I chose to do a client-based project on the company I work for, Billhighway. Billhighway has an abundance of potentially valuable data but is stored in raw transactional silos. Reporting is accomplished by creating static, paginated grids of data backed by complicated queries on the transactional schemas. The focus of this paper is to increase data fluency within Billhighway by demonstrating how to actualize value by turning data into information and information into actionable insights.

Two (2) approaches were taken:

1) Use traditional business intelligence (BI) methods to turn data into information and use the information to create self-service, interactive dashboards.

2) Create time-series forecasting models using machine-learning principles to predict future member balances. The goal is that the demonstration of these real-world, working examples will be part of a catalyst that initiates a data fluency reaction, benefiting both external clients and internal personnel.