Enterprise Analytics


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Enterprise Analytics

  1. 1. Enterprise Analytics Raising the Corporate IQ Organizations have experienced a dramatic increase in the volume of stored data during the past several years, with no slowdown in sight. The problem is no longer a lack of available data to support decision-making but of how to extract value from an ever- increasing volume of data. Enterprise analytics is an emerging category of software that enables companies to leverage their corporate data assets to achieve competitive advantage. We believe the enterprise analytics market is poised for dramatic growth as companies seek to better understand their customers and trading partners, optimize their business processes, and make better decisions faster. Our investment coverage in this sector includes: u Business Objects S.A. (Nasdaq: BOBJ; Buy-Aggressive; US$36.65) u Cognos Inc. (T: CSN, Nasdaq: COGN; Buy-Aggressive; US$18.79) u Hummingbird Ltd. (T: HUM, Nasdaq: HUMC; Neutral; US$18.00) u Informatica Corporation (Nasdaq: INFA; Buy-Aggressive; US$22.43) u MicroStrategy Inc. (Nasdaq: MSTR; Neutral; US$4.04) Highlights ¨ Competing in the e-business economy requires rapid detection of changing business conditions and the agility to respond intelligently to those changes across the entire supply chain. Enterprise analytics provides the technology infrastructure necessary for e-business success by enabling companies to continuously monitor their operations, extract critical insight from their data, and share those insights with customers, suppliers, and other business partners. ¨ We believe enterprise analytics has evolved from a tactical system used internally for historical reporting and analysis to become a strategic, enterprise-wide technology platform that informs all aspects of business operations. Enterprise analytics enables companies to increase profitability through more timely and effective decision making, improved forecasting, greater operational efficiency, and tighter cost management. We believe the current economic slowdown has only heightened the need for enterprise analytics. ¨ We are closely watching emerging developments, such as the use of XML to integrate structured and unstructured information, active/real-time data warehousing, advanced data mining, and wireless analytics, which will further enhance the strategic value of enterprise analytics. David Beck (212) 428-6463 dbeck@dainrauscher.com dbeck@us.rbcds.com May 25, 2001
  2. 2. DAIN RAUSCHER WESSELS Table of Contents Index of Exhibits Overview .......................................................................... 3 Storage Pricing and Storage Capacity Sold ....................... 4 Drowning in a Sea of Data .............................................. 4 Waves of Transactional Data ............................................. 5 Leveraging Information Assets To Improve Simplified Enterprise Analytics Framework ....................... 6 Decision Making ......................................................... 6 Enterprise Analytics Closes the Decision- The Market for Enterprise Analytics Is Poised Making Loop ................................................................... 7 for Take-Off ................................................................. 9 Positioning BI and Analytical Applications ........................ 11 From Decision Support to Analytical Applications: Enterprise Analytics Solution Architecture ........................ 12 The History of Enterprise Analytics .......................... 10 Data Warehouse Revenue Forecast 2000-2004 ............. 15 Enterprise Analytics Solution Architecture .................... 13 ROLAP Versus MOLAP ..................................................... 18 Data Warehousing Market Overview ............................ 15 Business Intelligence Software Revenue Business Intelligence Market Overview ....................... 18 Forecast 2000-2004 ..................................................... 19 Analytical Applications Market Overview ....................... 23 First-Quarter Financial Results ........................................ 20 Emerging Areas: The Frontier of Enterprise Analytics .. 32 Analytical Applications Revenue Investment Coverage Overview .................................... 36 Forecast 2000-2004 ..................................................... 21 Business Objects S.A. .................................................. 34 Market Share by Segment 1999 Versus 2004 .................. 22 Cognos Inc. ................................................................... 42 Analytical Applications Vendor Positioning ....................... 26 Hummingbird Ltd. ......................................................... 51 Enterprise Analytics Meets Informatica Corporation ................................................ 58 Knowledge Management ............................................. 29 Microstrategy Inc. .......................................................... 67 BI License Revenue Comparison and Total BI Public Company Profiles .............................................. 75 Revenue Comparison .................................................. 37 Private Company Profiles ............................................. 80 Business Objects S.A. Income Statement ....................... 41 BI License Revenue Comparison and Total BI Revenue Comparison .................................................. 46 Cognos Incorporated Income Statement ......................... 50 EIP Vendor Categories ..................................................... 54 Hummingbird Ltd. Income Statement .............................. 57 Page 2 x May 2001
  3. 3. DAIN RAUSCHER WESSELS u Overview The rapid adoption of e-business has dramatically changed the business landscape. Faced with escalating competition and increasing customer demands, companies are seeking to streamline internal processes and connect more closely with their customers and suppliers. Throughout the 1990s, this imperative fuelled demand for enterprise application software, beginning with enterprise resource planning (ERP) applications, followed by customer rela- tionship management (CRM) systems, and supply chain management (SCM). Having com- pleted these deployments, companies are now looking to leverage the mountains of transac- tional data generated, creating a surge of demand for enterprise analytics software that enables users to access and analyze data. Enterprise analytics enables organizations to extract value from their data by integrating multiple data sources to provide an integrated view of business operations, and presenting this data so that current business conditions can be interpreted in the context of historical patterns. The latest breed of enterprise analytics goes beyond historical reporting and analysis to include real-time data delivery, predictive modeling, and proactive forecasting. These solutions enable organizations to respond more quickly to changing conditions by providing timely notifications of important events, such as a large contract win by a key competitor or a sudden decline in sales of a particular product. More advanced enterprise analytic systems can go one step further and actually forecast future conditions using sophisticated modeling techniques to predict future events based on historical patterns. This shift from reactive to proactive analysis increases the value proposition, elevating enterprise analytics from a tactical application to a strategic, enterprise-wide technology platform. We believe the enterprise analytics market has reached a critical inflection point and is poised for dramatic growth as companies seek to leverage their exponentially growing data stores to better understand their customers and trading partners, optimize their business processes, and make better decisions faster. In this report, we provide an overview of the technology landscape and the important trends shaping the enterprise analytics market, and highlight the major vendors. May 2001 x Page 3
