A Common Sence Methodology for Business Intelligence

Introduction
Utilities driven by the competition of deregulation are aggressively searching for new opportunities for increasing revenues while decreasing operating expenses. Many utility executives believe the key to competing in a deregulated environment is to leverage decades-long investments in technology and data maintenance to make better decisions. Some progressive utilities are using Business Intelligence technology to better manage assets including transmission and distribution facilities, company-owned buildings and properties, customer connections, and personnel.

Many utilities are still mired in GIS data collection and maintenance, but a few are starting to look beyond mapping, work management, and outage analysis applications to find new ways of using the tremendous investment in information technology and facility data. Utilities with a solid technology infrastructure are well-positioned to exploit the investment in facility data. Business intelligence and decision-making tools will be the next big technology to emerge in utilities. A good example of this forward-thinking asset management can be found at Allegheny Energy.

Mr. Robert L. Henry, Director, Asset Management for Allegheny Energy, has been intimately involved in AE’s re-engineering efforts of the past several years, and today has the responsibility for Asset Management. Mr. Henry describes his newly created job as trying to catch the revenue and expenses falling through the cracks of Transmission and Distribution System operations’ processes. Mr. Henry’s group has conducted a preliminary investigation of available business intelligence solutions, and is currently re-defining business processes based on a new company structure and focus. “We are actively studying business intelligence activities to become more competitive in T&D operations. If we can reduce our plant by just 3% of the three billion dollars, we will save ninety million dollars. We want to give our decision-makers and planners the tools to analyze facility data to form trends and make more informed decisions about where to invest, and just as importantly, where not to invest.” Mr. Henry and his team are researching data warehousing, data marts, and decision support software, collectively referred to as business intelligence, to meet these new corporate requirements.

Business intelligence is not about creating new data, but creating new tools for planners and decision-makers to leverage decades-long investments in legacy systems. The highest goals of business intelligence are:

Eliminate isolated islands of information within and between organizations.
Move decision-making and information centrally located at the top, and distribute responsibility throughout the organization.
Align the direction of IT more closely to organized business plans and practices.
Create planning and analysis tools that can be easily refined and frequently adjusted for new business requirements.

Understanding the Terminology
It is easy to get that glazed look in your eyes when you start talking to consultants or the IT department about the benefits of Business Intelligence technologies. You are probably somewhat familiar with data warehousing because this technology has rolled out to financials in the accounting department. But the whole issue of Business Intelligence may seem like a “Black Hole” that is best left to the IT staff or a team of consultants dressed in expensive suits. But you don’t have to know all the buzz words to get a project started. Here are a few basic terms that are useful in researching and discussing business intelligence technologies.

Business Intelligence – typically refers to the 3D’s: Data Warehousing, Data Mining, and Decision Support.
Data Warehouse – repackaging of core business operations data into large-scale databases periodically populated with operations and customer data from production computer systems.
Data Marts – Subsets of corporate data fashioned for a specific task.
Decision Support – Includes OLAP, simulation, ad hoc queries, planning, modeling, what-if analysis, data
Data Mining – refers to using tools to query huge raw data repositories called meta-data.
On-line Analytical Processing (OLAP) – restructures data into simplified data schemas, pre-calculated aggregates and summaries, indexes data and aggregates between cubes to speed access, and links data in cubes to SQL access routines.

Cost Of Service Scenario
This paper describes a development methodology for implementing Business Intelligence in the context of determining the value of retaining a group of customers in a remote portion of the service territory. The following example with be used throughout the paper to describe an implementation methodology for business intelligence: