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Data quality tools for PostgreSQL
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.
RightData
RightData is a self-service suite of applications that helps you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities.
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Atlan
Atlan uses DataOps to create a new paradigm for building trust in your data. It auto-generates data quality profiles which make detecting bad data dead easy. From automatic variable type detection & frequency distribution to missing values and outlier detection, Atlan offers everything.
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CloverDX
CloverDX's data quality features offer you automated data quality and error handling for your workflows. With CloverDX's data quality features, you can discover and deal with bad data fast. You can automate bad data identification & correction, rule definitions, and reports on data quality. In short, CloverDX gives you the ability to make better decisions with more trustworthy data.
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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.