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Data quality tools for IBM DB2
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.
Dataedo
Dataedo is a data governance & data catalog tool that helps you ensure data quality while documenting data. It allows you to understand where your data is coming from through data lineage, peak into values itself to validate quality with data profiling, and gather invaluable feedback from the community.
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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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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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SAS Data Quality
SAS Data Quality is a comprehensive tool that meets all the data quality requirements of a business. It makes it easy to profile and identify problems, preview data, and set up repeatable processes to maintain a high level of data quality.
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Oracle Data Profiling and Data Quality for Data Integrator
Oracle Data Quality for Data Integrator is a comprehensive award-winning data quality platform that meets even the most complex data quality requirements. Oracle Data Quality addresses the enterprise data quality needs of all projects, including data warehousing and business intelligence, master data management, data integration, migration, service-oriented integration processes.
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Open Source Data Quality and Profiling
Open Source Data Quality and Profiling tool is an open source project dedicated to data quality and data preparation solutions. This tool is developing high performance integrated data management platform which will seamlessly do data integration, data profiling, data quality, data preparation, dummy data creation, meta data discovery, anomaly discovery, data cleansing, reporting, and analytic.
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IBM InfoSphere QualityStage
IBM InfoSphere QualityStage is designed to support your data quality and information governance initiatives. It enables you to investigate, cleanse and manage your data, helping you maintain consistent views of key entities. It provides capabilities including data profiling, standardization, probabilistic matching, and data enrichment.
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Talend Data Quality
As an integral part of Talend Data Fabric, Talend Data Quality profiles, cleans, and masks data in real time. It lets you quickly identify data quality issues, discover hidden patterns, and spot anomalies through summary statistics and graphical representations.
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DataCleaner
DataCleaner is a premier open source data quality solution. The heart of DataCleaner is a strong data profiling engine for discovering and analyzing the quality of your data. Find the patterns, missing values, character sets, and other characteristics of your data values.
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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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DQ*Plus
DQ*Plus data quality software cleanse data, verify addresses, find and consolidate duplicate records in batch and real-time. It accurately matches duplicate records and intelligently merges them into a single best record.
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Informatica Data Quality
Informatica Data Quality ensures end-to-end support for growing data quality needs across users and data types with AI-driven automation. It uses AI-driven insights to automate the most critical tasks and streamline data discovery to increase productivity and effectiveness. It ensures the delivery of high-quality information with data standardization, validation, enrichment, de-duplication, and consolidation capabilities.
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SAP Master Data Governance
SAP Master Data Governance improves the quality and consistency of information across your organization by consolidating and centrally governing the master data lifecycle. It empowers you to collaboratively describe, catalog, and implement rules for data quality evaluation. Initiate and schedule quality evaluations and manage evaluation results. In addition, you can overview current data quality status and KPIs. In short, it lets you define, monitor, improve data quality.
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IBM InfoSphere Information Server for Data Quality
IBM InfoSphere Information Server for Data Quality enables you to cleanse data and monitor data quality on an ongoing basis, helping to turn your data into trusted information. The solution offers end-to-end data quality tools to help you understand your data and its relationships; analyze and monitor data quality continuously; cleanse, standardize and match data; and maintain data lineage.
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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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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.