Best practices for evaluating data quality tools

Best practices for evaluating data quality tools


August 24, 2010
17 Pages - SKU: DFMN2828962
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Data quality might not be getting as much airtime these days as exciting, cutting-edge technologies such as cloud computing or virtualization. However, that does not mean that its importance has diminished in corporate IT. Data quality has already proved its value in strengthening ERP, CRM, and BI investments, supporting business initiatives such as post-merger and acquisition management, customer integration (single view of the customer), and driving effective customer communications and more informed decision-making. Hence, companies can ill afford to leave the quality of their data to chance, particularly in still-tough economic times. Having accurate, consistent, and up-to-date information is now a business imperative rather than a luxury. However, companies should not underestimate the effort involved. Getting data quality - or in fact any data integration initiative - right is a difficult and complex endeavor. Yet the benefits are certainly worth the effort. Implemented correctly, it not only gives organizations the agility to better ride out the current recession, but also raises its competitiveness when the economy recovers. Admittedly, a large part of the challenge relates to people, process, and data governance, which are separate issues. This paper focuses on the other part of the equation - making sure the organization chooses the right data quality tool for the job. There are many automated data quality software solutions on the market, but not all are built equal. Since the investment in data quality can run into millions of dollars and is an ongoing process, organizations need to be sure they are opting for the best technology available on the market for their needs.



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