data quality


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data quality

A dimension of data contributing to its trustworthiness and pertaining to accuracy, sensitivity, validity and fitness to purpose. Scientists trust data that are accurate (high quality), and that are reviewed by trusted peers and investigators and free from unauthorised alteration (high integrity).

Key elements
Attribution, legibility, contemporaneousness, originality, accuracy, precision, completeness, consistency (i.e., logical and not out of range).
References in periodicals archive ?
SAP was among 13 data quality solution vendors that Forrester selected for its 30-criteria evaluation of where each company stood in relation to each other to help enterprise architecture (EA) professionals select the right partner for their data quality needs.
We are now making Data Governance a very pragmatic initiative with the enterprise by providing real-time visibility to data quality metrics," said Tamer Chavusholu, Co-Founder and Managing Partner at Kaygen.
Business stakeholders are flush with data quality frustrations.
According to a recent Experian Data Quality study, less than half of companies have interactive contact data validation on their ecommerce site, which can lead to strong improvements in data quality.
Understanding the level of performance of these factors is also important as it gives important insights for business representatives towards finding the best approach for improving the overall level of data quality inside their organizations.
The reason that open source techniques are often of little use for specific data quality applications is complex.
Pitney Bowes Business Insight and automated data mastering technology vendor Silver Creek Systems released the results of a co-sponsored report this week called The State of Data Quality Today.
In order to deliver high-quality data, we need to understand the sources of poor data quality and what can be done about them.
New TDQ-IT software extends the power of SQL Server Integration Services to profile, cleanse, integrate and deliver the critical data quality businesses need
Although 66% identified operational efficiency as the most affected by poor data quality and 69% suggested that one of their main challenges was ensuring data is up-to-date and accurate, very few organisations had the policies, tools and processes in place to manage data quality in the most effective manner--or at all.
An analysis of 2004 claims data undertaken by Risk Management Solutions highlighted the significant role of poor exposure data quality used in the models (see "Recipe for Disaster," page 84).
As a result, customers who process large volumes of product and customer data from multiple regions can achieve superior data quality for optimal business operations performance.

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