Data quality tools and solutions
Cleanse data, manage it and support better decision-making
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Data quality solutions from IBM

Trusted data is the foundation of good business decisions. IBM offers data quality solutions that help to optimize the key dimensions of data quality: accuracy, completeness, consistency, timeliness validity, and uniqueness. 

These robust data quality tools help you to  identify, understand and correct data flaws to drive better decision making and governance.

DataOps ensures that data quality is preserved to meet all your business goals across a business-ready data pipeline. By implementing a data quality solution from IBM, your organization can enhance data integrity to get the most from your informational assets.
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See why IBM is a Leader in the 2022 Gartner Magic Quadrant for Data Quality Solutions
Introducing DataStage as a Service Anywhere - execute ETL and ELT Pipelines in any Cloud, Data Center or On-premises. 
Benefits of data quality Act on a trusted view

Govern your data and accurately target customers for cross-sell.

Support data governance

Manage diverse data across its lifecycle and optimize ROI.

Modernize systems

Consolidate applications and automate processes to reduce cost.

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Data fabric
Simplify data governance

Understand and correct data flaws to drive better decision making and governance. Automate with IBM Cloud Pak for Data to manage data trust, protection and compliance with active, end-to-end policy management.

Data quality tools Turn your data into trusted information
Turn data into trusted information. Continuously cleanse data and monitor its quality.
 Create and monitor data quality
Maintain an accurate view of entities such as customer, location, vendor and product.
 Improve Hadoop data quality
Get a rich set of data capabilities for Apache Hadoop big-data storage clusters.
 Intelligent data cataloging
Find business data, curate it, categorize it, govern it, analyze it and share it.
 Detect and resolve data incidents fast
Alert, respond, and resolve all your data incidents in one location with data observability.
Data quality case study
Flagstar Bank David Abayev, Data Quality Architect at Flagstar Bank, describes the strategy to modernize information architecture. Flagstar Bank David Abayev, Data Quality Architect at Flagstar Bank, describes the strategy to modernize information architecture. Watch the video (1:47)
Intelligently automate data and AI - Discover the next generation of IBM Cloud Pak® for Data.
Resources
Gartner® 2022 Magic Quadrant™
Discover why IBM is named a Leader for the 17th year in a row in the 2022 Gartner Magic Quadrant for Data Integration Tools.
 Download the data quality and AI performance guide
Learn from the experts in this step-by-step guide on how to take a values-driven approach to data quality and AI practices
 The DataOps interactive guide
See how DataOps builds a data-driven culture with automation, quality and governance.
 How clean is your data (Part 1)
Learn how data quality management delivers business value.
 Inline address data quality
Learn why verification and geocode solutions are critical while running ETL jobs.
 Build a modern, cost-effective data warehouse
Learn how data quality drives end-to-end flow from data creation to better business decisions.
 Data Quality for AI Toolkit
This framework of services provides a systematic way to assess and remediate data with well-specified APIs.
Next steps

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Turn data into trusted information

Discover how IBM InfoSphere can continuously cleanse your data and monitor its quality.

 Explore IBM InfoSphere Information Server for Data Quality Join the DataOps community

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