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Best Practices Awards 2008

TDWI’s Best Practices Awards recognizeorganizations for developing and implementingworld-class business intelligence and datawarehousing solutions. Here are summariesof the winning solutions for 2008.

TDWI’s Best Practices Awards recognizeorganizations for developing and implementingworld-class business intelligence and datawarehousing solutions. Here are summariesof the winning solutions for 2008.
TDWI Best Practices Awards Judges

TDWI thanks this year’s panel of expert judges:

    Barb Wixom, Associate Professor, University of Virginia
    Claudia Imhoff, President, Intelligent Solutions, Inc.
    Evan Levy, Partner, Baseline Consulting
    Hugh Watson, Professor of MIS, University of Georgia
    Jed Summerton, Chief Solution Architect, Conect : The Knowledge Network
    Jill Dyché, CBIP, Partner, Baseline Consulting
    James Thomann, CBIP, Principal Consultant, DecisionPath Consulting
    John Bair, Information Strategy
    Jonathan Geiger, CBIP, Executive Vice President, Intelligent Solutions, Inc.
    Joyce Norris-Montanari, CBIP, President, DBTech Solutions, Inc.
    Mark Peco, CBIP, Partner, InQvis
    Mike Lampa, President, TeamDNA, Inc.
    Nancy Williams, CBIP, Vice President and Principal Consultant, DecisionPath Consulting
    Patty Haines, President, Chimney Rock Information Solutions
    Philip Russom, Senior Manager, TDWI Research
    Sid Adelman, Principal, Sid Adelman & Associates
    Steve Dine, President, Datasource Consulting, LLC
    Steve Williams, President, DecisionPath Consulting
    Wayne Eckerson, Director, TDWI Research

BI/DW on a Limited Budget


Solution Sponsor: Baseline Consulting

JEA owns, operates, and manages the electric system established by the City of Jacksonville, Florida, in 1895. JEA also owns, operates, and manages Jacksonville’s water and wastewater system, beginning in 1997. JEA is Florida’s largest municipally owned electric utility, the 8th largest in the United States, and the 113th largest of the more than 3,000 electric utilities in the country.

Business Impact. JEA’s BI project, referred to internally as the Distribution System Loading—Phase I (DSL—I), made Automated Meter Read (AMR) data useful by combining daily and 15-minute interval consumption from individual meters with customer and connectivity data. It delivers BI reports and analytical models and enables JEA’s business areas to analyze utility load data, to analyze actual transformer load by transformer rating and time, and to supply load (such as summer peak data) at the transformer level to the GIS system.

Users can access standard reports and create ad hoc queries on load information to support on-demand business inquiries. The reports and queries help the utility identify overloaded transformers that may fail (degrading service reliability) and underloaded transformers that could serve additional customers, increasing system efficiency and reducing operational cost.

Maturity. Phase I further extended JEA’s “enterprise” data warehouse, reaching broadly across lines of businesses and constituencies. Phase II will integrate customer load and GIS data for improved distribution planning and operations. It will aggregate customers’ loads by phase to upstream devices, such as group switches, so the company can analyze load at any point along a lateral or feeder by phase from the transformer to the substation breaker.

Relevance. Using the data warehouse environment to integrate the AMR data with data from other essential systems to solve critical business needs related to system reliability and operational savings has made JEA an acknowledged early adopter in the industry. The data models created for this initiative provide an extensible platform for developing additional analytical tools, such as long-term planning reports and drill-down analysis tools.

Innovation. Using daily and interval data provided by AMR for analytics and performing transformer-load management calculations with complex engineering formulas—a business solution not currently available in commercial software—have made this a groundbreaking project.

Despite its limited budget and resources, JEA believes its solution can be implemented in other utilities deploying AMR technology. Its cost ($350,000), paid back in less than a year, is proof that it only requires imagination, dedication, and executive-level vision—not huge capital outlay—to create an innovative and useful business solution using a data warehouse.

Customer Intelligence

WINNER: Cisco Systems, Inc.

Cisco Systems, Inc. designs, manufactures, and sells internet protocol (IP)-based networking and other products related to the communications information technology industry worldwide.

