affect customer trust.
The core functionalities of data quality software should include data profiling, data cleansing, data enrichment, data monitoring and data governance. Data profiling helps to understand the quality of data by analyzing its metadata. Data cleansing helps to remove errors and inconsistencies from data. Data enrichment helps to improve the quality of data by adding more information to it. Data monitoring helps to ensure that data quality is maintained over time. Data governance helps to ensure that data is managed effectively and efficiently throughout its lifecycle.
Data quality tools identify errors and inconsistencies in data and provide a framework for correcting them to ensure that the resultant data is valid, accurate, complete and consistent. Data accuracy is the level to which data represents the real-world scenario and is supported by a verifiable source. Accuracy of data ensures that the data can be used for its intended purpose. Alongside data accuracy, data also must be complete and consistent. Data completeness ensures that all required data is present and accounted for, while data consistency ensures that data is uniform across all systems and applications.
