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The Bigger CIS Picture

Data Mining and Warehousing: Many utilities have no ability to turn raw customer information into significant insights about their business.

Fortnightly Magazine - May 2005

That's where data mining and customer analytics come in.

Data-mining tools enable user-defined analysis of data based on a robust data warehousing framework that provides a single view of the customer by combining customer information, account management data, and information on installed services, meter reading, billing, payment, and collection.

What Is Data Mining?

Data mining provides tools and techniques that add intelligence to the data warehouse. It derives its name from the similarities between digging through and extracting meaning from information in a large database and mining a mountain for a vein of valuable ore. Both processes require either sifting through mountains of material, or intelligently probing it to find its hidden value.

Data-mining tools use pattern recognition technologies, along with statistical and mathematical techniques, to sift through warehoused information and unearth significant facts, hidden patterns, relationships, trends, exceptions, anomalies, and predictive information that otherwise might go unnoticed. These new analytical tools can answer important business questions that, until the advent of these tools, were too complex and time-consuming to consider, much less resolve.

Currently, data mining and the associated use of customer analytics and business intelligence software are more prevalent within the European utility industry than in North America because of the higher level of competition under way (and thus the more pressing need to fully understand customer buying and usage patterns). For example, to survive a price war, German power company Hamburgische Electricitats-Werke AG (HEW) implemented a data warehousing project followed by a data-mining project to analyze customer acquisitions and better position itself to compete.

At Electricité de France, the French national electric power company is using data mining to better understand and predict electric power load curves of individual customers, and to characterize records that fail consistency checking of the data warehouse. This makes it easier to produce accurate reports even when data is missing, through the use of statistical adjustment.

Patterns That Lead to Better Decisions

Using data-mining tools, utilities also can analyze any correlations between customer profiles and payment histories. Analysis capabilities can enable users to query CIS data in an manner to identify patterns and analyze root causes for key issues like delinquency and slow payments, thereby increasing the potential for collection. Similarly, data-mining techniques have been used to develop systems that can detect fraudulent credit card transactions in near-real time.

Predictive models can take the uncertainty out of projecting timing and magnitude of utility consumption and thus facilitate and lower the risks of load forecasting, so that more accurate load information can be used to negotiate better rates. Many utilities that collect and maintain meter data reflecting actual energy usage for individual consumers to support accurate customer billing are installing advanced metering technology to enable time-of-use and real-time pricing rate programs. This data can be mined as a means to improve geographic load forecasting and subsequent targeting of energy efficiency and demand reduction programs, or it can be mined by utilities and state regulators to target specific regions or major customers (by class or individually). Data analysis could identify promising tradeoff opportunities to mitigate price