Tuesday, September 7, 2010

Data Quality ROI

What’s Your Company's Bottom Line on Data Quality?

Poor data quality is a significant problem for many organizations as it can lead to inferior customer service, reduced employee productivity, lost sales opportunities and increased marketing costs.

• What tangible or bottom-line benefits will be realized as a result of data quality management efforts?
• What will data quality management cost?
• What’s the organizational impact of a data quality management project?
• What’s the downside of not pursuing data quality management?
• When is the best time to engage in new data quality initiatives?
• Is data quality management a stand-alone initiative, or should it be pursued in conjunction with other projects, such as customer relationship management?

Ten Good Incentives for Data Quality:

Among the key benefits are improved customer service and relationships, more efficient operations, lower costs and more revenue-generating opportunities. All of these benefits contribute to return on investment.

1. Back-office improvements. Data quality management can lead to benefits such as unified billing, accurate revenue accounting, accurate contract billings, unified credit management and reduced mailing costs.

2. Reduced costs of direct mail/marketing. Companies can decrease the costs associated with direct mail and marketing efforts by having more accurate data.

3. Reduced operating costs. Improved customer data and the consolidation of that data into a single source can lead to significant savings in operations costs for companies.

4. Faster and more accurate billing. Operating a central resource to manage data quality rules, taxonomy, and process enables organizations to keep customer information accurate, consistent and up-to-date, helping to ensure that invoices and other mailings get to customers in a timely manner. And, collections can be expedited with more accuracy in customer information.

5. More effective cross-selling and up-selling. With more accurate and reliable data on customer preferences, interests, and demographics, enterprises can more effectively cross-sell and up-sell their products and services to customers.

6. Better relationships with customers. Organizations with more accurate and reliable information about customers will have better relationships with those customers.

7. Improved customer service. Having accurate and timely information on customer preferences and concerns is vital to providing top-notch service.

8. Front-office improvements. Organizations can unify the corporate Web site with back-office systems, ensuring consistent data from all sources.

9. Improved regulatory/compliance efforts. With greater assurances that customer data is current and correct, companies are more likely to be compliant with government regulations.

10. Bolster privacy efforts. An effective data-quality and customer data management effort can help protect the privacy and security of sensitive customer data.

[source: http://www.oracle.com/master-data-management/roi-from-data-quality-white-paper.pdf]

FTI Expert Data Quality Assessment Strategy

Having high quality information about customers, and their needs, enables enterprises to respond proactively and deliver the products and services customers want. Keeping this data timely, accurate and easy to access throughout the enterprise is a key to improving business success.

Achieving data quality management should be an active strategy for IT executives, CFO's and a high priority goal of organizations looking to optimize heir business operations. When looking for the best investment to fit your organizational needs and also your budget, talk to FTI for honest answers and feedback on the current industry standards and options.

Freelance Technologies, Inc
888-970-9944 (Toll-free)
fti@freelancetech.com

1 comment:

  1. Great post. This is a great resource for anyone trying to decipher the ROI on their data quality. We have a community for IM professionals (www.openmethodology.org) and have bookmarked this post for our users.

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