Make Accelerated Data Ingestion a Reality in Benefits and HR
Three key learnings from this article:
- Employee benefits services firms solve their complex data ingestion challenges with Osmos.
- Benefits enrollment companies leverage Osmos features like Uploader and Pipelines to make accelerated data aggregation a reality.
- Whether it’s one-time data ingestion or a recurring data ingestion need, Osmos’s industry-leading solutions streamline processes at scale.
Streamlining data aggregation to simplify business
Service sector: Employee Benefits Services
Data ingestion challenge: Data transformation, data mapping, data cleaning, and aggregation
Customer need: accelerated data aggregation
Employee benefits services is a challenging sector. Much like financial institutions, they handle substantial transaction volume. Which means errors and failures are not an option. It’s their job to ease the burden on human resources departments and streamline the process of paying for employee medical, insurance, and financial benefits. That includes consolidated billing and invoicing, reconciliation, and custom billing solutions for customers who are often payroll service providers.
A prominent benefits solutions firm regularly aggregates customer carrier invoices into one online portal each month. Teams of operations analysts are responsible for extracting Excel/CSV files and then preparing, mapping, cleaning, and validating masses of data in Excel. Only then can they upload the "cleaned" data to their MS SQL database.
These team members are also tasked with data troubleshooting. When they discover errors in final reports, specialists must methodically retrace their steps to figure out what happened. This painstaking process is costly and time-consuming. As we know, lost time translates to customer pain.
Solution: Osmos empowers benefits administration firms to simplify business processes for employers, benefits brokers, and carriers.
Benefits providers and service firms successfully leverage Osmos Uploader to import carrier invoices for aggregation. Their internal teams trust Osmos to accelerate employee benefits billing and administration.
Discover data ingestion freedom
Service sector: Benefits Enrollment
Data ingestion challenge: Automating data ingestion
Customer need: Decoupling their developer resources from data ingestion
A leading health benefits aggregator provides care delivery, navigation, and advocacy services. They specialize in connecting individuals and families to the proper care, impacting decision-making at every stage of the benefits journey.
As a part of providing these services, the firm needs to ingest live data from various sources like healthcare partners, insurers, and customers’ HR systems. Each data source is unique and non-standard, often with poor data quality. This non-standard data needs to be validated and transformed into a standardized data model before it can be processed. This data ingestion workflow is tedious, involving manual cleanup.
Developers are often brought in to accelerate data ingestion by writing custom-built scripts. These are, by nature, one-off solutions for an individual vendor or partner. This costly process must be managed long-term, meaning the developers are permanently tied to the data ingestion process. This one factor can prevent an organization from operating at scale as their ability to acquire new business and manage data relationships is directly tied to their ability to hire developer resources.
Solution: Benefits enrollment and management firms leverage Osmos to automate the process of ingesting customer data from carriers and benefits administrators using self-service tools like Uploaders for one-time data ingestion and Pipelines for recurring data ingestion. Osmos gives benefits organizations the freedom to streamline the benefits enrollment process for customers by automating and improving the Benefits Enrollment process.
Should You Build or Buy a Data Importer?
But before you jump headfirst into building your own solution make sure you consider these eleven often overlooked and underestimated variables.
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