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.
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.
Schema change detection
Don’t let upstream changes impact downstream output. Automatically track schema evolution to ensure validity and prevent potential issues on assets like dashboards and reports. Stay ahead of unwanted schema changes by setting up alerts so you can take immediate action.
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
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Frequently asked questions about data quality
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.
