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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.

Articles

Data Loss Prevention: Who's Driving the Bus?

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

Diagnosing Customer Data Disorder

The second installment of a three part series explores an approach to understanding an organization's current state of customer data disorder and looks at business symptoms of the problem

Right-Time Information for the Real-Time Enterprise

An IT organization can enable the real-time enterprise to improve business agility by delivering accurate, consistent and timely information across the extended enterprise

Customer Data Disorder: Part 1 - Defining Customer Data Disorder

Customer data disorder means there is significant variance between information about the customer party in the information entity and the party itself in the real world

Real Time Data Warehousing, Part 3

This third and final article of the series addresses the questions “What causes the biggest headaches?” and “What can do you do about it?”

Columns

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

Solving from the Top of the Transformation Chain, Part 2

If you want to determine your largest customers and understand how they interact with your company, start with the source data, then match, identify and link

Data Quality: You Don’t Just Need a Dashboard!

This month, I’ll discuss how data quality issues occur and how your organization can begin to identify and address them.

Delivering Data Quality: The Executive Sponsor

It is unlikely that the people working on central entity data systems can achieve compliance to new data quality standards across the organization.

Ask the Experts

How do you measure/calculate information quality quotient for a particular data set?

How much time is needed to clean the master data and get it on track?

How can one measure the quality of data - both on master data and transactional data?

What are some best practices for customer data matching, cleansing and integration when your customers are public and private institutions in a variety of industries?

What standard/guidelines should be implemented in the transactional systems to make the data business intelligence ready?

White Papers

Data Warehousing Ensuring Data Integrity

By Cindy Maurer

Making Data Work: Addressing Data Quality at the Enterprise Level

By Informatica

Can your SharePoint Backup Harm Your Business?

By AvePoint

The Value Behind Integrity

By by ETNA Software

Building Profitable Customer Relationships and Personalized Retention Strategies

Books

Corporate Information Factory, 2nd Edition

By William H. Inmon, Claudia Imhoff, Ryan Sousa

The Data Warehouse Challenge: Taming Data Chaos

By Michael H. Brackett

Data Quality for the Information Age

By Thomas C. Redman




Industry Vendors