BP Lubricants implemented the pilot for its Business Intelligence and Global Standards (BIGS) program, a strategic initiative for management information and business intelligence. At the heart of BIGS is Kalido, an adaptive enterprise data warehousing solution for preparing, implementing, operating, and managing data warehouses.Marketing Research and Data Analysis

Kalido’s federated enterprise data warehousing solution supported the pilot program’s complex data integration and diverse reporting requirements. To adapt to the program’s evolving reporting requirements, the software also enabled the underlying information architecture to be easily modified at high speed while preserving all information. The system integrates and stores information from multiple source systems to provide consolidated views for:

• Marketing Customer proceeds and margins for market segments with drill down to invoice-level detail

• Sales Sales invoice reporting augmented with both detailed tariff costs and actual payments

• Finance Globally standard profit and loss, balance sheet, and cash flow statements—with audit ability; customer debt management supply and logistics; consolidated view of order and movement processing across multiple ERP platforms

Benefits

By improving the visibility of consistent, timely data, BIGS provides the information needed to assist the business in identifying a multitude of business opportunities to maximize margins and/or manage associated costs. Typical responses to the benefits of consistent data resulting from the BIGS pilot include:

•Improved consistency and transparency of business data

•Easier, faster, and more flexible reporting

•Accommodation of both global and local standards

•Fast, cost-effective, and flexible implementation cycle

•Minimal disruption of existing business processes and the day-to-day business

•Identification of data quality issues and encourages their resolution

•Improved ability to respond intelligently to new business opportunities

1. What is BIGS?

2. What were the challenges, the proposed solution, and the obtained results with BIGS?