Data Quality Channel

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If there is a single pitfall that undermines any given data management initiative, it is most likely to be found in the realm of data quality, a requirement for sound decision-making. Whether in combination or by themselves, databases are almost certain to contain entry errors, multiple common entries and other redundancies that inevitably lead to incorrect or incomplete identifications of customers, products and locations. Thus, data quality is a critical prerequisite to any BI initiative that would otherwise skew or obfuscate meaning in the reporting and analytic outputs of databases, reporting tools, dashboards and scorecards.

4 Steps to Create an Effective IT and Business Partnership

Undertaking business intelligence, data quality or an enterprise data management project without full cooperation and collaboration between IT and the business is a formula for frustration

Leverage Data Quality to Build an Effective Enterprise Architecture

It might be time to rethink the notion that effective information architecture development will solve the data quality problem

Data Profiling: How to Get Started Without Business Rules

When the master data management show rolls into an organization, a data quality program, targeted at the master data, is usually part of the effort

5 Data Quality Management Steps for MDM

Without DQM, MDM is merely a dump of the data repository, and sans MDM, DQM cannot bring ROI to the organization

Data Loss Prevention: Who's Driving the Bus?

DLP solution vendors must continually enhance their technology to enable businesses to leverage information effectively.

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Four More Sources of Poor Data Quality

How to develop a strategy to identify and understand the issues

Seven Sources of Poor Data Quality

Quantify types and extent of data quality problems to prioritize efforts

Data Quality and MDM

A data quality mind-set increases MDM program success

Understanding the Cost-Benefit Quality Curve

Set realistic expectations for the delivery of useful knowledge, not perfect information

Why is the B2B Model so Difficult?

The complexities of the B2B model exist due to data quality, loyalty challenges and analytical misdirection

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Overall Approach to Data Quality ROI

Observing Data Quality SLAs by David Loshin

Populating a DQ Scorecard by David Loshin

Data Quality Remediation

Improving the Accuracy of Spend Analysis through Data Quality

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Data Quality Assessment

By Arkady Maydanchik

Data Mining VIII: Data, Text and Web Mining and their Business Applications

By Edited by: A. Zanasi, C.A. Brebbia and N.F.F. Ebecken

Data Quality Assessment

By Arkady Maydanchik

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• Find Data Quality products & services on Information Management Marketplace

Articles from this Site

• Internet Marketing Leader Selects Silver Creek Systems
• Pioneer Investments Selects DataFlux
• Pitney Bowes Group 1 Software Unveils CDQ Platform v5.6
• Satori Software Selected by U.S. Senate
• Understanding the Cost-Benefit Quality Curve
• More Articles from this Site

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White Papers

• Overall Approach to Data Quality ROI
• Improving the Accuracy of Spend Analysis through Data Quality
• Data Quality Remediation
• Populating a DQ Scorecard by David Loshin
• Observing Data Quality SLAs by David Loshin
• More White Papers

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Web Seminars

• The Learning Curve: Data Quality Fundamentals
• The Learning Curve: Ensuring Data Quality in Data Integration
• The Learning Curve: Why You Need a Data Quality Assessment
• The Learning Curve -- Data Quality Curriculum Launch
• Closing the Loop: Real-Time Event Detection and Response
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eLearning

• Data Quality Assessment
• Data Resource Quality: Turning Bad Habits into Good Practices
• Data Quality Fundamentals
• The Data Model Scorecard
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Books

• Data Quality Assessment
• Data Mining VIII: Data, Text and Web Mining and their Business Applications
• Data Quality Assessment
• Introduction to Data Mining Using SAS Enterprise Miner
• The Complete Idiot's Guide to Lean Six Sigma
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