The importance of data quality for data warehousing and CRM |
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By Curtis Hall
17 Feb 2005 | Cutter Consortium, special to SearchCRM.com |
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The impact of dirty data
Poor data quality can impact an organization in many ways. It can result in misguided marketing promotions being sent to the wrong address with incorrect information -- including improperly spelled names, title and company. To a prospective customer, your company looks stupid. For you, it means that you've wasted time and money targeting a customer who really doesn't exist. But most unsettling is that it means that the integrity of your customer database(s) and other customer information systems is suspect.
With the current focus on customer relationship management (CRM), data integrity has taken on even more importance. In short, with so much information streaming into and out of organizations from call centers, customer Internet forms, point-of-sale contact information, Web-based applications and other customer information systems, data quality and integrity should play an important part in your data warehousing and CRM efforts.
Data quality: An ongoing concern
For data warehousing and CRM, data quality issues first arise during the initial application design stages when requirements for extracting and transforming data from operational systems are developed. However, data quality issues do not stop here; they remain an ongoing concern throughout application development, use and maintenance.
Specialized data cleansing and enhancement tools
These tools are designed for cleansing and enhancing customer files consisting of name, address, Zip code and other customer (and business) information, and for matching, correcting and consolidating multiple database entries (i.e., customer and/or firm names) across different databases and file systems. As a result, they are quite useful for direct marketing efforts (both via regular mail and e-mail) and other CRM-related operations.
Options
Companies have several options for employing data cleansing and enhancement in their data warehousing, CRM and e-business operations. They can purchase a product outright and install it on site. Onsite deployment offers the most flexibility for enforcing data integrity throughout the organization. But it also means having to deal with implementation headaches as well as having to train and allocate staff to use the tools.
Another alternative is to use online services. Such outsourcers provide general data cleansing services for cleaning up customer information files. More important, they offer more advanced data cleansing and enhancement techniques that can deliver content from various third-party data suppliers on a record-by-record basis at the point of customer contact in real time over the Internet.
Such online services are useful for standardizing and improving customer and prospect databases by deduplicating records and by geocoding, appending and enhancing files. Working on a per- transaction pricing model, companies can send customer information one record at a time over the Internet, where it is corrected and appended with geomarketing and householding data so that it is possible to view customer information down to the neighborhood or household level.
Organizational data quality standards
Achieving enterprise data quality requires establishing some sort of departmental or organizational standards to help maintain data consistency and quality. This involves getting the business people responsible for creating or manipulating the data, such as end users, entry people, auditors, etc., to assume a sense of ownership for data quality.
This last requirement is probably the most difficult to plan for and implement because it relies to a large degree on changing human nature (as opposed to systems management techniques), and because business processes are distributed throughout the organization.
Conclusion
Data quality is an important issue that should be accounted for starting with initial application design through implementation, maintenance and use. The good news is that companies now have a number of options in the way of products and services to help them get a handle on their data quality problems. These range from installing software on site to the use of online interfaces to third-party data suppliers.
Curt Hall is a Senior Consultant with Cutter Consortium's Business Intelligence Advisory Service and author of its survey-based Executive Updates. Mr. Hall is coauthor (with Paul Harmon) of Intelligent Software Systems Development: An IS Manager's Guide; a contributing author to James Martin and James Odell's Object-Oriented Methods: Pragmatic Considerations; and the author of Cutter's Data Warehousing for Business Intelligence.
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This article was originally published by Cutter Consortium
www.cutter.com
© 2004 Cutter Consortium. All rights reserved. Reproduced with permission.
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