  4. 4. DAIN RAUSCHER WESSELS u Drowning in a Sea of Data There has been a dramatic increase in the volume of stored data during the past several years, with no slowdown in sight. In fact, a September 1999 study of 50 Global 2500 organizations by Forrester Research revealed that these organizations had an average of 15 terabytes of data and expected that to increase by more than 50% annually for the next five years. This data explosion is being driven by four key trends: u Falling Storage Costs: Paralleling the rapid improvement in microprocessor technol- ogy, continued innovation in storage technology has resulted in a steady decline in the cost per Gigabyte of data storage. According to International Data Corporation (IDC), disk storage prices have declined by a factor of 30,000 during the past 40 years, and the price per Gigabyte is forecast to drop by another 85% between 1999 and 2003. Declin- ing storage costs produce a virtuous cycle by making it more cost-effective for compa- nies to store larger quantities of data, encouraging even more data storage, and fuelling demand for more powerful storage systems. In this way, data begets data. Exhibit 1 x Storage Pricing and Storage Capacity Sold Cost per Gigabyte of Magnetic Disk Storage ($US) Total Disk Storage Capacity Sold (petabytes) $350 3000 $300 2500 $250 2000 $200 1500 $150 1000 $100 $50 500 $0 0 1995 1996 1997 1998 1999 2000 1995 1996 1997 1998 1999 2000 Source: IBM, Interanational Data Corporation u Maturation of Enterprise Software Deployments: The 1990s witnessed strong de- mand for ERP software, driven by business re-engineering projects aimed at streamlin- ing key operational processes, and accelerated by the need to replace outdated systems in preparation for Y2K. As a result, ERP has become a backbone for most Global 2000 organizations, with penetration rates estimated to be as high as 85%. More recently, demand for CRM and SCM applications has surged as organizations seek to leverage their ERP backbone by integrating additional functionality and extending it to connect more closely with customers and trading partners. By moving off-line business pro- cesses to the Internet, these “extended enterprise” applications capture and store un- precedented quantities of detailed, transaction-level data about customers, suppliers, and products. Page 4 x May 2001
  5. 5. DAIN RAUSCHER WESSELS The following diagram conceptually illustrates how successive waves of data-intensive enterprise applications, and in particular, applications related to e-commerce, combine to create exponential growth in the volume of enterprise data. Exhibit 2 x Waves of Transactional Data Enterprise-Wide E-Commerce Volume of Data CRM ERP SCM Time Source: RBC Dominion Securities Inc. u Increasing Adoption of E-Commerce: The rapid growth of business-to-business (B2B) and business-to-consumer (B2C) e-commerce enables companies to track their interac- tions with customers and suppliers at an even more granular level of detail. Web site log files provide a rich source of information about how visitors interact with the company’s Web site, including the specific pages viewed, amount of time spent on each page, and the visitor-to-buyer conversion rate. Due to the continuous nature of e-business and the relative ease of tracking every “click,” these click-stream databases can quickly grow into the Terabyte range. In fact, DoubleClick Inc. (Nasdaq: DCLK; Buy-Speculative; $14.33) reportedly collects more than 250 Gigabytes of click-stream data per day across its Internet advertising network. u Utilization of Data Warehouses and Data Marts: Organizations are using data ware- houses and data marts to build large repositories of historical information. Effectively, data warehouses and data marts serve as a means of off-loading information from trans- actional systems so that it can be placed in historical context and organized in a format that is optimized for analysis. In many cases, data warehouses and data marts deliver the additional benefit of integrating disparate data into a centralized location to provide a consistent and holistic view of the business. Creating an independent data store for analytical purposes enhances query response time and also protects mission-critical trans- actional systems by separating them from decision support applications. May 2001 x Page 5
  6. 6. DAIN RAUSCHER WESSELS Exhibit 3 x Simplified Enterprise Analytics Framework Data Warehouse Transactional End User Data Sources Data Presentation Access Staging Server Source: RBC Dominion Securities Inc. u Leveraging Information Assets To Improve Decision Making Decision-making involves evaluating choices based on historical information and future ex- pectations. Thus, decision-making relies on data, and while companies now have more data available than ever before, in its pure form it is nothing more than a collection of disparate facts. Raw data has tremendous potential value, but this value must be unlocked by manipu- lating and analyzing it in the context of other data and applying various benchmarks or decision criteria to arrive at a conclusion. This is the role of enterprise analytics. In this context, we define enterprise analytics as a group of software products that improve the speed, effectiveness, and consistency of corporate decision-making, and enable the timely and convenient delivery of defined information to effect positive change on business operations. This definition includes: u Data warehousing infrastructure, which provides the underlying platform to consoli- date disparate data sources and present this data in a format suitable for use by business intelligence software and analytical applications; u Business Intelligence (BI) software, which supports decision-making by enabling users to explore, analyze, and manipulate data to uncover important trends and patterns; and u Analytical applications, a special class of enterprise analytics that provide specialized analysis templates directed at specific business problems or functions. The evolution of enterprise analytics parallels that of enterprise applications. Both software groups support the needs of specific functional areas, such as finance, operations, sales, and marketing. However, whereas enterprise applications store and manipulate real-time information in support of business transactions, enterprise analytics leverage static data within a historical context in support of corporate decision-making. As Exhibit 4 illustrates, enterprise analytics offers to “close the decision-making loop” between enterprise applications (transactional systems), which “act/operate” and “track” business operations, and enterprise analytics systems, which “report” on and “analyze” transactional data, blurring the lines separating these two application categories. Page 6 x May 2001
  7. 7. DAIN RAUSCHER WESSELS Exhibit 4 x Enterprise Analytics Closes the Decision-Making Loop Transactional Systems Act / Operate Optimize/ Change Track Suggest Report Analytical Business Applications Intelligence Software Model Analyze Source: RBC Dominion Securities Inc. Effectively, enterprise analytics support feedback mechanisms for an organization and its operational systems. We see an interesting parallel between these intelligent feedback loops and the field of system dynamics, which is devoted to the study of dynamic physical and biological systems in which the outcome of a process effects change on the source of the process in an iterative fashion. In fact, Jay Forrester’s 1961 book, Industrial Dynamics, sparked a new branch of management theory based on the application of system dynamics to corporations. Enterprise analytics enables companies to derive value from their data. Integrating Multiple Data The data warehouse collects, transforms, and integrates data from a variety of internal and Sources external sources to provide a single, enterprise-wide source of clean and consistent data for analysis. Effectively, the data warehouse becomes an integration hub, facilitating improved decision-making by providing managers with a cross-functional view of the business. The integration of customer data has become a top priority as organizations seek to better understand customer needs and preferences, and strive to provide consistent service across all customer touch points. Although this sounds simple, the necessary customer information is frequently locked away in separate systems designed to support specific types of interactions, such as customer service and e-commerce. For example, determining an organization’s best customers is not simply a matter of looking at sales histories and preference profiles stored in the CRM system, but also requires an examination of financial information, such as the average collection period. Providing Historical Context Operational databases are primarily concerned with transaction processing and are designed to maximize reliability, response, and availability. The database typically contains only a limited amount of historical data, and can respond to basic requests for transaction-level information, but is not equipped to support more complex queries. By contrast, enterprise May 2001 x Page 7
