Customer data, and the quality of that data, is a companys most important asset. With every interaction, customers provide critical information. In todays market, its paramount to capture this data accurately, completely, consistently and in a timely fashion, while ensuring that everyone using the data has a common understanding of what it represents. Accurate data matters. When customers misdial, they may reach the wrong party or no one at all. For communications companies, the cost of poor data is much greater, resulting in poor investment decisions, lost customers, missed opportunities and regulatory penalties. Organizations are driven by activities, information and results captured across the enterprise in the form of data that is updated and edited so frequently that it seems alive. Similarly, poor customer data acts like a virus and holds companies back. In many businesses, the underlying data problems have gone unchecked for years. Data entry errors by both employees and customers, along with inconsistencies, contribute to an organizations data quality problems. Few companies take action to address data quality initially, but these upfront actions ensure that only high quality information enters the database. Most companies deal with these upfront quality issues ineffectively. Many, for example, have point-of-use systems that cleanse data in batch processes. Common instances include postal address corrections during mail production or running a product penetration analysis for a marketing campaign. The problem is that the back-end data remains unchanged - updates are lost, data quality erodes and information assets lose value. In the real world, customers move, household situations evolve and services are modified. Rarely are these changes captured consistently across organizations and, without a proper maintenance program, data quality degrades. In many communications companies, data quality is the responsibility of the IT department. While IT must play a role, the fact remains that poor data is not an IT problem - its a business problem. Every customer experience, every decision and every touchpoint depends on high-quality customer data. Marketing must understand the true extent of a customer relationship, but data quality also plays an important role in service optimization, network planning, customer service and operations. A team approach, where business users own the rules and IT owns the applications, helps ensure that data is always fit for use regardless of venue. Enterprise data quality, however, does not require a boil the ocean project, and some groups have difficulty getting initiatives off the ground due to overscoped early efforts. By working with a team that understands the value of high quality data in an area that makes measurable business impacts, companies often gain the early traction needed. One data field known for poor quality is customer address. It is one of the most costly, as address errors affect billing, service delivery and customer relationships. Street name suffixes, postal codes and apartment numbers may be irrelevant for some customer addresses, but vital to others. While street name changes are rare, hundreds of thousands of ZIP+4 codes change each year. Most importantly, customers and prospects move. More than 40 million Americans change their address annually, making it difficult for companies to maintain high quality mailing lists. In the U.S. alone, nearly 10 billion mail pieces are undeliverable as addressed each year and many more are delayed. For companies in the communications industry, poor data quality leads to delayed cash flow, customer dissatisfaction, higher postage and increased return mail costs. As competitive pressures rise in todays economy, communications firms are leveraging data quality initiatives to boost sales and revenue in three distinct areas - target marketing, distribution and cross-selling. Address information is essential for effective targeting. With high-quality data, customer intelligence tools go beyond who and where to provide rich demographic, behavioral and purchasing data. This helps marketing teams to cost-effectively pinpoint pockets of opportunity. For distribution, given the cost of retail space, economic pressures and competitive landscape, the need to re-examine networks has never been greater. Accurate customer data provides the means to forecast sales, evaluate alternative sites and improve results in underperforming stores. In business-to-business environments, accurate data makes it easier for communications carriers to optimize sales territories by measuring product penetration across markets - increasing both productivity and share. To cross-sell means to understand the complexity of todays customer relationships, as companies not only encompass traditional local and long-distance telephone service, but also wireless communications, high-speed Internet and entertainment. Understanding household relationships and product penetration is the difference between maintaining a standalone relationship and making a triple- or quad-play. Increasingly, data quality is scrutinized by state and federal regulators. The Mobile Telecommunications Sourcing Act (MTSA) is one of several regulations that impact how communications firms must assign and collect taxes from their customers. Incorrect tax jurisdiction assignment subjects companies to possible class-action lawsuits or fines - and high-quality customer data is essential to alleviating these risks. As of November 1, 2008, communications firms that bill customers for services are subject to the FTC Red Flag Rules, and must implement a customer protection program. This program must be able to detect a red flag, which is a pattern, practice or specific activity that indicates possible identity theft. While data quality initiatives come in various sizes, nearly all effective programs encompass five best practices data profiling, data governance, back-end cleanup, interactive processes and maintenance.
You dont know what you dont know - and that is certainly true in data quality. Documenting the state of a data quality program helps garner the executive attention required to move forward, as actual data quality often falls short of expectations. Even in companies that have written standards, it is common to find inconsistent rules in place. Required fields may be populated, for example, but proper analysis indicates the presence of false data with sequential Social Security numbers (123-45-6789) and phony email addresses (email@example.com) being common problems. Misuse of data fields is equally problematic, as individuals enter departments into name fields or delivery instructions into addresses. Data profiling is an easy, cost-effective, eye-opening experience.
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