In 2010, many business initiatives will bring attention to data quality, as the completeness and integrity of customer information can have a clear impact on near-term profitability. Previously, we examined how data quality affects marketing and operational efficiency. Today we focus on compliance and risk management.
8. Risk Management. While companies in the insurance, financial services and healthcare arena may face new regulatory oversight when it comes to risk management, organizations of all sizes will be looking for ways to mitigate risk in these uncertain economic times—especially when it comes to compliance. Million dollar fines, ongoing lawsuits and damaged reputations are just a few of the costs that can be mitigated when companies have confidence in their customer data.
9. Tax Management. With states and localities dealing with budget pressures themselves, there will be increased scrutiny on how businesses assess, collect and manage tax obligations. Unfortunately, tax jurisdictions change frequently, and jurisdictions are not aligned with ZIP Codes or U.S. census data—so companies will look to specialized applications in order to keep pace with ongoing changes, comply with government requirements and avoid penalties.
10. Fraud Detection. One effect of the recent recession is an increase in fraud. Many companies, including those in the financial services and insurance industries, are looking for ways to integrate fraud detection applications into their core business processes. Here, data standardization, address validation, location intelligence and the ability to match against “watch list” files can greatly reduce risk and exposure.
Whether your organization is looking to pursue one, two or all ten of these strategies, Pitney Bowes offers solutions that can help, including our SpectrumTM Technology Platform. As we start 2010, think about the ways you can leverage data quality to locate new opportunities, connect with customers and communicate more efficiently.
0 Response to “10 ways to maximize value of customer data – Part III”