Sponsored Content
How to Set Up a Winning Data Quality Management Plan
Susannah Adler (Sponsored by Melissa Data Corporation)
Insurers rely on data to inform many of their business functions, from risk assessment to underwriting to customer personalization and marketing. And, with 402.74 million terabytes of data created each day, there’s no shortage of information out there. But the amount of available data isn’t the issue; rather, it’s the quality, and maintaining high-quality data can be quite a challenge.
Savvy insurers can escape this challenge relatively unscathed by keeping a closer eye on the information they use, examining sources and putting in place tools and processes that ensure better-quality data. It all starts with a data quality management plan, some technology tools and a little help from the experts. As the saying goes, “Failing to plan is planning to fail,” and this is one area insurers can’t afford to overlook.
The data management challenge
Bad data can come from any number of sources, such as external customer-facing channels (inaccurate data collected on website forms) or internal errors in manual data entry. Rapid data decay is yet another source of bad data: third-party lists can introduce outdated information, such as old email and physical addresses and phone numbers.
In fact, Melissa Data Corporation estimates that almost one-quarter of contact data passing through its own servers is either incorrect or stale within one year. In addition, department “misalignment” can create multiple conflicting versions of data, and working to mitigate these conflicts can impact operational efficiency. Altogether, bad data costs organizations an average of $12.9 million annually.
Given the bad-data landscape, it’s critical for insurers to have in place a data quality management plan that provides a unified source of truth across the business and that adheres to six pillars: accuracy, completeness, consistency, uniqueness, validity and conformity.
Crafting a winning data quality management plan
So, what does a winning data quality management plan look like, and what are the steps to create one? It starts with assembling a cross-functional team — project manager, assessor, marketing/IT experts, database administrator — to conduct a data audit. Next is defining clear goals and then scoping the assessment, focusing initially on customer and contact data. The final step is to audit all data sources for accuracy and completeness, identifying leakage points, testing solutions and implementing the system with a continuous improvement framework that tracks metrics over time.
Technology plays a critical role in overcoming the hurdles that bad data introduces. Insurers that use real-time cleansing tools to verify and standardize data at its point-of-entry can reduce errors and cut waste. Address autocompletion boosts efficiency and improves the customer experience, while batch processing and periodic cleansing help update existing records. For IT, high-quality data enhances AI reliability to eliminate typical biases, which improves insights.
Insurers don’t have to navigate through data quality management alone. Partnering with a data quality specialist will bring peace of mind as well as deep domain knowledge and expertise. This, along with a winning data quality management plan, will ensure better business outcomes for today’s insurers, and will position them for leadership in competitive markets.