Business Impact. Cisco’s new customer intelligence center (CIC) initiative is a framework and a platform based on an enterprise data warehouse (EDW) that focuses on customer data integration (CDI). The EDW integrates data from many sales, marketing, and financial applications, and serves up the data to support multiple analytic and operational applications. Through powerful application logic, coupled with data models in EDW that integrate data across multiple domains, Cisco can correlate revenue bookings with sales and marketing activities; more than $500 million in bookings have been assisted by the CIC program, fully justifying the cost of this initiative.

Maturity. Following deployment in June 2007, Cisco’s CIC had 250 users within a month, and in one year it had 1,700 active users logging into the application many times a week. User adoption is one of the most important metrics for gauging the success of an application, and a detailed deployment plan is in place to continue this success and double the adoption and usage in the next five months.

Relevance. It’s been proposed that a 360-degree view of customers can increase sales through up-sell and cross-sell activities. In many ways, Cisco’s CIC proves this to be true, with the application using customer data and its interactions with Cisco to the fullest extent.

Innovation. Cisco applied well-known data warehousing platforms, data integration tools, and reporting/analysis tools to its customer intelligence solution. Innovation in Cisco’s diligent focus on tracking revenue in multiple dimensions lets data both enable and track sales accurately. This helps Cisco increase revenue and provides an understanding of customer needs that will lead to greater efficiency and effectiveness in sales and marketing.

Data Governance

CO-WINNER: Airlines Reporting Corporation

Airlines Reporting Corporation (ARC) is an airline-owned company that provides financial settlement solutions and data and analytical services to the travel industry, including airlines, railroads, travel agents, corporate travel departments, airports, and industry analysts.

Business Impact. Since data is the fundamental building block of all of ARC’s products and services, ARC designed, developed, and implemented a phased approach for a data governance program. Data governance has improved accountability for data content, attained a single version of the truth for core industry data, improved productivity and reusability around data, improved time to market for data products and services, and increased revenues.

Maturity. ARC’s business intelligence and data warehousing (BI/ DW) implementation has been in place since 2002, and many data governance functions were handled in an ad hoc manner around the BI/DW implementation. However, ARC management saw an opportunity to formalize and improve data-oriented business processes by instituting a formal data governance program, which rolled out in mid-2004. BI/DW infrastructure still supports governance, though data distribution and data classification are now handled more rigorously, with rules controlling these activities based on whether data will be accessed internally or delivered externally to customers.

Relevance. ARC’s need for rigorous processes around data access and usage was driven by the fact that data for them is a product delivered externally to customers. Even so, IT departments that only handle data internally could learn a lot from ARC’s sophisticated policies and procedures that define how all data is classified, identify priority areas for data improvement, and enforce strict rules for how, when, and to whom data may be distributed.

Innovation. ARC’s innovation is demonstrated in its BI/DW infrastructure (which is designed and extended to serve both internal employees and external customers and partners) and in its comprehensive data governance policies and procedures for data classification, security, improvement, and distribution.

Data Governance


Solution Sponsor: ASG

HSBC, headquartered in London, is one of the largest banking and financial services organizations in the world. HSBC’s international network comprises more than 9,500 offices in 79 countries and territories in Europe, the Asia-Pacific region, the Americas, the Middle East, and Africa. Through an international network linked by advanced technology, including a rapidly growing e-commerce capability, HSBC provides a comprehensive range of financial services: personal financial services; commercial banking; corporate, investment banking, and markets; private banking; and other activities.

Business Impact. As with many financial institutions, HSBC has been under pressure in recent years to control data access and usage to comply with multiple regulations coming from multiple sources. In particular, HSBC had immediate needs to demonstrate compliance with UK data protection laws, California data privacy laws, and Basel II—not to mention internal policies. By instituting a data governance board, HSBC achieved these compliance goals. HSBC also automates many data governance policies and procedures via a global metadata repository.

Maturity. HSBC rolled out the new global metadata repository (GMR) in January 2003. For more than five years, the GMR has provided business and technical users a day-to-day reference tool to better understand their data through the display and analysis of their business metadata. The GMR aids in the discovery and display of relationships among various metadata components (including relationships within business units and across the organization), and in compliance to group standards (e.g., model compliance to the group enterprise data warehouse model).

Relevance. Data governance is still quite new, so best practices for the organization of data governance boards and committees are just now becoming known. Very little is yet known about how software automation can assist with coordinating the many people and processes of data governance plus help with policing data access and usage. HSBC is ahead of the pack in that it has tackled both of these new areas, and its example can guide companies in a wide variety of industries and geographic regions.

