The importance of Data Quality for Effective Relationships

Meta Group reports that enterprises now share 28% of their data warehouse data with partners, suppliers, and customers and forecasts more than 50% growth in this usage by 2002/03. For e-business collaboration, companies are also giving affiliates and customers access to ERP and other back-end systems via the Internet. Opening systems offers opportunities for faster product development and fulfillment and better relationships.

Or does it? There are risks in exposing internal data—and, fortunately, solutions for avoiding them and leveraging these opportunities.

Prevent Data Degradation with Enterprise Data Quality Solutions

PricewaterhouseCoopers’ “Global Data Management Survey 2001” found 75% of companies studied reported problems from poor data quality. Even if companies cleanse warehouse and internal systems data during data migration, data quality is vulnerable to degradation. New data enters daily from new and expanded internal systems and the Web (where input is uncontrollable), resulting in data issues.

For example, inconsistencies and typos in names, addresses, and product data occur, generating duplicates—such as Mark Atkins versus Mark Akins. Information is entered into the wrong fields, becoming buried. Errors, such as transpositions in product numbers, also increase.

These data issues can impede integrating all information on a supplier or customer—for example, Atkins/Akins. That means the company lacks a complete picture of this customer, skewing its customer count, profiling, targeted marketing, and online personalization. So it misses the opportunity to build the relationship.

Even worse, quality problems hinder customers and affiliates from getting accurate information from your company—and keep online transactions from being seamlessly processed. The result is mistakes, delays, and satisfaction issues. Furthermore, poor data makes a poor impression.

A one-time fix can’t solve these problems. Companies need an enterprise data quality solution. This involves:

• Implementing automated batch and real-time data quality processes enterprisewide—wherever data is deployed for new purposes, external data integrates with internal systems, and customers and affiliates search internal databases.;

• Regularly auditing data in critical systems.;

• Proactively modifying systems to parallel and support the rollout of new strategies.;

To do this job requires top-level sponsorship and an understanding of how information flows support the business as well as robust data re-engineering tools. Look for software that handles any data and provides mathematically-based matching technology, to ensure accuracy and completeness in finding related records.

Bridge the Communication Gap with Effective Product Search

Even with high-quality data warehouses and internal systems, customers, partners, and suppliers will run into roadblocks when looking for specific data because of a communication gap: different people use different nomenclature, categories, and descriptions.

For example, customers or affiliates may want notebooks. Your warehouse lists laptops. Customers may categorize a Palm Pilot as a hand-held computer. In your data it’s a PDA. Customers are looking for an AAA or triple A battery. Your records describe it as 9 volts or 9.

This gap wasn’t a problem when company representatives familiar with internal terminology and inventory mediated between customers or partners and your data. The reps could translate AAA into 9 volts based on the context. However, open systems are self-serve.

The good news is that technology can bridge the gap and interpret the context of words. Product search engines have evolved that deal with users’ errors, word variations, and divergent terms and categories. Look for search software that provides:

• Context mediation—for determining the business meaning of a word or value based on the context or associations of adjacent data.;

• Normalization—for transforming users’ terms to your terms and recognizing word variations and synonyms. Getting both sides of the search aligned facilitates matching. But it can’t eliminate all non-standard descriptions, so you need the next capabilities, too.;

• Fuzzy retrieval—for finding data without a precise key, such as a product number, or under conditions where data is inconsistent or missing.;

• Fuzzy matching and filtering—for measuring and ranking “possible” matches—to get the best one(s) and avoid irrelevant matches.;

Why take the time and trouble? So customers, partners, and suppliers won’t have to. Effective search is effective service. When internal systems are open, high-quality data and product search help to eliminate the risks of misinformation, make processes seamless, and build relationships that benefit your enterprise