A large energy company develops and manages energy-related businesses and services nationwide, and directly provides electricity to millions of businesses and residences in the Midwest.This company had recently undertaken a series of mergers and acquisitions, which expanded its presence into new areas and new markets.
A side effect of any corporate growth is the proliferation of business systems. This company had dozens of enterprise resource planning (ERP) systems in use across the company, each with detailed information on customers and the entire supply chain. While each of these systems was useful for the specific business unit that depended upon it, nowhere did the company have a single, consistent view of this critical business data across the entire enterprise. In fact, the acquisitions had created an inconsistent, disjointed network of systems and applications across the various divisions and business units.
This lack of an enterprisewide view was troubling for a company with millions of customers. The company had difficulty providing service for customers with multiple accounts across different lines of business.
Even more troubling, this lack of consistent, reliable and accessible data meant that the company’s ability to respond effectively during emergencies was compromised. Since they provided a basic service in a part of the country where severe weather was inevitable, the company was determined that data quality issues should not interrupt a critical utility during an emergency.
The energy corporation decided to standardize all information from existing ERP systems on a single SAP application – a multi-year data migration project involving hundreds of IT and business employees dedicated to delivering better data.
The company chose the unique DataFlux GUI-based design environment to address and resolve problematic data before it entered the ERP system. DataFlux allowed business and IT staffs to work together to develop business rules that ensured only high-quality data fed the new system.
DataFlux technology helped users to profile the data to understand the state of data in each source system. Then, they could create new standardization, correction or de-duplication routines to deliver consistent data to the new ERP system.
Since the company had everything from customer information to product details in the source systems, the users had to discover and improve the quality of a wide variety of information. The company built detailed business rules to standardize and validate data on inventory, assets and other entities.
And with DataFlux technology, the same rules can be surfaced in ERP packages like SAP. So, data quality can be managed over time within the application, using the same rules that govern data quality during the implementation phase.
Large-scale data consolidation projects are notorious for running over budget and beyond anticipated schedules. Often, bad data “pollutes” the system, requiring massive rework and reloading of data during the implementation process. Additionally, end users become frustrated with new software if the underlying data isn’t useful or accurate.
However, by making data quality a priority from the beginning, this utility company met or exceeded budgetary targets and time schedules throughout the implementation. With the new ERP system in place, the company began to obtain a unified view of the enterprise, helping the company create a more efficient, effective corporate culture.