Prime 3 Software's client, a leading nationwide transportation corporation, sought to gather data from multiple systems to make business decisions on its service lanes.
The data was stored in multiple systems. The pricing data was being sourced from the ERP, while volumes came from operations, and cost/revenue was furnished by accounting. The data was not represented in the same format and was being reconciled by an application on the fly as it was fed to multiple systems.
Due to inconsistent data formats and multiple systems requesting the data, the dependent systems were not performant and the client could not accurately gauge the quality of a prospective price.
By working with the client’s IT team we recommended a data warehouse. The data is only being read, so there isn’t a transactional nature that complicates the querying of the data results. Using an Extract-Transform-Load (ETL) approach, the data was sourced from the various applications databases and normalized around the effective dates of the price. This gave the data the proper granularity for the user to dig deeply into the historical performance of the price and gave the user the ability to review the data with a number of tools — including business intelligence software.
The data is now reconciled nightly to provide consistent, accurate results. More importantly, the data is available to the application in milliseconds. This ensures client applications can be highly efficient and effective for integration and the overall user experience.
Normalizing the data in a data warehouse gives the client new capabilities to monitor and report on the performance of their prices throughout the life of the agreement. Before Prime 3's improvements, this capability was unavailable. It also allows the client to coordinate more closely with its customers in order to ensure service level metrics and agreements are upheld. The enhanced quality metrics provide a better overall picture of the business throughout the course of the agreement. Importantly, the client is in position to apply new technology explore pricing models that are data-centric and help grow the business.