Building a Foundation for Data Quality Success

InfoManagement Direct, April 25, 2008

Bob Hagenau

Businesses need quality data that provides complete and actionable insight to help them address a wide range of issues across departments and lines of business. Unfortunately, enterprise data is constantly changing. Companies have to deal with tens of millions of daily changes in customers, partners and equity markets, along with high organizational and competitive fluidity and strict and evolving regulatory compliance requirements.

Staying on top of these changes and requirements is a tremendous challenge: just when one marketing database is cleaned up, a new campaign brings in all-new leads. As soon as one system migration is completed, another company is acquired – and so on. To be successful in this environment, companies must build a strong foundation of people, process and technology to ensure data quality.

Although regulatory compliance is often cited as the primary driver for data quality initiatives, according to AMR Research, a global view of customers, suppliers and materials could save businesses $1 trillion.¹ To obtain this global view, companies are looking to improve data quality across sales, marketing, human resources, procurement, manufacturing, distribution and finance.

Examples of how companies can use data quality to address issues beyond compliance include improving cash flow, customer profitability and manufacturing quality.

• One of the world’s largest freight carriers wanted to reduce days sales outstanding by reducing the number of invoices kicked back by customers for correction. The freight carrier was having difficulty correctly calculating shipping rates because rates are based on a customer’s aggregate volume, and many customers did business with more than one of the company’s five autonomous business units - and each business unit used a different customer code in its freight management system. To resolve this problem, the freight carrier started an initiative to improve correlation between divisional data and higher level corporate codes.
• A large retail bank wanted to improve profitability of business customers. In order to get a more accurate and granular understanding of the factors driving profitability, the bank had to match customer records across deposit, loan, retirement and call center systems with D&B information. The need for detailed profitability analysis was the impetus to address data quality issues such as alternative spellings, abbreviations, historical addresses, name changes, trade names, incorrectly used fields and data entry mistakes.
• A global consumer appliance manufacturer wanted to reduce warranty costs and product returns. It started an initiative, Right the First Time, to eliminate introducing poor quality products into the market by sensing and responding to production nonconformance at the time of occurrence. Data quality was a critical component of this initiative because only the extensive analysis of data across multiple systems could determine whether poor product quality was caused by product design, raw material or production plant issues.

Creating a Foundation for Data Quality Success

Data quality is not a new concept, but has evolved over the years. In the 1990s, data quality was about consolidating data from multiple systems into a data warehouse to facilitate reporting and analysis. This was a very IT-focused project using extract, transform and load (ETL) and metadata management technologies for data profiling and integration. Once data warehousing projects were completed and companies started generating reports, they realized there were many errors, missing or incomplete data, and duplications in the source systems. In order to address the garbage-in-garbage-out problems of data warehousing, companies started new IT projects using cleansing and enrichment technologies.

Now companies have realized that in order to take data quality to the next level, they need standard definitions of business entities like customers, products, suppliers and employees. This growing awareness is being driven by data management issues such as mergers and acquisitions, information as a service and regulatory compliance. This requires master data management technologies to introduce formal data governance processes that are defined by cross-functional councils and implemented by data stewards who understand the business context of information. The most successful companies have moved data quality from an IT project to an ongoing business program with executive commitment and formal program funding as part of the corporate budget.  

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