Deregulation, diversification of product and services offerings and company mergers are rapidly altering the landscape of the power grid that utility companies have until recently regarded as invariable. Those companies which are able to nimbly transform into cybercorps--businesses optimized for the wired world--will prevail in the increasingly competitive marketplace. Building an energy services cybercorp means developing an organization which leverages its product resources, its strategic partnerships and its human capital through its globally wired infrastructure to proactively generate customer demand and become more profitable. In this model, technology becomes the nervous system of the organization's infrastructure with agile synapses (applications) firing information to various parts of the organization. The entire machine runs to serve customers and uses the power of information to fuel the satisfaction of customer needs for profitable gains.

This article highlights several reasons for utility companies to build a highly scalable, functionally architected knowledge warehouse. It discusses the importance of initiating a customer-centric data warehouse project to support strategic marketing and business development decisions. This article also highlights the need to select a decision support (DSS) tool that allows utility companies to conduct highly sophisticated analysis against large amounts of customer information. Throughout this discussion, we will simultaneously discuss the high-level process of building a data mart or data warehouse, including how it should be structured to quickly provide energy services companies with answers to some of their most crucial customer questions.

Building the Knowledge Warehouse

A knowledge warehouse is an organization's strategic and tactical information application environment which combines business solutions with technology to allow companies to make more informed sales, marketing and operational decisions. A knowledge warehouse contains:

  • Information applications (including OLAP applications for report generation and ad hoc analysis);
  • One or many data stores (such as an enterprise data warehouse or several data marts containing customer information); and
  • Meta data store(s).A data store contains all of the organization's strategic and tactical historical cross-functional atomic data. This data is integrated, time variant and non-volatile.

    Central to james martin + co's knowledge warehouse methodology is the view that the decision support system, and each building block thereof, will be most successful in the quality of the analysis they provide when:

  • There is a clearly stated value proposition for building the warehouse. If there is not a value proposition, the effort will most likely fail.
  • The value proposition supports a cross-functional view of the information in the warehouse and will provide measurable results to the organization.
  • The OLAP tool is flexible enough to meet the full range of your current and future analysis requirements. This means your tools should have broad schema support, sophisticated reporting functionality and the ability to continuously monitor and manage your environment, allowing your DSS project to change along with your business. You should always wrap your products around your business, not your business around the products.
  • The development team is composed of both business and technical resources from the core functional areas. This ensures that technical resources understand the breadth of the reporting requirements and that business users understand the technical issues involved with the project's development.
  • The organization approaches development from the customer's (internal and external) point of view.

Begin and End with Your Customers

Energy services companies beginning their transition to the deregulated market are overwhelmed by the wealth of opportunities that a newly diversified marketplace offers, leaving many of them wondering: How do we ensure that our new or existing offerings are competitively marketed? How do we harness the power of the years of data we already possess to tell a story about our business' history? How do we use external data sources to develop a sound market knowledge base? How do we use our information to develop a direction to move forward?

Clearly, every products or services organization must understand its customers. Through understanding customer needs and trends an organization can more effectively serve its constituents through the development of new or customized products and services. To understand its customers, utility companies need to build customer-centric data warehouses and decision support systems. Although many utility companies house years of historical transaction data recorded at the premise level instead of the customer level, companies can still design a customer warehouse by integrating several source systems. Transaction data from the billing system can be integrated with customer data from the marketing system to present a one-customer/many meters view of the information. While this strategy certainly does not stand in place of operational systems, it can provide rapid access to time-critical information.

To truly understand the customers behind the meter, you cannot just build a data warehouse--you need to query it with a powerful and scalable OLAP tool. For example, how many kilowatt hours do my customers consume (as measured at a premise base) by time of consumption, geographic location and vertical industry? Which large manufacturing customers that spend more than a certain amount per month may prefer to off-load some of their usage to a particular time of the day if we offered cheaper rates at those times? Are there residential customers who live in an area where recent break-ins have caused them to consider home security services? Where is there a growing area of Internet-enabled customers? Are there any opportunities for national partnerships with companies who have local branches that use our services? Armed with the answers to these customer-centric questions, utility companies can develop a series of marketing approaches to more effectively cross-sell services, increase profitability and enhance customer service.

