Data touches nearly every part of an insurance agency. Agents rely on clean data to perform numerous tasks, from communicating with clients and offering them the best insurance policies from the most competitive carriers, to viewing a policyholder’s profile and identifying upsell and cross-selling opportunities. Dirty data makes it difficult for agents to perform such tasks. If clients’ policy information is filled with errors or duplicate profiles exist for the same client, it becomes difficult for agents to properly service clients.
More than ever, insurance agencies across the board are realizing that they need to invest in cleansing their data. Not only to better manage their current product portfolio, but also to monetize their data through new products and services as well as excellent customer experience.
Dirty data makes it difficult for agents and brokers to perform numerous insurance specific tasks, especially if their clients’ policy information contains errors, or if there are duplicate profiles for the same client – both of which are a common problem.
The problem compounds when agents resort to manually cleansing data. And, while a certain amount of manual checking and cleansing is important, most manual processes only fuel the creation of dirty data.
Another part of the data dilemma is that stored information often exists in isolation with no provenance and no way to determine when it was created, its original source system, or whether it had been combined with other data. A lack of system integration results in disparate data that is made worse over time and results in more mistakes, making data analysis and accurate reporting impossible.
It is difficult to definitively measure the true cost of poor data quality, but the UK government quotes The Data Management Body of Knowledge, which believes that organizations spend between 10-30% of revenue correcting data quality issues.
Some of the data challenges facing insurance agents and brokers include:
Decentralized, dirty data forces manual intervention that is, in itself, an error-ridden exercise. Using a Data Integration Hub (DIH) allows insurance agencies to automate many of their laborious, manual daily processes. Automation also allows agents to effortlessly validate policy details, endorsements, and coverage making the quote-to-bind process more accurate and efficient.
More than simply automating the manual processes, a Data Integration Hub has sophisticated error management capabilities that allow insurance agencies to catch duplicates and errors before data is moved to new systems, significantly boosting data quality throughout the organization. What’s more, it guarantees that data being pushed into third-party systems is clean and ensures the information returned is meaningful and actionable. Essentially, a DIH empowers agents to streamline and speed up data validation, integration, and orchestration.
Clean, reliable data allows agencies and their agents to become more agile, responsive, and cuts down wasted efforts spent qualifying data. Less money will be spent on E&O insurance helping to boost profit margins. If you want to learn how you can eliminate dirty data and maximize one of your most valuable assets – your data – contact Synatic today.
