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Data Quality Channel

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Companies often cannot rely on the information that serves as the very foundation of their primary business applications. Inaccurate or inconsistent data can hinder your company's ability to understand its current - and future - business problems. This leads to poor decisions that can cause a host of negative results, including lost profits, operational delays, customer dissatisfaction and much more.

An effective data quality strategy can help you better understand your business environment, allowing you to maximize profitability and reduce costly operational inefficiencies.

Data quality technology allows companies to analyze, improve and control enterprise data, providing the infrastructure to enable data governance by transforming raw data into consistent, accurate and reliable corporate information. The building blocks of enterprise data quality methodology are:

• Data Profiling - Inspect data for errors, inconsistencies, redundancies and incomplete information
• Data Quality - Correct, standardize and verify data
• Data Integration - Match, merge or link data from a variety of disparate sources
• Data Enrichment- Enhance data using information from internal and external data sources
• Data Monitoring - Check and control data integrity over time

This resource channel is brought to you by DataFlux and DM Review. As leaders in the industry, DataFlux and DM Review continually provide this Web site with continually updated, accurate and targeted information.

Articles

Do You Know If Your Data Is Accurate?

While compliance isn’t dominating the news as it did a few years ago, it is still an issue that organizations struggle with on a daily basis

The Ins and Outs of Imperfect Data

Corporate data is inherently imperfect.

Fighting Improper Payments with Master Data Management

Improper payments from government agencies have been a long-standing and significant problem.

Diagnosing an Effective Data Quality Initiative

Before undertaking any data-driven effort, it is essential to have a clear understanding of the integrity of your current data.

The Invisible Risk of Poor Data Quality on Change Management

Change management causes IT processes to be slower, clumsier or less effective, with devastating impact on the profitability of the business. Why?

Columns

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.

Talking with Your Business Partners about Data Quality

The description “bad data,” or something more emotional, is simply too general to be valuable.

Technical Measures for Data Quality Investments

This column looks at technical measures of data quality for the cases presented in my May column.

Calculating the Return on Data Quality Investments

Everyone agrees that data quality is important, but that doesn’t make them willing to pay for it.

Five Keys to Data Quality

There is still a significant human element to the overall quality of your data.

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