  8. 8. DAIN RAUSCHER WESSELS analytics enable users to explore the data along multiple dimensions, such as time, geography, or product line. This enables users to interpret current business conditions by comparing current trends against historical patterns. Enabling Real-Time Information The focus of enterprise analytics is broadening from historical reporting and analysis to Delivery and Analysis include real-time data delivery, predictive modeling, and proactive forecasting. Enterprise analytics can enable organizations to respond more quickly to changing business conditions by delivering notifications to the right person at the right time via the right communications device (cell phone, PDA, pager, etc.). For example, the purchasing manager might receive an alert when the inventory of a critical part falls below a certain level. Enterprise analytics can even go one step further and actually forecast future conditions by using sophisticated modeling techniques to predict future events based on historical patterns. For example, a healthcare provider could combine patient records with medical claims data and demographic statistics to predict which patients are most likely to develop a serious medical condition. Automating Simple Decisions The latest breed of enterprise analytics solutions not only analyze transactional data but can also suggest changes to business processes based on that analysis, and in some cases implement these changes directly by providing feedback to operational systems. In this way, enterprise analytics can be used to improve operational efficiency by automating basic business decisions. For example, enterprise analytics is increasingly being used to enable real-time personalization on the Web, automatically adjusting the marketing message and products presented to a customer based on past purchasing habits. Facilitating Complex Enterprise analytics can also assist with complex decisions that require human intervention Decision-Making by providing pre-built models and analysis templates, calculating the impact of various alternatives on key business metrics, and building context by tapping into best-practice repositories and external data sources such as Dun & Bradstreet. u The Market for Enterprise Analytics Is Poised for Take-Off While the high value-added nature of enterprise analytics is well understood, we believe that the market for these products has reached a critical inflection point and is poised for dra- matic growth due to the convergence of several key trends: CRM Is Priority No. 1 CRM has been one of the hottest, if not the hottest, enterprise software markets during the past several years. Companies have accepted the gospel that it is far more expensive to attract a new customer than to retain an existing customer, and are seeking ways to strengthen their customer relationships. Many companies are implementing one-to-one marketing strategies in an effort to increase revenue per customer by upselling and cross-selling to existing customers. These personalized marketing campaigns use sophisticated modeling techniques to predict future behavior based on the customer’s stated preferences and past purchases. By enabling companies to integrate data across all customer touch points and divide customers into segments based on that data, enterprise analytics provide the foundation for personalized marketing initiatives. E-Busines Ups the Ante E-business has raised corporate performance standards by exposing companies to new global competitors, increasing customer expectations, and accelerating the pace of business. Faced with these challenges, companies are seeking to leverage their transactional data to optimize business processes and achieve competitive advantage. Companies are turning to enterprise analytics to monitor business processes, identify bottlenecks in the supply chain, pinpoint key revenue and cost drivers, and more accurately evaluate the return on investment associated with major capital projects. Page 8 x May 2001
  9. 9. DAIN RAUSCHER WESSELS Web-Based Computing The shift from client-server to Web-based computing is having a positive impact on the Dramatically Reduces Costs market for enterprise analytics by making it more cost effective for organizations to deploy enterprise analytics to a wider group of internal and external users. In addition, the major enterprise analytics vendors have introduced portals, which bring enterprise analytics “to the masses” by providing an interface that is familiar to users due to the popularity of consumer Web portals such as Yahoo! (Nasdaq: YHOO; Neutral). Portals also enable information democratization by providing a pervasive and highly cost-effective information delivery platform. “Information Democratization” Recognizing that decisions throughout the enterprise impact corporate performance, companies are dramatically expanding their use of enterprise analytics to ensure that those employees closest to a problem or opportunity have the tools to evaluate choices and arrive at an appropriate decision. At the same time, companies are discovering that they can strengthen relationships with their customers and suppliers by extending enterprise analytics to these external partners and enabling them to analyze and optimize their interactions with the company. Broad Deployment of One of the most significant costs associated with deploying enterprise analytics is the cost Packaged Data Marts to implement and operate a data warehouse. These costs can roughly double if a customized data warehouse solution is used versus a packaged solution. Historically, many organizations undertook the implementation of large, centralized data warehouses, which required custom development of the architecture and metadata framework, as well as interfaces to legacy systems. The use of packaged applications with standard data schemas has simplified much of the effort involved in deploying a data warehouse by enabling vendors to develop standard extraction templates for the most popular data sources, such as SAP, Oracle Corporation (Nasdaq: ORCL; Buy-Aggressive; $16.83), and Siebel Systems, Inc. (Nasdaq: SEBL; Buy- Aggressive; $50.26) applications. Reducing the amount of custom coding required dramatically reduces the cost to deploy and maintain a data warehouse, removing a key barrier to the mainstream adoption of enterprise analytics. u From Decision Support to Analytical Applications: The History of Enterprise Analytics Emergence of the Data Warehouse: The Dawn of Decision Support The earliest enterprise applications were designed to improve operational efficiency by automating key business processes, such as billing, payroll, and purchasing. They ran on mainframe systems and provided very little if any reporting functionality. Managers had to rely on paper-based reports generated by the Information Technology (IT) department or manual reports derived from system output. Since each business process typically had its own application, managers had to request reports from each system and manually integrate the results in order to obtain a cross-functional view of the business. By the 1980s, decision support systems (DSS) had emerged that could extract data from mainframe transactional systems and generate standard reports. A common example was the executive information systems (EIS), which provided pre-defined, summarized reports targeted at executives. Although EIS were very useful for performing high-level, macro analysis, manual intervention was still required if the user wanted to probe more deeply to investigate trends and patterns. Furthermore, EIS queried the mainframe directly, which negatively impacted the speed and stability of mission-critical transactional applications. EIS failed to achieve widespread adoption, and users remained dependent on the already overburdened IT department to provide management reports. May 2001 x Page 9
  10. 10. DAIN RAUSCHER WESSELS Eventually, the concept of a data warehouse was introduced to solve the problems associated with querying transactional systems directly. Data warehouses are databases that extract large volumes of data from disparate transactional systems at regular intervals and store it in a format that is optimized for user access and analysis. Instead of querying transactional processing systems, users access the data warehouse, enhancing query response time and protecting mission-critical transactional processing systems by separating them from decision support applications. Data warehouses also support the extraction of data from multiple transactional systems (e.g., finance, human resources, sales, manufacturing), providing users with an enterprise-wide perspective of the business operations. The emergence of the data warehouse spurred demand for new data analysis tools to leverage the cross-functional business data gathered in the warehouse. The first BI products introduced were desktop query and reporting and online analytical processing (OLAP) tools. These desktop analysis tools were a significant improvement over the original DSS, which had a rigid user interface, limited depth of analysis, and were relatively expensive to deploy due to their reliance on proprietary hardware and software. Evolution of BI: From Tools to Product Suites The introduction of the original ad hoc query, reporting, and OLAP products brought about a marked change in enterprise reporting. Instead of relying on the IT department to generate