Altair Engineering, Inc. has formed a strategic partnership with Digital Commerce Technologies Inc. to resell and provide implementation services in the Americas for HiQube, the company's optimization-driven, data analytics solution.
Since solidifying its partnership with Altair Engineering, DCTI, a global information technology services provider, made its first sale of HiQube to Iowa's Cedar Falls Utilities (CFU). The driver for the sale was HiQube's ability to easily capture and analyze over 10 years of utilities usage data from legacy sources within its unique multi-dimensional engine. The deployed solution provides CFU far deeper insight into consumer pricing, purchasing and trends across all utility service offerings.
HiQube combines three data management methodologies, hierarchical, relational and multi-dimensional, within a single unified database architecture. This enables users to perform self-directed drill-down, drill-through, and ad hoc queries as well as "what-if" analyses. As a result, users can explore a multitude of key performance indicators (KPI) and create relevant and personalized reports quickly, in real time.
CFU, a provider of electricity, water, gas and cable services to more than 17,000 customers in Iowa, was looking for an efficient solution to process 56 million records from the past 10 years. With HiQube, CFU was able to breakdown and analyze the data into 17,000 customer accounts, 110 different services, 73,600 meters/service points, 76 rate schedules and 160 routes.
"HiQube opened us up to analysis of our data at extreme levels of granularity, something we couldn't achieve within our timeframe and budget using traditional business intelligence tools," said Mark Meier, Manager of Information Systems & Meter Reading at Cedar Falls Utilities. "We were able to drill down the data into months and then break down months by account, by rate schedule, by usage, by service route, by meter reader and by customer. Additionally, we incorporated rate scenarios by usage to determine the outcomes of 'what-if' scenarios and then compared the data. This gives us greater predictability and accuracy of business decisions than our current processes."