Energy services companies need to ask these questions, but they are finding that the necessary data is often located in disparate data sources including transmission, billing, marketing and weather syndication systems; VSAM files; magnetic tape libraries; and RDBMS tables. Because the data is not organized into a single data warehouse, comparisons between data from different departments is difficult and costly. Yet making the jump to solve the integration hurdles in one fell swoop can be a daunting task that proves very costly if not handled effectively. It is best to develop technical knowledge within an organization around a specific value proposition and to begin a project with a scope defined by specific functional targets.

Customer Focused

Several utility companies have been transformed into customer-focused service companies by developing capabilities for virtual call centers, real-time pricing, power brokering, power marketing, mobile field services management and multiple product billing.These utility companies recognized the need for customer-centric data warehouses and decision support systems. With an OLAP tool that can handle sophisticated questions against large amounts of transaction-level data, utility companies can leverage their data warehouse to more effectively understand their customer base and tune their strategic marketing and operational direction. A data warehouse will also become important for merging companies that will want to develop a sound profile of overlapping customers for cross-marketing before they fully integrate their billing systems.

In the case of utility companies, it is crucial that their decision support applications can be deployed over the Web to give strategic customers, partners and corporate users timely access to accurate customer, product and service data. The companies that are able to provide this information to their key customers over the Internet are the ones who will lead the competition in the national and international marketplaces. By increasing the dissemination of corporate information, utility companies can better understand their customers and leverage their strategic partnerships to develop more targeted marketing programs and product offerings.

To this end, james martin + co has partnered with MicroStrategy to develop a Web-enabled customer data warehouse template customized for the utilities industry. This template's goal is to help utility customers jump-start their data warehouse efforts by providing a database schema and a number of predefined queries which focus on a company's top consuming and revenue-generating customers.

Utility companies can slice and dice premise-level transaction information along time, customer, geography, and product dimensions. This template then provides a framework for development of the full customer-oriented data warehouse which may incorporate third-party demographic information or external weather data for usage trend analysis.

Tips for a Successful Customer-Centric Data Warehouse

There are several key factors that are critical to the a customer-focused data warehouse in particular.

  1. Develop a limited scope for a targeted, customer information data mart or data warehouse.

  2. Build a cross-functional executive steering committee, composed of high-level managers from all affected departments to control the project and resolve data quality and ownership issues.

  3. Utilize a knowledge warehouse methodology as a guide to the development process and transfer that knowledge to internal personnel.

  4. Select an OLAP tool that utilizes the full depth and breadth of your data warehouse. That is, your OLAP tool should be powerful enough to ask very detailed, multi-level questions, such as: Show me my residential customers in Stamford, CT, who have spent more than $500 a month for more than four of the past nine months and have an outstanding balance greater than $1000.

  5. Take into account that the warehouse will continuously grow and reporting requirements will constantly change. Your schema will not be static, nor will your database size. To that end, you must select a scalable and flexible OLAP tool to access your data.

  6. Construct a post-implementation support and enhancement project to facilitate full knowledge transfer, address user questions and develop enhancements.

  7. Plan for varied levels of training on how to use your OLAP tool and on the type and detail of the data available in the data warehouse.

  8. Continuously manage and monitor the developed warehouse. This will allow you to manage security for different user groups, identify the most popular queries and tune the database for peak usage times.


Those who have completed or are undergoing a data warehouse development effort might be saying, "We want to start by understanding our general ledger data because our priority is to determine where we can cut costs in our business." Undoubtedly this is another important application which should be carefully considered as part of the overall knowledge warehouse development, but it is recommended that you focus first on the customers because they are ultimately the life blood of your business. You should understand the customers behind the usage patterns before you analyze pricing trends or load balancing. By constructing a customer-centric data warehouse, a utility company develops the capability for strategic analysis that will allow their organization to focus on its market and key drivers for revenue growth--customers.

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