With the onslaught of Big Data and more sophisticated business intelligence and analytics, data quality will be more important than ever to insurers in 2012. The experts say you can't get there without clean, accessible data. Poor contact data affects a variety of operations, from underwriting to policy service. But from a cost perspective, inaccurate contact data can have a significant impact on insurers. In a September 2011 report "Why is Data Quality Important for Insurers?" Deloitte lists four reasons:
1. Poor data quality is expensive: 10 percent of revenue.
2. Management needs valid, complete, consistent information.
3. Controlled data quality is mandatory for compliance.
4. Good data quality helps increase customer value.
Data is coming at insurers from an increasing variety of non-traditional sources. In fact, results from a survey of 100 insurance industry respondents connected with data management across IT, marketing, operations and finance reveal mobile and social media as growing data capture points. Eighty-four percent of the respondents to the survey-"The Dilemma of Multichannel Contact Data Accuracy" from Experian QAS, a division of Experian Marketing Services-currently capture customer contact data through mobile applications. Ninety-six percent see the use of mobile platforms growing, and 95 percent communicate via social media.
An Abundance of Data
As if ensuring accurate, clean data and managing it wasn't already a big enough challenge, these new, emerging sources are making it even more difficult. With new collection points, insurers are struggling to achieve accurate contact data. For survey respondents, the median percentage of inaccurate data in an organization's existing database was 35 percent.
Church Pension Group (CPG) recently initiated a master data management program as a result of an abundance of data. "Because we have different lines of businesses, we have different transactional systems those business units use," said Danette Patterson, manager, Enterprise Data, at the insurer, which provides retirement, health and life insurance benefits to Episcopal clergy and lay employees. "Our first step was getting a centralized database in place, using Oracle's Customer Data Hub (CDH) to manage that data and exchange it across systems for elements that we have in common."
Like Church Pension Group, other insurers have taken notice. Experian's July 2011 report reveals that 90 percent of the respondents plan to invest in initiatives related to data quality "in the next 12 months." So, by our calculations, right now.
According to the report, the reason for the inaccurate data appears to stem from the way insurers clean it-manually. Only 27 percent of respondents currently use in-house software tools to cleanse contact data. The most popular tools are point-of-capture address verification, back-office software tools for existing data and e-mail verification.
Another technology that helps insurers refine data for use is de-duplication, which employs sophisticated algorithms to weed out redundancies in data and compress it to a fraction of its former size. Major storage vendors IBM, EMC and NetApp offer de-duplication within their product lines, but take different approaches. There also are third-party vendors that will de-duplicate data for carriers as a service.
"Technologies must be selected according to the business case," says Kurt Hausermann, senior manager, enterprise risk management at Deloitte AG and author of the Deloitte report. "A profiling tool, which shows the content of your data, is a good starting point. Cleansing tools help you to standardize data and remove duplicates (e.g. in master data or marketing data)."
However, technologies, such as de-duplication, data profiling and data quality assessment tools, are just one of three dimensions in a successful strategy, according to Hauserman. Two other dimensions-data risk management and data governance -have to exist.
Also, before looking to tools, insurers need to consider different approaches to data quality, two to be specific-traditional and emerging-according to Mark Gorman, CEO and founder, The Gorman Group Insurance Consultancy.
In his report, "From the Backroom to the Boardroom: The Evolution of Data Quality in the Insurance Market," Gorman indicates that in the traditional approach the focus is on fixing data quality issues. Issues are identified after reports have been generated and before the reports are shared with a broader audience. In the emerging approach, data is recognized driving information for applications, such as predictive analytic models, management dashboards, revenue and expense forecasting, account profiling and segmentation, etc.
The appropriate approach varies among organizations, but Gorman says picking one is the first step to a successful data quality environment. "An organization can do both [traditional and emerging], especially if siloed," Gorman says. "However, the emerging method, once implemented, requires senior management support and will take precedence over time."
In developing its MDM program, CPG started with tools but discovered that every unit has different processes, policies and data validations and verifications that may not correspond to the other business units. "We found different things that we needed to establish to ensure that the data was cleansed and maintained appropriately," Patterson says. "From that we established a centralized team to manage that data and build processes and rules identified through data mining, monitoring and reporting. You need to form various committees and an organizational structure that has a certain hierarchy in order to ensure that all of the units are aligned, especially when you have so many lines of businesses."