reports and waiting weeks to receive them, users could create their own standard and customized reports, and even explore the underlying data to investigate unusual patterns and trends. However, these tools were deployed in a client/server architecture, making them relatively costly to deploy and maintain; consequently, their use was typically limited to a small group of “power users.” The acceptance of Web-based, thin-client computing as a replacement for client-server architecture precipitated the next major shift in the BI market by providing a much more cost-effective means of deployment. Most of the major enterprise analytics vendors have released thin-client versions of their software as well as BI portals, modelled after popular consumer Web portals such as Yahoo, which enable users to access enterprise analytics tools and information through a customizable browser-based interface. These product developments have significantly expanded the user population for enterprise analytics to include employees at all levels of the organization. Enterprise analytics are also being deployed to third-party users, such as customers, suppliers, and other business partners, via an extranet. The evolution of enterprise analytics from a specialized niche tool to an enterprise application has prompted changes to the back-end product architecture in addition to the user interface. In general, the market has shifted away from tools toward integrated product suites that provide improved ease of use, additional administrative and security features, and greater scalability. Individual enterprise analytics tools, such as ad hoc query, reporting, and OLAP, are now largely sold as part of enterprise business intelligence suites (EBIS) or BI platforms. EBIS are designed for the common BI user, and provide access to all of the basic BI tools through a portal interface coupled with a centralized administrative console to facilitate wide- scale deployment. BI platforms offer more sophisticated BI functionality and are targeted at developers as a platform for building and deploying analytical applications. The EBIS category is currently the largest and most active BI market segment; however, BI platforms are beginning to attract greater interest due to rising demand for analytical applications. Page 10 x May 2001
  11. 11. DAIN RAUSCHER WESSELS Introduction of Analytical Applications: Packaged Solutions Begin to Replace Home-Grown Systems Analytical applications go one step beyond BI tools by providing out-of-the-box decision support, enabling users to perform more strategic analysis. Whereas the various BI tools facilitate highly versatile, self-directed data analysis, analytical applications provide pre-built models, reports, and analysis templates that guide the user’s decision process. These reports and templates incorporate business rules and performance metrics that reflect the “best practices” associated with the decision domain. For example, a sales analysis application might enable an organization to identify its most profitable customers by calculating gross contribution margin by customer. Exhibit 5 illustrates the relative positioning of BI versus analytical applications in terms of depth of functionality and breadth of the user population. As functionality becomes more specialized, the product price per seat tends to increase, and the user population narrows. For example, data mining tools are typically used by a small group of technical specialists skilled in the application of sophisticated statistical algorithms, whereas BI portals are deployed to a broad user group with widely varying skill levels. Analytical applications provide more functional depth than BI tools and carry a higher average price per seat, but are typically deployed to a smaller group of users. Exhibit 5 x Positioning BI and Analytical Applications HIGHEST Data Mining FUNCTIONALITY Analytical Applications Client/Server BI Tools BI Tools LOWEST PRODUCT BI Portals EVOLUTION 10 100 1,000 10,000 100,000 1 million NUMBER OF USERS Source: RBC Dominion Securities Inc. Traditionally, analytical applications were custom developed by the IT department or external consultants using a generic data warehousing architecture, including BI tools. These custom solutions often took several months to complete and required considerable end-user May 2001 x Page 11
  12. 12. DAIN RAUSCHER WESSELS involvement to develop the report templates. Although there were a few packaged analytical applications available, these were generally limited to niche financial applications. Today, organizations are feeling pressure to enhance business operations and are looking to leverage their corporate data assets for immediate strategic advantage. As a result, rapid implementation has become a primary concern, and analytical applications have developed in response to this demand. The analytical applications market has expanded beyond its roots in financial consolidation and budgeting to include CRM analytics, operational analytics (e.g., human resources, supply chain management), and vertical analytical applications targeting data-intensive industries such as financial services, telecommunications, and healthcare. u Enterprise Analytics Solution Architecture Exhibit 6 illustrates the major components of an enterprise analytics solution: Exhibit 6 x Enterprise Analytics Solution Architecture Metadata Modeling EBIS / BI Portal Packaged “Mass CRM Data Mart Market” Data Marts Analysis Tools User - OLAP ERP - query and Power reporting - data mining User Legacy Data - visualization Warehouse Extraction E-Commerce Transform Function Load Specialist BI Platforms SCM Analytical External Data Mart Applications Transactional / Operational Systems ETL Tools Data Data Data Data Data Sources Storage and Preparation Analysis Access Management Source: RBC Dominion Securities Inc. The enterprise analytics market can be divided into three major segments, which roughly follow the outline of the basic solution architecture: 1) Data Warehouse Infrastructure Extraction, transformation, and loading (ETL) tools extract data from a variety of heterogeneous sources, cleanse it, transform it into a format suitable for analysis, and deliver it to the data warehouse. Data Warehouses Data warehouses store and manage data in a format that is optimized for analysis. This involves aggregating and summarizing the data and creating indexes to facilitate searching and speed query response. Data Marts Data marts store a subset of the data contained in the data warehouse, such as data specific to a particular department. Page 12 x May 2001
  13. 13. DAIN RAUSCHER WESSELS Metadata Management Tools Metadata provides essential information about what is stored in the data warehouse, such as its source, format, and meaning within the business context. Metadata also oversees the process of extracting data from source systems, performing required cleansing and transformation, and delivering it to the data warehouse. Once data has been captured in the warehouse, metadata ensures that decision support tools and applications access, manipulate, and display it appropriately. 2) Business Intelligence Software Query and Reporting Tools Query and reporting tools allow users to access, navigate, and explore relational (two- dimensional) data and to quickly and easily create reports without understanding the underlying database language, connectivity, and functionality. Online Analytical Processing OLAP tools allow users to perform trend, comparative, and time-based analysis by enabling (OLAP) Tools data exploration of pre-calculated and summarized data along multiple dimensions. Users can explore the data first at a summary level, then drill down through the data hierarchy to examine increasingly granular levels of detail. Data Mining Tools Data mining tools attempt to uncover meaningful patterns and trends in very large data sets by applying complex mathematical, statistical, and pattern recognition techniques. Enterprise Business EBIS offer the most commonly used BI functionality, such as ad hoc query, reporting, and Intelligence Suites (EBIS) OLAP viewing, and usually incorporate a portal interface. EBIS also typically include all of the tools required to build and deploy a data mart (referred to as a “packaged data mart”), including ETL tools, metadata management tools, and warehouse management tools. BI Platforms BI platforms have evolved out of what used to be known as the “high end” of the BI market. These platforms provide all of the tools required to develop and deploy analytical applications. 