CASE STUDY

Optimizing Member Communication with a proper Data Model

The Challenge

A growing regional healthcare payer organization had no internal resources to help their marketing department consolidate membership data for their Member Preference Center. At the time, member preferences were isolated within 4 disparate systems. Because of this, there were increased difficulties when it came to the systems communicating with each other. Due to a lacking data model, member contact information and communication preferences were not up to date which caused data to become stale. Also, the absence of proper standardization practices often meant that the data conflicted with each other creating headaches for both the marketing team as well as their members.

The Conflict?


The marketing team at a prominent health insurance company had no means to consolidate member contact preferences due to siloed data. This negatively affected communication between this group and their 1+ million members.

The Solution

Our data teams determined that by integrating all data sources into a centralized location and creating a data hub to house all up to date member communications that this will improve data quality but also optimize the communication between the marketing team and their members.

Our architects were able to successfully create a large-scale relational database utilized a 3NF schema which allowed for normalizing principles to reduce the duplication of data, avoid anomalies, preserve its integrity, and simplify data management. This sophisticated data model also permitted for reporting and analyses of the data to be accurate and trusted.

Technology Used

SQL Server, Oracle, Informatica & Power BI

The Result

With the proper data model and architecture in place, data assets are now being housed in a central repository allowing for a single source of truth. The success of this project has equipped the marketing team with a 360 view of their members. Having this ability allows the group to focus their campaigns holistically. This unified infrastructure also gives access to other service lines of the organization empowering more cross-departmental collaboration. As a result, the organization has seen a decrease in input costs and an increased confidence in their services from users and, just as important, their members.

Contact Us

Retail

Predictive Analytics Improve Business Outcomes for Retailers

If data is the new business currency, then predictive analytics are the means in which organizations can take control of that currency to maximize its benefits.
Retail

Artificial Intelligence (AI) Use Cases for the Retail Industry

Overall spending on Artificial Intelligence (AI) systems is projected to reach $79.2 billion in 2022, which is more than double the amount spent in 2019.
Retail

Automation Solutions Closes the Gap on Last Mile Delivery for Retailers

According to a recent retailer’s report conducted by Blue Yonder, only 14% of the 300 executives surveyed say their fulfillment locations are fully automated.