Insurers are often responsible for handling large volumes of records to make informative decisions that will impact a wide range of industries. Capturing and properly utilizing this accumulated information is how agencies make these important choices. However, if data quality
is poor, these organizations may make the wrong conclusions and negatively impact businesses all over the world.
Data quality tools need to be able to acquire information with the correct levels of granularity to improve the odds of insurers making the right decisions, according to a report by Property Casualty 360. If the records are broken down too much, then they are too difficult to analyze. Meanwhile, if the data is too generalized, agencies will not be able to decipher important industry-specific drivers. As a result, analysis tools must tread a fine line and be able to capture information in the right stages.
According to Property Casualty 360, businesses can benefit from first deploying a highly generalized analytic solution to create a "baseline" for the industry. Insurers can then break down data by business, region or channel to acquire more specific information used to make decisions.
Leveraging this information is the next step for insurers to create policies and programs that match a customer's needs. Data repositories and analytic tools should be combined into a single ecosystem to guarantee the successful analysis of information, the news source reported. In doing so, organizations won't need to worry about data loss or inadvertently using the wrong records for a specific project.
According to the Data Warehousing Institute, data quality is now essential to organizations of all sizes, as they continue to trudge through the unpredictable economy and try to gain a competitive advantage over rival firms. Knowing the customer is one of the best ways to guarantee insurers are using high-quality information.
More organizations around the globe are creating data quality practitioner positions in an attempt to guarantee they use the highest quality of information when conducting analyses on trends or market behavior. If poor records are used during evaluations, organizations will suffer consequences that can lead to revenue losses or customer satisfaction deteriorations, regardless of the technology or methodology used.
The use of high-quality data is even more important for insurers, as the decisions made by these agencies span across business barriers and affect entire industries.