3) Analytical Applications Analytical applications leverage basic query, reporting, and OLAP functions to analyze and process data. They are typically function- or industry-specific and incorporate standard reports and analysis templates that reflect the “best practices” for a given decision domain. Analytical applications automate processes such as consolidation, variance analysis, correlation, reporting, budgeting, and forecasting. u Data Warehousing Market Overview Technology Overview Data warehousing is composed of a series of processes that extract data from source applications, prepare it for analysis, and make it accessible to end-users. Data Staging Area The data staging area is composed of all of the ETL activities required to collect data from operational source systems and convert it into a common format that is suitable for analysis. This involves intensive data cleansing to remove inconsistencies (such as blank or incomplete data fields), eliminate duplication, and conform dimensions (customer, product, geography) and measures (revenue, cost, profit). End users do not have access to the data while it is in the data staging area. Presentation Server The presentation server physically stores the cleansed data received from the data staging area and permits end users to access the data using various analysis tools, including query and reporting, OLAP, data mining, visualization, and analytical applications. The presentation server stores data in a dimensional format, either in a multidimensional database or a relational database that uses a star, snowflake, or other summary schema to represent inter-relationships within the data. The presentation server can be either a data warehouse or a data mart. May 2001 x Page 13
  14. 14. DAIN RAUSCHER WESSELS Data Warehouse A data warehouse is a comprehensive analytical data store for the entire organization that includes all of the data contained in any constituent data marts. The data warehouse is populated from the data staging area and in turn delivers data to the individual data marts. Data Mart Data marts are smaller than data warehouses and contain only a subset of the data stored in the data warehouse. Typically, a data mart stores data that is specific to a particular business function (e.g., marketing) or enterprise application (e.g., finance). Due to their smaller size and more narrow scope, data marts can generally be deployed much more quickly and cheaply than data warehouses. As a result, many organizations implement an analytical infrastructure incrementally by installing successive data marts, each tailored to the needs of particular user groups, instead of deploying an enterprise-wide data warehouse. While this approach offers significant flexibility to accommodate the changing needs of the business over time, it can be problematic if the data marts are not using consistent data and common definitions of dimensions, such as “customer, product, or geography” and measures such as “revenue, cost, and profit.” If the various data marts do not employ common dimensions, then two users performing the same analysis on two different data marts will produce slightly different results and may come to conflicting conclusions about the same problem. Data warehousing best practices dictate that the individual data marts should be connected to a common data warehouse that stores all of the data contained in the constituent data marts. The data staging area populates the data warehouse, which in turn feeds data to the individual data marts. This sequential process ensures data synchronization and common data definitions, and also provides administrative efficiencies. Metadata Metadata is the foundation of the data warehouse and plays an essential role in its development and deployment. Metadata oversees the data warehouse generation process, specifying required source-to-target data mappings, ensuring data quality by enforcing conformed dimensions and facts, and presenting the data warehouse contents to end users. Metadata also documents the warehouse generation process and provides essential information about the data stored in the data warehouse, such as its source (application and database), format (numeric, date, currency), and meaning within the business context. As such, metadata is the foundation of any BI solution, and is critical to its overall success, because the value of the analysis derived is critically dependent on the quality of the data used (i.e., garbage in, garbage out). Just as the data warehouse architecture can be separated into front-office and back-office components (i.e., the data staging area and presentation server), there is front-office metadata and back-office metadata. Back-office metadata oversees the process of extracting the data from source systems, performing required cleansing and transformations, and delivering it to the data warehouse. For example, back-office metadata provides definitions of common dimensions and factors, maps source-to-target data flows, and specifies required data transformations. Once the data warehouse has been populated, front-office metadata makes it easier for end users to understand the content of the data warehouse in business terms and ensures that end-user tools and applications can effectively access, manipulate, and display the data. For example, front-office metadata would ensure that an application did not combine two pieces of data that are expressed in different currencies. Page 14 x May 2001
  15. 15. DAIN RAUSCHER WESSELS Market Size Exhibit 7 x Data Warehouse Revenue Forecast 2000-2004 9,000 8,000 7,000 6,000 ($ millions) 5,000 4,000 3,000 2,000 1,000 - 2000 2001 2002 2003 2004 Source: International Data Corporation IDC forecasts that the data warehousing market will grow from $4.2 billion in 2000 to $8.2 billion in 2004, representing a CAGR of 19%. This growth is being driven by demand for data integration, especially real-time data integration, to support customer relationship management and supply chain initiatives, and enable more rapid response to changing business conditions. Competitive Dynamics The data warehousing market is composed of two major market segments: the data warehouse management market and the data warehouse generation market. Data warehouse management is by far the larger segment, and is expected to generate revenues of $7.0 billion by 2004, up from $3.3 billion in 2000. These figures represent revenue from the sale of database management systems (DBMS) for data warehousing applications. Oracle, IBM, and Microsoft dominate the data warehouse management market, and together comprised more than 74% of the market in 1999, according to IDC, with Oracle leading the market by a healthy margin with a 35% share. Other vendors targeting this market include the SAS Institute, NCR, Informix (Ardent), and Sybase. By contrast, the data warehouse generation market remains fairly diversified, but is beginning to consolidate around the larger players, which include IBM, Informatica, Informix (Ardent), Microsoft, and the SAS Institute. There are two types of products that compete in the data warehousing generation market: 1) general purpose third-generation (3GL) and fourth-generation (4GL) language tools, which are used for custom data warehouse development; and 2) packaged data warehouse development tools. General purpose tools accounted for approximately 53% of the data warehouse generation market in 1999, according to IDC, and are expected to continue to generate meaningful revenue during the next five years. However, the market is moving toward packaged data warehouse development tools, and IDC forecasts that 3GL and 4GL development tools will represent only 30% of the market by 2004. The proliferation of packaged applications (ERP, May 2001 x Page 15
  16. 16. DAIN RAUSCHER WESSELS CRM, and SCM) with standard data formats is the primary driver behind the trend toward packaged data warehouse development tools, because it enables vendors to develop standard ETL templates for the most popular data sources, such as SAP, Oracle, and Siebel applications. The shift toward second-generation warehouse generation tools is evident in the relative growth rates of the major vendors. The SAS Institute, which generates most of its revenue from general purpose tools, experienced only 9% revenue growth in 1999 (the latest year for which data is available), compared to 18% growth in the overall market. By contrast, Informatica, a leading vendor of packaged data warehousing software, grew its revenue by 147% in 2000 following an increase of more than 120% in 1999. Although the SAS Institute remained by far the largest vendor in 1999, Informatica has become the No. 3 player overall through strong industry partnerships, savvy marketing, and patented technology, and is the leading vendor of packaged data warehouse development tools. With more than 1,200 customers, including more than half of all Fortune 100 companies, we believe Informatica’s data warehouse platform is well positioned to emerge as the industry leader, if not the de facto standard. u Business Intelligence Market Overview Technology Overview BI is composed of a series of software tools and applications that enable