Innovation. One of HSBC’s innovations is the use of an enterprise metadata repository to classify data so various applications and users can handle it automatically according to rules that comply with the classifications. Further automation enables reporting tools to generate reports based on the data classifications.

Enterprise BI

CO-WINNER: Cisco Systems, Inc.

Cisco is the leading supplier of networking equipment and network management for the Internet. Cisco hardware, software, and service offerings are used to create Internet solutions that allow individuals, companies, and countries to increase productivity, improve customer satisfaction, and strengthen competitive advantage.

The enterprise BI platform for Cisco’s quality organization is known as quality data infrastructure (QDI). This cross-functional effort was sponsored by corporate quality, global supply chain management (manufacturing), and the Cisco development organization (engineering).

The goal was to build the foundational framework for these diverse communities; a key objective was to define, deploy, and institutionalize the Cisco-wide quality data architecture (QDA) and enable the best-in-class processes, systems, service predictability, and operational excellence to meet and exceed customer expectations.

Business Impact. Standardized quality world class metrics (KPIs) have shown tremendous improvements in the past two years and include immediate returns, production yields, component DPMO, supplier quality measurement, and poor quality cost reduction.

QDI provides BI that allows prediction of customer quality issues so staff can prevent surprise by customer escalations; it provides a single point enablement for reviews with Cisco’s external customers and compliance with industry and government regulations. Estimates of major productivity gains were as high as 35–40 percent per user.

Maturity. QDI is currently used by approximately 3,500 distinct users per quarter (compared to 1,200 users in 2006) and by about 20,000 external customers. Recent QDI internal customer satisfaction scores averaged 4.67 out of a total of 5 (up from approximately 4.3 in 2006).

Upcoming releases include correlation capabilities across subject areas for predictive analysis via new conformed dimensions. QDI will soon provide enhanced end-to-end visibility for key data correlations.

Relevance. QDI has implemented a robust data management and data governance model aligned with the enterprise data management strategy with a well-defined “system of record (source) determination” and “single source of truth” structures for singlepoint accuracy.

By incorporating an elaborate data profiling methodology into the project development life cycle, the company can measure the quality of data at the onset and assess the worthiness of proposed solutions up front.

Innovation. An innovative business model exists for iterative dashboard development by the business team with leverage on an onsite, offshore development model. In addition, working closely with Oracle Corporation, Cisco implemented a single sign-on (SSO) “securely” between Enterprise Oracle Analytics dashboards, enterprise-based Oracle 11i applications, and hundreds of other applications, including homegrown J2EE applications and third-party vendor solutions, all secured by the enterprise authentication engine.

Enterprise BI

CO-WINNER: GE Rail Services

GE Rail Services (GERS) is a leading service provider to the global rail industry. As a member of General Electric Company (NYSE: GE), GERS leases the most diverse fleet of railcars in the industry as well as a full range of intermodal assets to transport vital commodities.

Business Impact. GERS’s latest comprehensive information delivery solution, a “one-stop-shop” enterprise BI portal, meets a wide spectrum of user needs—from self-service and ad hoc analysis to standardized metric reports organized by business function, to dashboards and many sophisticated strategic and operational analytics applications.

The senior management at GERS views the enterprise business intelligence (EBI) platform as a strategic investment. It has helped GERS improve the efficiency of operational, commercial, financial, and services aspects of the business, generating significant financial benefits and a unique competitive advantage.

Maturity. GERS began developing its EBI platform in 2000 and continues to invest in developing innovative cross-functional BI capabilities. It has tailored the delivery of BI content to meet the unique requirements of different decision makers.

“The EBI practice at GERS has … matured through several business and leadership cycles. The four drivers behind our EBI success story are strong business ownership, a centralized core BI team, strategic technology partner relationships, and mature processes,” said Vijitha Kaduwela, BI leader at GERS.

Relevance. GERS has mastered all key ingredients needed to be successful in EBI. The standardized reports on the portal have helped free up key resource capacity to focus on business activities, while SMEs and power users leverage self-service.

Innovation. The BI function reports to the CIO organization and consists of three core functions: data warehousing, reporting and metrics, and business analytics, an area that is traditionally on the business side. The innovative centralized approach allows GERS to capitalize on the synergy. The functional expertise, combined with the IT process rigor, has helped the BI team become highly productive.

Enterprise DW

WINNER: Avnet, Inc.

