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Data quality tools for Salesforce
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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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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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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WinPure Clean & Match
WinPure Clean & Match is a complete data quality, cleansing, matching, and de-duplication software suite for your mailing lists, databases, spreadsheets, CRM's, etc. It lets you instantly fix data quality issues, as it scans each data list and provides over 30 different statistics ranging from % filled/empty cells to most common values & counts. It also features red & amber coloring to highlight potential data quality issues.
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MIOvantage
MIOvantage is a data quality platform that works alongside your existing systems to provide the best data quality possible. With a single solution, you can profile data, run rules, deduplicate data, identify entities, generate reports, and more.
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DQLabs
DQLabs.ai is an augmented data quality platform to manage your entire data quality life cycle. With ML and self-learning capabilities, organizations can measure, monitor, remediate and improve data quality across any type of data – all in one agile, innovative self-service platform.
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Collibra Data Quality
Collibra Data Quality lets you use predictive data quality to improve trust in your data. With predictive, continuous, and self-service data quality, you can centralize and automate data quality workflows to gain better control over your organization's end-to-end data pipelines and streamline analytics processes across the enterprise.
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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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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.