Data Quality Management

“Don't think you're going to start getting valuable insights from data if you don't have any data quality process.”

David Loshin

“Organizations without information quality management are wasting from 10% to more than 20% of their operating revenue or budgets in process failure and information scrap and rework due to poor quality information! “

 Larry English

"Most organizations don't understand real quality management principles. Data quality is not just data cleanup."

 Larry English

“Ultimately, poor data quality is like dirt on the windshield. You may be able to drive for a long time with slowly degrading vision, but at some point, you either have to stop and clear the windshield or risk everything.”

Ken Orr

"Reason #1 for the failure of CRM: Data is ignored. Enterprises must have a detailed understanding of the quality of their data - how to clean it up, how to keep it clean, where to source it and what 3rd party data is required. Solution: Have a data quality strategy. Devote one half of the total timeline of the CRM project to data elements."

Scott Nelson and Jennifer Kirby - "Seven Key Reasons Why CRM Fails"

Data is a key strategic asset, and ensuring its quality has become a business imperative in today’s enterprises and they are increasingly seeking solutions to improve their data quality. An organization’s data comes from a wide variety of sources, including databases, external providers and the Web. The result is tremendous differences in formats and origins. Quality is often compromised and integration becomes impossible. Spending the money, time and resources to collect massive volumes of data without ensuring its proper management is ineffective and often leads to failed business initiatives.

Inaccurate or inconsistent data can hinder your company's ability to understand its current and future business problems. If poor data is not identified and corrected early on, this will lead to poor decisions that can cause a host of negative results; including lost profits, operational delays, customer dissatisfaction and also defective data will contaminate all downstream systems and information assets.

The quality of any analysis is only as good as the data upon which it is based. All too often, companies are finding that a variety of analyses still yield faulty results, because the data is not of high quality. Data warehousing, data mining, marketing automation, and other data-driven solutions cannot result in success and deliver attractive “Returns on Investment” unless data quality issues have been resolved in a sufficient manner.

“Global Data Management Survey” by PricewaterhouseCoopers found that 75% of companies suffered significant bottom-line impact from poor data quality. A recent survey by The Data Warehouse Institute (TDWI) estimated that data quality problems currently cost US businesses more than $US600 billion a year. More than 50 percent of data warehouse projects will have limited acceptance, or will be outright failures, as a result of a lack of attention to data quality issues, according to Gartner, Inc. Gartner also indicates that “Poor information quality costs organizations 10-20% of total revenue”.

An effective data quality strategy can help your organization better understand your business environment, allowing you to maximize profitability, reduce costly operational inefficiencies and increase customer satisfaction. The goal of data quality management is to provide the infrastructure to transform raw data into consistent, accurate and reliable corporate information and to help your enterprise achieve competitive advantage. Data quality is not only a glamorous topic, but it is definitely a management philosophy that affects your day to day business process.