users to access and analyze the contents of a data warehouse/data mart. The most commonly used BI tools are query and reporting, and OLAP, although data mining is generating increased interest as a way to discern meaningful trends within large sets of click-stream data. Although analytical applications are a special class of BI software, we have chosen to treat them separately for the purposes of this report, because they represent a relatively new market with very different competitive dynamics. Query and Reporting Query and reporting tools allow users to access, navigate, and explore relational (two- dimensional) data, and to quickly and easily create reports without understanding the underlying database connectivity and functionality. These tools support both ad hoc queries and scheduled reporting, and offer a variety of delivery options for report sharing and distribution, including printer, e-mail, intranets (internal users), or the Internet (external users). Most vendors also offer a subscription feature, which enables users to request the reports that are relevant to them and have them automatically delivered to their inbox. Query and reporting tools are often used to generate regular financial reports, such as daily/weekly/ monthly sales and production summaries. OLAP OLAP tools allow users to perform trend, comparative, and time-based analysis by enabling data exploration along multiple dimensions. Users can explore the data first at a summary level, and then drill down to examine increasingly detailed levels of information. OLAP is often used in applications such as budgeting and forecasting, variance analysis, and trend analysis. For example, a sales manager might use OLAP to analyze sales by product by month (three dimensions) instead of sales by product (two dimensions). Page 16 x May 2001
  17. 17. DAIN RAUSCHER WESSELS There are three different approaches to OLAP, known as MOLAP, ROLAP, and HOLAP. All three require the creation of a multidimensional data structure for analysis, but they differ based on where the multidimensional data structure is stored and how persistent it is. It is these differences that serve as the basis for determining the most suitable product for different application requirements. ROLAP ROLAP tools store the multidimensional data in a relational database, using a star, snowflake, or other summary schema to represent the inter-relationships within the data. The ROLAP engine translates user queries into SQL queries, executes the queries against the database, aggregates the data, and performs required calculations, then delivers the results to the end user. ROLAP provides broad and deep access to data. Users can access summary data, but can also drill down to view transaction-level data. The ability to access very granular data makes ROLAP suitable for unique, ad hoc queries against very large data sets. However, these tools tend to provide slower query response times because the ROLAP engine aggregates data and performs calculations on the fly. The ability to generate highly detailed and complex queries must be traded off against query performance. ROLAP’s reliance on SQL is also problematic, because SQL was designed for one-dimensional, transaction-oriented queries, not complex, multidimensional queries. ROLAP tool vendors overcome this limitation by generating multipass SQL, which requires the creation of temporary tables in the database to store the output from each step of a multi-part query before the sets are joined to produce the final result. However, most relational databases do not have the capacity to store more than a few hundred temporary tables, limiting the scalability of ROLAP tools. MOLAP MOLAP relies on pre-built multidimensional cubes stored in a multidimensional database. MOLAP offers much faster query response than ROLAP, because data aggregation and calculations are performed in advance when the cube is constructed. Furthermore, multidimensional databases are accessed via an application programming interface (API) rather than SQL, and are better suited to support multidimensional analysis, because they store the data in a “pure” multidimensional format. Thus, MOLAP is not subject to the limitations of SQL, and provides better scalability than ROLAP, but requires a greater up- front investment of time and resources to design and build the cubes. MOLAP is well suited for routine, well-understood queries, and can serve the needs of the majority of BI users. However, MOLAP is not designed to support unanticipated queries or very detailed data exploration, since the cubes contain mostly summarized data. Therefore, MOLAP may not meet the needs of highly sophisticated BI users and is generally not appropriate for applications requiring the analysis of very large data sets. May 2001 x Page 17
  18. 18. DAIN RAUSCHER WESSELS HOLAP HOLAP is a hybrid of MOLAP and ROLAP, and can store data in a multidimensional or relational database. HOLAP enables users to access the relational database in real time as with ROLAP, but can also store data in a multidimensional format to provide faster responses to routine queries, as with MOLAP. Exhibit 8 x ROLAP versus MOLAP ROLAP MOLAP Data Source Relational database or a legacy Pre-built multidimensional cubes system that has an SQL layer Calculations Data is aggregated and calculations Data aggregation and calculations are performed “on the fly." are performed in advance Applications Suitable for unique, ad hoc queries Suitable for routine, well-understood against very large data sets. queries Response Time Provides but slower response times Provides quick responses Major Vendors Information Advantage, Informix, Cognos, Hyperion, and Oracle MicroStrategy, and NCR Source: RBC Dominion Securities Inc., and Hurwitz Group Data Mining Data mining tools attempt to uncover meaningful patterns and trends in very large data sets by applying complex mathematical, statistical, and pattern recognition techniques. The most common types of data mining are clustering, decision trees, neural networks, and regression. Data mining is used in applications such as churn analysis, fraud detection, and link analysis to determine the connection between one or more events. Data mining has been around for a number of years, but has failed to achieve widespread desktop adoption like OLAP, ad hoc query, and reporting. Instead, data mining is typically used by a small, specialized group of experts to perform highly sophisticated applications, such as fraud detection. Data mining is also increasingly being used to analyze large volumes of Web traffic data (“click-stream” analysis), and may become more common as companies seek to better understand who is visiting their Web site, who is buying online, and at what point shoppers abandon their shopping carts and why. Page 18 x May 2001
  19. 19. DAIN RAUSCHER WESSELS Market Size Exhibit 9 x Business Intelligence Software Revenue Forecast 2000-2004 14,000 12,000 10,000 ($ millions) 8,000 6,000 4,000 2,000 - 2000 2001 2002 2003 2004 Source: International Data Corporation IDC forecasts that the market for BI software (excluding analytical applications) will increase from $3.9 billion in 2000 to $12.8 billion in 2004, a CAGR of 34%. Four key factors are driving this market: 1) demand for decision support tools to facilitate better and faster decision- making; 2) completion of data warehouse implementations, spurring demand for tools to leverage the analytical data store; 3) wider deployment of BI within and outside the enterprise; and 4) increasing demand for analytical applications, which is having a trickle-down effect on sales of the underlying BI software. Competitive Dynamics The BI market has entered a consolidation phase. The top 10 vendors accounted for 59% of the market in 1999, compared to 53% in 1998 (IDC), and the top 10 vendors grew more than 1.5 times faster than the overall market. The major players in this market include Business Objects, Cognos, IBM, Oracle, and the SAS Institute. The competitive dynamics in the BI market have changed considerably during the past several years, largely due to the trend toward enterprise-wide deployment. Historically, BI was implemented on a departmental basis in response to a specific need, with the IT department often not involved in the vendor selection process. Consequently, it was not uncommon for different departments within the same company to use BI tools from different vendors. However, as companies begin to expand their use of BI to provide better decision support tools throughout the organization, they increasingly view the purchase of an EBIS or BI platform as a strategic investment. We expect that the elevation of BI to a strategic IT priority will lead companies to standardize on a single vendor platform for enterprise-wide deployments. This movement toward standardization will tend to benefit the largest vendors, namely Cognos and Business Objects, because they have the product depth and scalability to satisfy enterprise requirements, the market share to define industry standards and maintain a long-term presence, and the ability to offer global delivery and support. May 2001 x Page 19
