Insurance agents rely on access to clean data every day to communicate with clients and offer the best insurance policies from the most competitive insurance companies, to view a policyholder’s profile, and to identify upsell and cross-sell opportunities.
Dirty data — caused by things such as manual entry errors, incomplete data conversion projects, or even duplicative profiles incorrectly migrated from one system to another — makes it difficult for agents to properly serve clients.
More than ever, insurance agencies are realizing investments must be made in cleansing internal agency data, not only to better manage current customer and product portfolios, but also to better monetize data through the addition of new products and services as well as to provide an excellent customer experience. Insurance agencies also are increasingly aware of which insurance carrier partners have the best data and are able to provide unfettered access not only to underwriters but agents as well.
Dirty data hinders agency efficiency
Dirty data makes it difficult for agents and brokers to perform numerous tasks, especially if a customer’s policy information contains errors, or if there are duplicate profiles for the same client. Both scenarios are common. The problem compounds when agents resort to manually cleansing data. While meant to be a solution, these additional manual processes only fuel the creation of dirty data.
Another part of the data dilemma is that information stored in agency management systems (AMS) 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.
Some of the data challenges facing insurance agents and brokers include:
- Data silos with poor rules will result in inaccurate dates, account numbers, and personal information, all stored in multiple formats. This makes reconciliation difficult and automatic reconciliation the stuff of dreams.
- Dirty data diminishes the return on investment (ROI) on a company’s IT investments, including the AMS, customer relationship management (CRM) system, and other InsurTech. This makes agents’ jobs increasingly difficult and diminishes output to the point of putting the firm at risk of losing valuable skills as frustration mounts. Perhaps most worrying, is the resulting loss of confidence in the foundational business data, putting the agency’s reputation and future at risk.
- Missing, incomplete, and inaccurate data can lead to incorrect client quotes being generated, under or overvaluing of coverage required by a client, and sluggish customer service as agents base decisions on dirty data. Dirty data influences the ability to get quality outputs from InsurTech partners, like Fenris or Relativity6. These trusted service provider systems apply artificial intelligence (AI), such as machine learning (ML), and enrich insurance data, allowing agents to optimize interactions across the customer journey, from quote to upsell and cross-sell interactions. However, if agencies supply dirty data, it must be expected that the information returned will be inaccurate (garbage in – garbage out for the old school computer geeks out there), limiting any potential competitive advantage.
Clean data with automatic availability
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 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 agencies to eliminate duplicates and reduce errors before data is moved to new systems, significantly boosting data quality throughout the organization. What’s more, it guarantees 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 to become more agile, responsive and productive. It also helps cut down wasted efforts spent qualifying data. Ultimately, clean data means less agency money will be spent on E&O insurance, thereby helping to boost profit margins and ensure success of the business.
Jamie Peers is a vice president for Synatic, which provides a modern Data Integration Hub (DIH) that enables enterprises to iterate quickly, by unlocking and optimizing data across multiple services and systems. He can be reached for further comment or information via email at [email protected].