Data Quality Tools & Solutions

Overview

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.

See why IBM is a Leader in the 2022 Gartner Magic Quadrant for Data Quality Solutions

IBM named a Leader in the Gartner® 2022 Magic Quadrant™ for Data Quality Solutions

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Benefits

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.

Use case

Worker at desktop PC with security displays in the background

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.

Solutions

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.

Case studies

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. (1:47)

Intelligently automate data and AI - Discover the next generation of IBM Cloud Pak® for Data.

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Resources

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.

IBM DataStage®

Explore AI-powered data integration for all of your multicloud and hybrid cloud environments.

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

Schedule a 30-minute one-on-one call

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

Connect with experts and peers to elevate technical expertise, solve problems and share insights.

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