Celigo > Case Studies > Genomic Health Streamlines Revenue Recognition Process with IoT Solution

Genomic Health Streamlines Revenue Recognition Process with IoT Solution

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Technology Category
  • Application Infrastructure & Middleware - Data Exchange & Integration
  • Functional Applications - Warehouse Management Systems (WMS)
Applicable Industries
  • Finance & Insurance
  • Healthcare & Hospitals
Applicable Functions
  • Maintenance
  • Warehouse & Inventory Management
Use Cases
  • Facial Recognition
  • Picking, Sorting & Positioning
Services
  • System Integration
About The Customer
Genomic Health, Inc. was founded in 2000 with the goal of developing high-value diagnostics to enable more personalized cancer treatment decisions based on the genomic activity within a patient’s individual tumor. Today, Genomic Health is the leading provider of genomic-based diagnostic tests that address both the overtreatment and optimal treatment of early-stage cancer. The company has over 900 employees, with offices in the U.S. and Europe. Genomic Health operates within the insurance industry, which necessitates complex financial processes.
The Challenge
Genomic Health, a leading provider of genomic-based diagnostic tests, was facing a significant challenge with their financial process. The company operates within the insurance industry, which necessitates complex financial processes. Genomic Health had been using their data warehouse (EDW) to manage revenue recognition. However, the data warehouse did not work within the ERP system (NetSuite), which meant all revenue information was handled and stored within the data warehouse. Consequently, financial data needed for income statements and balance sheets had to be transferred from the data warehouse into NetSuite, using a home-grown process. This method soon became an issue for the insurance industry auditors who regularly review Genomic Health’s accounts. The company needed a better financial process that would satisfy the auditors, keep financial information within NetSuite, and allow the data warehouse team to access and pull the data they needed in the format required for their reporting.
The Solution
To address this challenge, Genomic Health implemented Celigo's integrator.io to streamline and simplify their revenue recognition process. All accounting information is now stored in NetSuite. Using NetSuite’s saved search feature, IT director Brian Weiss built custom integrations using Celigo integrator.io to extract data from the saved searches and securely transfer it to an SFTP site. The EDW team can access the data at their convenience, pull it into the warehouse and run their reports as needed. This new process not only satisfies the auditors but also suits the data warehouse team. The integrations were built to connect to specific saved searches, allowing the EDW team to pull the necessary data using unique columns, headings, etc., for each request. This ensures they receive the information the way they need it and in a format that they can use without having to spend time manipulating it.
Operational Impact
  • The implementation of Celigo's integrator.io has brought about significant operational improvements for Genomic Health. The new process satisfies the auditors and suits the data warehouse team, ensuring a smoother financial process. The EDW team can now pull the necessary data using unique columns, headings, etc., for each request, ensuring they receive the information the way they need it and in a format that they can use without having to spend time manipulating it. The turnaround time for changes to the integration has been drastically reduced from six months to a day or two. The system is also very stable, with consistent extracts and data transfers running nearly every day without any problems. Additionally, the integrations can be managed remotely using a cell phone, providing added convenience and flexibility.
Quantitative Benefit
  • The data warehouse team is now able to access and pull the necessary data in a format that they can use without having to spend time manipulating it.
  • Changes to the integration that used to take six months can now be done in a day or two.
  • The extracts and data transfers run consistently nearly every day without any problems.

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