TRENDS IN DATA QUALITY & BUSINESS PROCESS ALIGNMENT

November, 2011

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By Forrester Research

Commissioned by Trillium Software

Introduction

Enterprise organisations indicate that they place significant importance on data quality and make a strong effort, both financially and operationally, to ensure that data is supporting business process improvements. Today, there is movement toward investing and implementing data quality management technologies and best practices as the majority of companies believe their data quality management maturity is below average. This profile examines why data quality is critical to core business processes and functions in North American enterprises of 1,000 employees or more and reveals ways these organisations implement data quality improvements today — and through what delivery mechanisms. This market demand is pushing companies to adopt data quality, MDM, and other complementary technologies for a variety of use cases.

Strong Interest In Data Quality Impacted By Poor Business Process And Data Alignment

According to Forrester Research’s November 2010 Global Master Data Management Online Survey, 90% of North American enterprises are interested/considering, implementing/implemented, or expanding/upgrading data quality management technology in the next 12 months (see Figure 1). Business drivers for data quality implementation are plentiful and often differ based on industry and business sponsorship. No matter the industry, many organisations build their business case for data quality investments to increase revenue through improved direct marketing and account management, reduce costs through improvements to operational efficiencies, and mitigate and control regulatory and financial risk. Highly regulated industries, such as financial services, banking, insurance and healthcare, often use compliance and risk management as an entry point to their data quality strategies, while other commercial sectors, like manufacturing, retail, consumer packaged goods, and technology, look to improve business performance and efficiencies while reducing costs with better quality data.

 

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