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Teradata
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Data quality tools for Greenplum
Data quality tools measure how good and useful a data set is to serve its intended purpose. High quality data can lead to better decision-making and faster insights as well as reduce the costs of identifying and dealing with bad data in the system. It can also save time and allow companies to focus on more important tasks.
Global IDs Data Quality Suite
Global IDs Data Quality Suite ensures the quality of the data by establishing control points and read-only quality controls at the database level. It continuously monitors quality metrics, while it also automates control generation for critical data elements across all kinds of data sources.
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BigID
BigID offers actionable data intelligence by empowering you to find, classify, catalog, profile and get context for all your data in the cloud or data-center, at-rest or in-motion, structured or unstructured at petabyte scale. It lets you identify sensitive data using hundreds of pre-built NLP, Deep Learning, and pattern classifiers.
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Aperture Data Studio
Aperture Data Studio combines self-service data quality with globally curated data sets into a single data quality platform. This empowers modern data practitioners to build a consistent, accurate, and holistic view of their consumer data quickly and effortlessly. It lets you set custom workflows for data profiling, cleansing, validation, transformation, enrichment, and deduplication.
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Trillium Quality
Trillium Quality rapidly transform high-volume, disconnected data into trusted and actionable business insights with scalable enterprise data quality. It has been designed to run natively in cloud or on-premises big data environments, ensuring your business information is integrated, fit-for-purpose, and accessible across the enterprise, regardless of volume.
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Data quality tools are the scripts that support the data quality processes and they heavily rely on identification, understanding, and correction of data errors. Data quality tool enhances the accuracy of the data and helps to ensure good data governance all across the data-driven cycle.
The common functions that each data quality tools must perform are:
• Data profiling
• Data monitoring
• Parsing
• Standardization
• Data enrichment
• Data cleansing
Choosing the right data quality tool is essential and impacts the final results. To help you with the right selection, we prepared a list of tools that will assist you with maintaining a high level of data quality.