  20. 20. DAIN RAUSCHER WESSELS We can already see this shift beginning to play out in the market, as illustrated by the most recent quarterly results from the four major vendors: Exhibit 10 x First-Quarter Financial Results Business Objects Cognos Microstrategy Brio Q1 F2001 Q4 F2001 Q1 F2001 Q4 F2001 Ended Mar. 31, 2001* Ended Feb. 28, 2001 Ended Mar. 31, 2001* Ended Mar. 31, 2001 Total Revenue Total revenue of $98.3M, up Total revenue of $144.1M, Total revenue of $51.4M, up Total revenue of $44.2M, 35% YOY and down up 22% YOY and 2% YOY but down up 12% YOY and 7% QOQ 16% QOQ 12% QOQ 14% QOQ BI License Revenue BI license revenues of BI license revenues of BI license revenues of BI license revenue of $60.6M, up 35% YOY and $75.9M, up 17% YOY and $18.6M, down 28% YOY and $27.4M, up 2% YOY and down 12% QOQ 23% QOQ 29% QOQ 20% QOQ Earnings Before Interest EBIT margin: 15% EBIT margin: 20% $28.7M EBIT loss $2,000 EBIT loss and Taxes (EBIT) Large Deals 3 deals > $1M 4 deals > $1M Not disclosed 3 deals > $1M 48 deals > $200K 89 deals > $200K 76 deals > $100K 595 deals > $50K Quota Carrying Sales 355 quota-carrying sales 307 quota-carrying sales 173 quota-carrying sales 119 quota-carrying sales Representatives reps (up from 330 in Q4). reps (up from 294 in Q3). reps (down from 199 in Q4). reps (up from 113 in Q3). Indirect channel accounted Indirect channel accounted Indirect channel accounted Indirect channel accounted for 55% of revenues. for 34% of revenues. for 38% of revenues. for 21% of revenues. Geographic Distribution North America 35%, Europe North America 63%, Not disclosed North America 80%; of Revenues 55%, Japan 6%, Other 4% Europe 31%, Other 6% International 20% Sales to Existing/New 53% of sales were to 64% of sales were to 75% of sales were to 88% of sales were to Customers existing customers existing customers existing customers existing customers Days Sales Outstanding 64 days 92 days 62 days 69 days (DSO) *Business Objects and MicroStrategy are somewhat disadvantaged by this comparison as we are comparing their Q1 results against the Q4 results of Brio and Cognos. Source: RBC Dominion Securities Inc., company reports Cognos and Business Objects have experienced a significant increase in the number of large deals (>$250,000) signed each quarter and continue to post stronger BI license revenue growth than MicroStrategy and Brio. Cognos and Business Objects both report that they typically face one or two vendors in competitive situations today, compared to four or five just one year ago. We believe that Brio has been much less of a competitive threat since announcing disappointing financial results for the first quarter ended June 30, 2000. The company seems to have had difficulty integrating its acquisition of Sqribe and has suffered a number of executive departures during the past several months. Brio recently recruited Craig Brennan, a former Oracle executive, to lead a turnaround strategy as president and CEO, and we will be watching with interest to see whether he can re-establish positive momentum. Likewise, MicroStrategy has been struggling since restating its 1997, 1998, and 1999 financial results and reporting a significant loss in the first quarter of fiscal 2000. As a result, the BI market has basically narrowed to a two-horse race between Cognos and Business Objects. Page 20 x May 2001
  21. 21. DAIN RAUSCHER WESSELS Of the large software vendors, only Oracle has a meaningful presence in the BI market. However, the company’s products are designed to work primarily with its own applications, which limits their appeal in the broader market. Oracle has promised to enable the latest version of its Enterprise Data Warehouse to pull data from other software vendors’ applications, but this version has yet to ship. IBM is also an important player in the BI software market; however, IBM is dependent upon other software vendors for product such that it resells Business Objects’ BI software and licenses Hyperion’s Essbase technology for its DB2 OLAP Server. u Analytical Applications Market Overview Analytical applications are the newest segment of the BI market and have experienced the most activity during the past 12 months. Analytical applications leverage basic query, report- ing, and OLAP functions to analyze and process data extracted from a variety of internal and external sources. They enable users to perform more strategic and functionally specialized analysis, and automate processes such as consolidation, variance analysis, correlation, re- porting, budgeting, and forecasting. Analytical applications are typically function- or industry-specific and incorporate standard reports, performance metrics, and analysis templates that reflect the “best practices” for a given decision domain. Effectively, packaged analytical applications are based on the same premise as packaged operational applications (i.e., 80% of the functionality required is standard while the remaining 20% is business- or industry-specific). Market Size Exhibit 11 x Analytical Applications Revenue Forecast 2000-2004 7,000 6,000 5,000 ($ millions) 4,000 3,000 2,000 1,000 0 2000 2001 2002 2003 2004 Source: International Data Corporation May 2001 x Page 21
  22. 22. DAIN RAUSCHER WESSELS IDC forecasts that the market for analytical applications will increase from $2.5 billion in 2000 to $6.7 billion in 2004, representing a CAGR of 28%. This growth is being driven by demand for more out-of-the-box decision support functionality than that provided by stand- alone BI tools, and a shift away from custom solutions toward packaged applications that offer rapid implementation. The analytical applications market is composed of three major segments: u Financial and business performance management (BPM) applications; u Customer relationship management (CRM) applications; and u Operations/production applications. The financial and BPM segment consists of accounting (budgeting, forecasting, consolidation, and activity-based costing) and business performance measurement (BPM) applications. The CRM segment includes marketing analysis, click-stream analysis, multi-channel CRM analysis, and vertical-specific CRM analytical applications. The operations/production segment is composed of human resources, materials management and vertical-specific analytical applications targeting industries such as healthcare, retail, and financial services. Financial/BPM was the largest market segment in 1999 with a 44% market share, followed closely by operations/production applications at 41%. The CRM segment accounted for only 15% of the total market in 1999, but is expected to experience dramatic growth during the next five years, increasing to 36% of the total market by 2004 and overtaking financial/ BPM as the largest market segment. Exhibit 12 x Market Share by Segment 1999 Versus 2004 1999 2004 Financial/ Operations/ Financial/ Operations/ BPM Production BPM Production 32% 41% 44% 32% CRM CRM 36% 15% Source: International Data Corporation Financial Analytical Applications: The first analytical applications were financial consolidation packages designed to increase New Web-Based Solutions the speed and accuracy of the regular month-end process of closing the books and today, and Balanced Scorecards financial and BPM analytical applications represent the largest and most mature market Provide Next Leg of Growth segment. IDC predicts that financial and BPM applications will grow more slowly than the total analytical applications market during the next several years, increasing at a CAGR of 20%, compared to 28% for the overall market. However, this growth rate is somewhat deceptive because it masks rapid growth in BPM applications, offset by slower growth in financial budgeting and consolidation packages. Page 22 x May 2001
