November 16, 2012

Insurers are currently optimizing their workforce to gain efficiency and productivity from employees. Workforce optimization strategies include many components designed to better route requests, provide enhanced intelligence and better equip organizations to meet the needs of policyholders.

There are very sophisticated technologies available to help insurers meet workforce optimization goals. These systems analyze data and provide intelligence to help individuals make better decisions faster. Unfortunately, these advanced systems are only as good as the information supplied to them.

Data quality is a problem that plagues insurers. A recent Experian QAS study found that 92% of organizations suspect that their customer or prospect data might have inaccuracies. On average, respondents fear that as much as 25% of information is inaccurate.

If only 75% of the information in a given insurer's database is accurate, a sophisticated workforce optimization system may not be able to make the right calculations. Inaccurate intelligence hurts the efficiency of employees and causes a negative customer experience.

Even though insurers have data quality issues, these projects are frequently placed at the back of the line for IT projects. While data quality certainly isn't as interesting as workforce optimization or advanced analytics, it is something that must be considered to ensure the success of those and other projects.

In order to make the most of these systems, insurers need to get back to basics and clean the information contained in their database. Insurers frequently have problems with inaccurate contact data, duplicates and outdated information. However, there are steps insurers can take to improve accuracy for each of these scenarios.

  1. Inaccurate contact data: Insurers are typically large organizations with many employees and external agents entering policyholder or prospect contact information every day. With so much free-form data entry taking place constantly, errors plague databases. In fact, human error is cited as the top reason for data quality issues.

    To correct inaccurate data currently contained in systems, insurers need to standardize and fix existing records through software tools. Data can easily be transposed to a set of standards and checked for inaccuracy. Inaccurate information can then either be corrected or flagged for employee rework.

    This exercise not only improves information feeding workforce optimization systems, but also strengthens communication channels with policyholders.

    1. Duplicate records: Duplicate accounts are problematic for insurers. Often, Underwriting has an initial record for a policyholder, then Policy Services maintains a different record, and Claims may or may not have a listing for that same policyholder.

      Given this data landscape, eliminating duplicates can be difficult. However, standardized contact data can be an ally. Since contact information is typically found in every database, it can be used to help household information and identify duplicate contacts.

      It's worthwhile to select matching elements to determine where duplicates are found. Insurers need to determine the level of matching they want to accomplish, as well as the tolerance level for what is considered a duplicate record.

      1. Outdated information: Personal information is very fluid and changes on a frequent basis. Policyholders are constantly moving or experiencing other major life events and often do not inform insurers of these changes. Because of these factors, contact records may become outdated quickly.

        Contact data can be updated with current information. First, address information can be updated. The USPS has a National Change of Address file that businesses utilize through third party vendors. Organizations can match existing contact records and then update these records based on the Move Update file.

        Additionally, email address information can be updated. Third party vendors can remove syntax errors and check to see if an email address is still active. Updating this type of information is beneficial, as it aids in creating a database of consistent fields and updated records.

        Once information is clean within a database, insurers need to shift focus and make sure all incoming information is accurate. Verification tools can be put in place that use sophisticated matching techniques to identify accounts within a database and cleanse any contact information as it is being entered. This ensures that information stays clean and immediate business processes are efficient and accurately informed.

        While it is important to drive intelligence and efficiency, insurers need to take a step back and guarantee the information feeding these systems is accurate. Clean data improves the accuracy of sophisticated workforce optimization systems, which helps insurers get the most out of their investment.

        About the author: Thomas Schutz is SVP and GM for Experian QAS North America, serving as the company's top executive for all strategic business decisions in the United States and Canada. He be reached at thomas.schutz@qas.com.