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Data Quality Management

Data quality management (DQM) has increased in importance as information infrastructures become more sophisticated and the volume of data generated continues to expand rapidly. 

This critical business discipline requires a combination of guidelines, processes, and technologies to preserve the accuracy, completeness, timeliness, and consistency of enterprise information. Companies that embark on a data quality management initiative should include data profiling, data cleansing, master data management, and data governance as part of their strategy, in addition to assigning data stewards who are fully accountable for enforcing data quality procedures and rules.

Information Builders Omni-Gen Data Quality Edition is packed with features designed to optimize the integrity of any enterprise information regardless of its source, location, or format. It contains robust data governance capabilities for end to end remediation of data quality issues and allows organizations to increase their information capital to yield higher business value.

CIOs and business executives alike have ranked data quality management at the top of their priority list. Organizations seeking to exploit their enterprise information for competitive advantage must implement a far-reaching data quality management program, supported by the right tools and technologies, in order to maintain full control over the integrity of information across all applications, systems, databases, messages, and documents.

There are many data quality management solutions on the market today, each designed to uncover and correct the invalid or corrupt information that is hiding within enterprise sources.  Although much of this "dirty" data is caused by errors that occur during manual data entry processes, today's age of automation, where organizations dynamically share information with and collect information from countless third-party systems, is adding to the problem. New, unstructured sources of information like social media sites further exacerbate the issue, making the need for data quality management more urgent than ever before.

Organizational culture must be transformed to view data quality management as an ongoing strategy, not a series of one-off projects. This shift is difficult to foster, so workarounds and quick fixes prevail; however, the cost of bad data escalates as time goes on. Just as it’s much cheaper to fix manufacturing defects before a product is shipped to consumers, it’s much cheaper to manage data quality early on than to fix data after it has been delivered to analysts, applications, and other end users.

Promoting data quality across your organization doesn’t have to be overwhelming. Organizations can improve data processing in transactional and analytical applications, enhance system migration or software integration projects, improve customer identification processes, and validate inputs in online applications.

Information Builders has built the Omni-Gen Data Quality Edition so companies can exploit the full potential of enterprise information to improve operations, ensure compliance, meet service-level agreements, and increase stakeholder value. It makes it easier for business and IT to collaborate and projects that require data standardization, cleansing, and enrichment. It uses business definitions to generate artifacts needed for these projects and to adapt to changing requirements.

The Data Quality Edition helps companies leverage powerful data integration and cleansing technologies to ensure data availability, credibility, and trustworthiness. Organizations can improve data processing in transactional and analytical applications, enhance system migration or software integration projects, improve customer identification processes, and validate inputs in online applications.

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