  23. 23. DAIN RAUSCHER WESSELS BPM applications first began to appear in 1998, and are based on a methodology developed in 1992 by Harvard professor Robert Caplan and President of Renaissance Solutions David Norton. They are designed to evaluate an organization’s progress toward its strategic goals at a high level, based on a variety of quantitative and qualitative measures, such as performance against budget, growth in market share, and employee turnover. BPM is now one of the fastest growing categories of analytical applications, and IDC expects that demand for BPM packages will more than triple between 2000 and 2004, growing from $228.3 million to $729.3 million, a CAGR of 34%. By contrast, the market for accounting applications, including budgeting/planning and financial consolidation, is expected to grow at a CAGR of 14% between 2000 and 2004. Budgeting/ planning analytical applications are growing faster than financial consolidation packages, spurred by the introduction of new Web-based solutions from vendors such as Adaytum. These applications enable users at all levels of the organization to contribute to the budgeting exercise and greatly simplify the process of updating the budget to reflect changing business conditions. The line between budgeting/planning and financial consolidation packages is becoming increasingly blurred as the market moves toward comprehensive product suites. Historically, companies would implement separate applications for budgeting/planning and consolidation, and integrate them with point-to-point application interfaces. However, this approach is totally unsuited to the management challenges posed by a fast-paced business environment, which requires the ability to adjust business strategy in real time and immediately assess the associated financial impact. Consequently, the market is shifting away from discrete packages toward integrated financial analytical application suites. Hyperion introduced some of the first financial applications and remains the largest vendor in this space by a wide margin. Other players include Adaytum, Comshare, Oracle, and Walker Interactive. Financial and BPM applications have also offered a natural entry point into the analytical applications market for major ERP vendors like SAP and PeopleSoft, since they can pull much of the required data directly from their own transaction processing applications. Operations/Production The operations/production segment is composed of a wide range of applications, including Applications: Emphasis on materials management, human resources, and a diverse group of vertical-specific packages Optimization targeting data-intensive industries such as financial services, retail, healthcare, and telecommunications. This type of analytical application is more vertically specialized than the others because business operations and production processes vary widely, requiring highly customized data models. For example, subscriber-churn analysis is specific to the telecommunications industry. Analytical applications for supply chain management enable organizations to optimize their demand planning, purchasing, production, and delivery processes, and are generating a lot of interest as companies seek to increase efficiency, eliminate excess inventory, and speed time to market. IDC expects demand for SCM analytics will increase from $361.5 million in 2000 to $762.7 million in 2004, accounting for 11.5% of the overall analytical applications market. The dominant SCM application vendors, such as i2 Technologies, Inc. (Nasdaq: ITWO; Buy-Speculative; $25.86) and Manugistics Group, Inc. (Nasdaq: MANU; Buy-Aggressive; $38.93), have expanded their product lines to include analytical applications, and the major ERP vendors, such as SAP, have also targeted the space. SAP recently released a series of analytical applications to supplement its mySAP business intelligence solution, including a May 2001 x Page 23
  24. 24. DAIN RAUSCHER WESSELS supply chain analytical application that addresses supplier evaluation, spend optimization, inventory analysis, and manufacturing analysis. We believe that Manugistics’ recent purchase of Talus Solutions is particularly interesting because it will allow Manugistics to complement its supply chain analytics with revenue optimization tools. A number of private companies are also developing innovative SCM analytics offerings that incorporate the unique, inter-enterprise nature of supply chain activities. For example, Tilion’s In-The-Net Commerce Analytics service captures transaction data from each member of a trading community, transforms the data into XML format, and provides real-time, browser- based access to collective supply chain metrics. Shared access to inter-enterprise data enables trading partners to monitor jointly agreed upon performance metrics and to evaluate compliance with service level agreements. Human resources (HR) analytical applications represent a relatively small slice of the total analytical applications market, with sales of $32.2 million in 1999. However, IDC expects rapid growth from that small base, with sales reaching $370.1 million in 2004, a CAGR of 57.0% between 2000 and 2004. To date, the HR analytical applications market has been dominated by a few relatively small vendors offering point solutions focused on areas such as workforce management, training, and recruitment. The recent entry of PeopleSoft, the leader in HR transactional systems, is expected to have a major impact on the competitive dynamics. Vertical analytical applications represent the largest segment of the operations/production analytical applications category, and IDC forecasts that demand for vertical analytical applications will increase from $557.1 million in 2000 to $1.0 billion in 2004, accounting for 15.4% of the total analytical applications market. Financial services is the largest industry vertical and is expected to generate revenues of $520.6 million by 2004. The major vendors of vertical analytical applications include HNC Software and Algorithmics in financial services, and JDA and Retek in retail. CRM Analytical Applications: CRM has been one of the hottest enterprise software markets during the past several years, The “Killer” Analytical App with total worldwide vendor revenue for CRM software and maintenance increasing by 71% to $3.3 billion in 1999 from $1.9 billion in 1998, according to IDC. Not surprisingly, the CRM analytics market is expected to experience rapid growth during the next several years. IDC forecasts that CRM analytical packages will account for 36% of the total analytical applications market by 2004, generating sales of $2.4 billion, which compares to a 15% market share in 1998 based on sales of $301.5 million. CRM analytics is composed of marketing analysis, Web site (“click stream”) analysis, multi-channel CRM analysis, and vertical-specific CRM analytical applications. Page 24 x May 2001
  25. 25. DAIN RAUSCHER WESSELS Marketing analysis is currently the largest sub-segment, accounting for 33% of the CRM analytics market in 1999, according to IDC. Marketing analytical applications assist in planning and analyzing the results of automated marketing campaigns by calculating the number of qualified visits generated, the visitor to buyer conversion rate, cost per buyer, and overall campaign ROI. This sector has seen a lot of mergers and acquisition activity during the past 12 months as vendors seek to combine marketing analytics with operational CRM applications to offer an integrated suite. For example, E.piphany, Inc. (Nasdaq: EPNY; Buy-Speculative; $14.60) acquired Octane Software, and Broadbase Software, Inc. (Nasdaq: BBSW; Neutral) bought Servicesoft. Vertical CRM analytics is the second-largest sub-segment, composed of solutions for the financial services, retail, and telecommunications markets. Vertical CRM analytics is expected to grow more quickly than the overall market for CRM analytics, increasing at a CAGR of 52% between 2000 and 2004, compared to 49% for the overall CRM analytics area. Although it is the smallest sub-segment, click-stream data analysis has seen tremendous activity during the past year, with new firms such as NetGenesis, WebTrends, Accrue, and Quadstone springing up to capitalize on the opportunity to help firms make sense of their online visitors. Click-stream analytical applications track metrics such as average page views, the proportion of new versus returning visitors, visitor to buyer conversion rate, and shopping cart abandonment rate. With the collapse of the “dot-com bubble,” we expect to see some consolidation in this sector, such as the recent acquisition of WebTrends by NetIQ Corporation (Nasdaq: NTIQ; Buy-Aggressive; $28.25) for $1 billion. Multi-channel CRM analytical applications extract data across all customer touch points, including the Web, call centers, and traditional enterprise transaction systems, enabling companies to obtain a 360-degree view of their customers. Multi-channel CRM analytical applications accounted for 19% of the CRM analytics market in 1999, but should become the largest category over time as companies seek to implement more integrated CRM strategies, and click-stream analytics and marketing analytics disappear as discrete sub-segments. Competitive Dynamics Typical of an early-stage market, analytical applications is fragmented, comprising a variety of competitors from rapidly emerging “pure-play” vendors, such as E.piphany and Broadbase, to established software veterans such as Oracle, SAP, and PeopleSoft. Hyperion, a long-time vendor of financial analytical applications, held the largest market share in 1999 at 11.6%, followed by financial services analytics specialist HNC at 5.6%, according to IDC. May 2001 x Page 25