A Data Quality

of the information powering your business is becoming a strategic imperative. Traditional data quality tools offer a poor fit, are frequently expensive to integrate into an existing infrastructure, and offer stitched-together solutions that are difficult to understand and manage. dn:Director is the next generation information quality

needs can be due to a:  Migration to a new system from an existing legacy data system.  Part of a data warehouse load process.  Data acquisition from another/existing business.  A part of a business process re-engineering.  Consolidation of existing data systems. Data integration is a complex, expensive endeavor for companies and businesses

flawed data or erroneous data. Learn about the most common reasons that contribute to poor data quality and how to alleviate them. Poor data quality can play a significant role in eroding your company's bottom line and competitive advantage in the marketplace. Data corruption can occur due to a number of reasons, the following are the most

alone; you need to understand the underlying quality of the data contents. Datanomic's data quality management software, dn:Director, enables disparate views of data to be unified for MDM initiatives. By deploying Datanomic's holistic approach, dn:Director enables users to understand, improve, protect and control data standards across

and business transformations like these need a central organizational structure such as a data governance committee or board, staffed with both business and technology people. The board must institute and enforce policies and procedures for data management and business use of data. And data governance is best coordinated with IT

pricing on data conversions. There are a number of reasons that you would want to convert your data. ▪ Data acquisition from another/existing business ▪ Part of a data warehouse load process ▪ Migration to a new system from an existing legacy data system ▪ Part of a business process re-engineering ▪ Consolidation of existing data systems Data

suppliers, plus a lot of technical know-how, was needed to successfully accomplish a data migration, but the architecture becomes difficult to manage, and performance deteriorates due to the number of different interfaces between applications. The ideal solution is a software tool that supports the whole data migration

Explore data quality impacts, how data quality relates to business issues, and how to integrate data quality monitoring into operational systems. Data quality has often been isolated as a separate silo in the organization, rather than fully integrated into the

podcast, appropriate for both business and IT professionals, hear advice and tips for effective data quality program management from industry expert Evan Levy, partner and co-founder of Baseline Consulting. Leading an enterprise-wide data quality management program is no easy task. Program managers must have a unique combination of skills to

Get updated information and data quality best practices, from expert Larry English. Find out how to get started, build a business case and structure program teams for data quality success. Successfully implementing information and data quality programs can be challenging -- often

processes rely on a solid, high-quality data foundation, but companies often neglect data quality until a major problem occurs that could've been avoided. Learn about the benefits and importance of da... Most manufacturers would never think of eliminating the quality control function from their production processes. Without quality

By using dn:Director to improve data quality and isolate data that is unreliable, organizations are able to optimize the return on investment in BI and Data Warehousing and maximize competitive advantage. Data Quality: The Key to Successful Business Intelligence The Trouble with

the new database ▪ Support for all major databases ▪ ZERO Defect data Guarantee ▪ Data cleansing and data quality management ▪ Data integration with heterogeneous data sources ▪ Customized data transformations and extraction Our approach focuses on delivering an integrated solution that combines all the key components critical for a successful

implemented, nondisruptive, scalable, and comprehensive foundation for capturing data quality events as well as measuring and ultimately controlling data quality in the data warehouse. The industry has talked about data quality endlessly, but there have been few unifying architectural principles. This paper describes an easily

45;faceted.

This white paper provides you with ten key imperatives to consider when evaluating a data protection and control system. Download this white paper and find out how fixing broken access control to your file servers can significantly reduce the probability of data misuse. This paper also details:

• Past solutions administrators

The Optim Test Data Management Solution offers proven capabilities to optimize test data management processes. Now you can create "right-sized" test databases with minimal cost and effort. Optim helps companies improve application quality, speed time to

New Aberdeen research reveals that customer data quality is a sales and marketing leadership issue. Aberdeen found that over 70% of Best-in-Class firms are driven to improve customer data quality by competitive and profitability pressures ... New Aberdeen research reveals that customer data

the business requirements for your Data Warehousing projects--including data integration and data quality needs. Getting started with data warehousing, and selecting the right technology, is not easy. Between collecting technical and business requirements, narrowing down the list of options and testing the technology -- just getting up and

IT organizations and business information owners deploy Informatica Identity Resolution in data quality processes or embed identity data matching capabilities into third-party or custom-built enterprise applications. Easily integrated with the Informatica platform, the software leverages the platform's ability to deliver near

Join us as we introduce our newest Quality Management offerings that transform the way people work together to deliver enduring software quality as a strategic business competency - to improve operational efficiencies. Join us as we introduce our newest Quality Management

alone; you need to understand the underlying quality of the data contents. Datanomic's data quality management software, dn:Director, enables disparate views of data to be unified for MDM initiatives. By deploying Datanomic's holistic approach, dn:Director enables users to understand, improve, protect and control data standards across

Data Profiling delivers a deeper and broader insight in a fraction of the time required by traditional approaches to data analysis. The need to analyze data is at the foundation of every effective data management strategy since the dawn of

to meet the demands of an aggressive growth strategy while delivering high levels of service quality. This white paper shows you how automation helped one organization meet the demands of an aggressive growth strategy while continuing to deliver high levels of service quality. Use this real-life example for tips on how this approach can

Management capabilities enable you to: - Improve development, build management, and quality - Accelerate release cycles through automated release management - Maximize productivity - regardless of team size or location - Minimize overhead through automation and repeatable best practices - Demonstrate compliance with

Real-Time Streaming Protocol (RTSP) is a foundation for the current convergence of communications. When implementing RTSP, the multiple port and protocol nature of RTSP makes streaming audio/video difficult to manage. This white paper explains it. The growth of the Internet