Avnet, Inc. is one of the largest technology distributors globally. It provides distribution and marketing services, optimizes the supply chain, and provides design-chain services for the products of the world’s leading electronic component suppliers, enterprise computer manufacturers, and embedded subsystem providers. Avnet generated more than $15.6 billion in revenue in more than 70 countries during its fiscal year ending June 30, 2007. It is currently ranked 168 on the Fortune 500.

Business Impact. By implementing a data warehouse for its U.S. electronic components organization, Avnet’s BI team transformed a series of disassociated legacy reporting systems, fed by data from multiple data repositories, into a single, flexible reporting environment serviced by an efficient, scalable data repository populated by OLTP applications. Avnet’s sales force benefited from the efficient BI process and could produce reports within minutes rather than weeks, saving them substantial time in the collection of data and preparation of reports.

By retiring legacy reporting applications and supporting infrastructure, reducing the data footprint, and recouping productive time from business users, Avnet has delivered millions of dollars in bottom-line benefits.

Maturity. Avnet has implemented international BI reporting capabilities. Its data warehouses are multiregional, gathering and disseminating mission-critical data throughout Avnet’s global business. Widespread use of BI tools speaks to its success: more than 3,000 users generate more than 35,000 reports each month.

Relevance. Avnet’s practices of standardization on best-of-breed technologies and straightforward configuration of the technology have benefited its bottom line. Its data warehousing practices focus on providing information that senior executives can use to drive intelligent business decisions. Finally, its innovative practice of data stewardship is a strategic tool any large enterprise can use to scale data quality.

Innovation. Avnet’s data steward program goes far beyond similar programs normally associated with data warehouse implementations. At Avnet, business users are the rightful owners of the data warehouse and the BI reporting environment, ensuring the quality and accuracy of the data.

Government and Nonprofit

WINNER: Sinclair Community College

Solution Sponsor: SAS Institute Inc.

Sinclair Community College in Dayton, Ohio, is a League of Innovation and Vanguard college with an enrollment of approximately 23,000 students.

Business Impact. Sinclair Community College implemented a business intelligence system to improve access to data for decision making and continuous improvement efforts. Examples include targeting outreach efforts for recruitment and for retention.

Before implementing SAS, Sinclair focused recruitment efforts on establishing relationships with high schools and middle schools and participated in outreach programs. With a new analytics-driven approach, Sinclair can identify and target prospective students who would most likely register and succeed at the school by mining 10 years’ worth of student data, including age, gender, GPA, graduation rate, field of study, and marital status.

Maturity. Before SAS, Sinclair’s internal research group had to rely on IT to write custom applications to extract data. The quickness by which Sinclair has been able to answer questions and the enterprisewide use of the technology indicate a mature solution; more than 75 departments analyze data gathered by the college’s ERP system and more than 100 reports are available to the hundreds of users accessing the system daily. Demand for system use continues to escalate.

Relevance. To ensure consistent data and confidence in reporting, Sinclair uses one software platform for reporting, ad hoc analysis, and high-end analytics (data mining). A single SAS platform can pull data from existing systems and optimally integrate individual technology components within its IT infrastructure.

Innovation. To spearhead the implementation and maximize the value of the business intelligence warehouse, Sinclair created the Research, Analytics, and Reporting Office, its version of a BI competency center, which serves as a “front door” to data needs. In addition, the office quickly became the institution’s single source of decision-support information.

Operational BI

WINNER: The Boeing Company

Boeing is the world’s leading aerospace company and the largest manufacturer of commercial jetliners and military aircraft combined. The company also designs and manufactures rotorcraft, electronic and defense systems, missiles, satellites, launch vehicles, and advanced information and communication systems. Boeing has customers in more than 90 countries.

Business Impact. The Boeing Company combines three separate aerospace companies (heritage Boeing, McDonnell Douglas, and Rockwell). In this large, multinational entity, each heritage company had its own accounting system, so consolidating the company’s financials was extremely complicated and cumbersome, taking between 12 and 30 days. Furthermore, the ability to analyze the data was limited because data came from multiple systems and the tools being used (primarily Microsoft Excel). In 2006, Boeing implemented the first phase of an enterprisewide Finance Transformation.

The delivered solution provided an environment within which financial accountants could quickly find and understand the reasons for anomalies in the consolidated ledger. This information helps them make correcting journal entries that are captured in the next consolidation cycle and then incorporated into the next set of cubes.

