Monthly Archive for July, 2009

The real-time advantage

Published on July 17, 2009 in Data Quality. 0 Comments Tags: , , .

Many companies have the information they need to make better decisions, they simply don’t have the means to deploy this information when and where needed.

In a world where real-time processing has become vital to commerce, companies who do not provide their front line systems and representatives access to the most current data and analysis are at a distinct disadvantage.  Shortcomings include:

  • Unable to make operation decisions with confidence
  • Capture invalid and inaccurate data
  • Increase risk exposure
  • Generate costly downstream “reactive” processes
  • Amplify customer dissatisfaction

Whether it’s the insurance company that’s unable to complete an on-line quote, the credit card company that offers the wrong rate, the business-to-business CSR who fails to make the right cross-sell offer or the retailer who captures the wrong ship-to address-real-time data quality makes all the difference. 

Organizations that employ today’s best practices enjoy benefits across the board.  Customers receive better access to account information, sales teams can take advantage of more opportunities, support staff can resolve problems faster at lower costs, and management can view consistent information across all lines of business. 

To help you add agility and confidence to your business, Pitney Bowes Business Insight has developed an in-depth webinar on real-time data quality.  Take a moment and learn how you can reduce costs, improve customer satisfaction and boost revenues.

New laws address taxing problems

Published on July 17, 2009 in Geocoding. 0 Comments Tags: , , , , .

Companies are responsible for collecting taxes on behalf of state and local governments, but some industries face more complex rules than others. The insurance industry is one such example.

While all states levy a statewide tax on most forms of casualty, property and health insurance premiums, several states go a step further and allow local governments to apply a separate “local government premium tax” ranging from 2 to 10% on insurance policies to help finance local government services. 

Keeping track of state, local and municipal tax jurisdictions can be a challenge-13 insurance companies doing business in Kentucky discovered that the hard way when they found themselves subject to a class-action lawsuit that cited tax overcharges and violations of state laws.

Many insurance companies rely on ZIP CodesTM to assign applicable tax jurisdictions, however most state and local premium tax assignments in the U.S. are based on specially defined boundaries that don’t necessarily match municipal or ZIP Codes.  Complicating this matter, hundreds of thousands of Zip Codes experience changes in names and boundaries each month. 

Some states are taking action. Under a recent change in state legislation, Kentucky now requires that insurance carriers use a verified geo-spatial database that appends the correct state, county, township municipality and premium tax district information to each customer record. Any insurance provider selling policies in Kentucky must begin using a verified system by January 1, 2010.

To learn more about these issues-including how you can leverage location intelligence technology for accurate, up-to-date premium tax assignments-read the in-depth Pitney Bowes Business Insight white paper on this topic.

Learning the Data Four-Step

Published on July 15, 2009 in Data Governance, Data Quality and Data Stewardship. 1 Comment Tags: , , , , , .

According to a recent SiriusDecisions Research Brief, 10-25 percent of the records for the average B2B company contain critical errors. The same study reports that 66% more revenue goes to the company with high quality data management.

So, why does good data make such a difference?

Bad data hurts your image, your operations, and your bottom line – and just gets worse over time. 

  • It means more returned mail and redundant processes, reduced access to postal discounts, and greater susceptibility to fraud.
  • It limits service quality, and that causes lower rates of customer satisfaction and loyalty. 
  • It impacts so many businesses and business areas from insurance to financial services; telco to utilities; public and private companies. What’s more, departments within these businesses – from customer service, sales and marketing; to billing and resource planning; to sales-force automation – are all data-reliant.

Companies today, however, are making marked improvements in data quality. Data Governance – an exercise of people, process and tools, typically a committee that represents every level of the organization, defines clear standards for data management, security and use – and Data Stewardship, an expanding role that ensures the business rules set up by the Data Governance committee data are enforced – play important parts in a four-step process that improves both data quality and data usage.  

1 – Access and integrate: All too often data is kept in a different database in every department – you want everyone across your organization to be, literally, on the same page.

2 – Profile and Monitor: Assess where you are, analyze your data and pinpoint issues. Determine what you have, where it’s coming from, and how it does-and doesn’t-work in concert.  As part of this assessment, determine where you can enhance your approach to Data Governance, and set firm rules and requirements going forward.

3 – Remediate: Clean up your data. Validate it. Standardize it. Match and de-dupe it. Enrich it with spatial, credit and/or marketing data that will enable your organization to use it better.

4 – Deliver/Federate: Empower your Data Governance Committee, specifically your data stewards to advocates proper collection, management and use of data.  First ensure it is fit for use, then ensure it used as intended.

With the new modular tools out there today that work across platforms and address each and every one of these steps in an integrated fashion, getting to better data quality is certainly getting easier. 

What steps are you taking to improve your data quality – and your bottom line? Comments and questions welcome.