Maturity. The vision for the application originated in March 2007. Although the application has robust functionality, in some respects the project is still in the early stages of maturity. Much work can still be done to improve performance as the company gains a better understanding of the users’ actual application use. Data quality measures, archiving of dynamic content, and user training in the application (versus the tool) need further refinement.

Relevance. Though the drill-down, drill-through application may not be relevant to other companies, the technique of integrating an analytical environment with a relational set of detail data may be considered a best practice. It allows the user to easily analyze large data sets, identify anomalies, and discover the underlying reason for the anomaly, which then forms the basis for taking corrective action.

Innovation. The innovations in this application are as many and as varied as the challenges the company encountered. The project was able to handle data volume and complexity, a low tolerance for data latency, and presented a synchronized view of the data, integrating an existing cube with a new DMR model, as well as complying with SOX regulations.

Predictive Analytics


BP is one of the world’s largest oil and gas companies, serving millions of customers every day in more than 100 countries across six continents. BP’s businesses include exploration and production; refining and marketing; and alternative energy, its low-carbon energy business. Through these activities, BP provides fuel for transportation, energy for heat and light, retail services, and petrochemical products for textiles and packaging.

Business Impact. BP developed and deployed early warning systems that monitored heavy machinery and control systems, markedly increasing the reliability, operational integrity, and performance of equipment.

The company’s initial trial generated value of $2 million during the trial alone, at a cost of only $50,000. Additional trials provided further savings of almost $3 million through detections such as pump seal problems, failing instrumentation, control problems, and turbine fouling.

Maturity. In slightly more than a year’s time, BP has made significant implementations at more than half of its facilities in one business segment and online pilots in all other business areas. Millions of dollars have already been saved; BP’s goal of significantly reducing unplanned maintenance is well on its way to being realized.

Relevance. Predictive analytics supporting machinery and controlling health is one BP best practice. Although a skilled technician must look at the system’s output data, far fewer experts are required to spot developing problems than with traditional data monitoring methods, and more problems are caught. Quick-win maintenance savings more than pay for the technologies in the first year, and the safety benefits in a potentially hazardous environment are priceless.

Innovation. BP’s aggressive adoption of wired and novel wireless technology to capture more measurements has significantly increased the volume of data available. This wealth of data put BP in an even better position to leverage predictive analytics technology.

The technologies’ data-driven approach has many advantages over the traditional trending or first principles models used in the past. It’s generally faster to implement, easier to maintain, does not require sophisticated engineering knowledge, and makes use of a wealth of existing but unused data, representing a breakthrough in the area of equipment health.

Radical BI

WINNER: Guy Carpenter & Company, LLC

Solution Sponsor: MicroStrategy

As a part of the Marsh & McLennan Companies, Guy Carpenter & Company, LLC, creates and executes reinsurance and risk management solutions for clients worldwide. In addition, Guy Carpenter’s Instrat unit utilizes industry-leading quantitative skills and modeling tools that optimize the reinsurance decision-making process and help make the firm’s clients more successful.

Business Impact. Guy Carpenter’s expertise is in helping its insurance company clients understand the level of risk in the context of particular geographies, demographics, economic conditions, etc. To this end, Guy Carpenter offers i-aXs, a Web-enabled platform that allows clients to interpret and analyze vast amounts of their insurance data. Since Guy Carpenter custom developed i-aXs for each client company, it has made a radical departure from standard business intelligence (BI) practices and embraced the new practice of “BI mashup.”

Maturity. i-aXs has been in operation since November 2006 and currently supports approximately 2,000 users internally and across a wide range of client companies.

Relevance. Risk exposure is a complex analytic problem because it involves multiple dimensions (e.g., geography, economic brackets, and consumer demographics) represented by data from multiple sources. Because of the diversity, conducting analytics often requires multiple tools, yet results must be aggregated in a single user interface. The BI mashup is natural for complex analytic problems like this because it enables the developer to quickly bring diverse data and analysis resources together and present the results in an integrated (and sometimes overlaid) fashion in the user interface.

Innovation. Guy Carpenter’s i-aXs creatively combines complex risk exposure data with maps, satellite images, and innovative data visualizations. These come together in a professional-looking BI mashup, along with traditional charts and tables. This innovative combination is enabled by various in-house tools and data plus Internet-based datasets and applications.



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