[callout: no organization can afford to deny itself the power of business intelligence ]

Publication: Telephony Author: Douglas Hackney Headline: Applied Business Intelligence [callout: no organization can afford to deny itself the power of business intelligence ] [begin copy] 1 Business Intelligence 1.1 Definition Business Intelligence (BI) is a market that will be worth $148 billion dollars by 2003, according to Survey.com. It is growing at exponential rates; is mission critical to every business, regardless of size; has every major technology company as a player; features e-everything delivery and interaction; offers exponentially more capability at orders of magnitude lower prices than just a year ago; is commercializing top-secret technology from formerly off-limits US government programs; has some of the hottest initial public offerings (IPO) and best performing companies of the last two years; and has nowhere to go but up. BI consists of all activities related to organizing and delivering information and analysis to the business. This includes data mining, knowledge management, analytical applications, reporting systems, data warehouses, etc. The BI space is an exciting place to be today, but only if you leverage it to provide high-impact solutions that solve specific business problems. 2 Architectures 2.1 Monolithic The primary components in a BI infrastructure are data warehouse (DW) systems. These systems combine and integrate data from a wide variety of operational systems. They cleanse the data to remove errors; they standardize the data so that key entities (such as product and customer) and metrics / measures (such as revenue and net profit) are consistent across the system; and they integrate the data so that information from different systems (such as accounting data and marketing data) can be combined to yield very high impact information and analysis (such as lifetime value of a customer or profit by product). In the early to mid '90s, many organizations attempted to build their BI infrastructure data warehouse elements in a "top-down" monolithic fashion (see figure one). This approach attempted to model the enterprise, then incrementally build a central mega-data warehouse resource. This has become known as the "dream of homogeneity" as it assumes and demands a consistency of systems, data and architectures that is inconsistent with the heterogeneous nature of a business environment. These large scale enterprise-class projects had trouble delivering value to the business, with studies showing failure rates from 30% (Meta Group) to 80% (DWN and OTR). Enterprise data warehouses aren't the only large scale projects having troubles. A

recent Boston Consulting Group (www.bcg.com) study showed 70% lack of success in largescale enterprise projects involving ERP, CRM, etc. systems. These high failure rates led to the development of an alternative approach to achieve the goal of the enterprise data warehouse called "bottom up." It involves the creation of a series of highly targeted, architected data marts that are integrated into the resulting data warehouse system. This approach has proven to be very popular and effective. The BCG study found that small, targeted solutions are five times more likely to be rated as a success by the business. It is surprising that in the face of these statistics that there is an ongoing fixation with some oldthink DW guru adherents that the enterprise, top-down, monolithic DW approach is the only viable way to achieve the goal of an integrated information resource. This group is blind to five key factors: 1) both methods, top-down and bottom-up, are viable given a suitable political and cultural environment; 2) top-down monolithic approaches are sure death in organizations that lack the senior level support, long-term sustainable political will, and political and communication skills required to be a success; 3) top-down monolithic approaches are incapable of accommodating today's heterogeneous mix of custom DW/data marts (DM), turn-key, packaged DW/DMs, data mining and analytical applications (see figure two); 4) technological considerations such as architectures, approaches, tools, technologies, etc. are meaningless to the business - it is fast, measurable high-impact on the business that counts; and 5) the business makes the rules, not the technologists.

2.2 Federated BI Architecture The current BI market is built on the foundation of a modern BI infrastructure, consisting of a federated BI architecture accommodating all the components of a contemporary BI system: packaged/turnkey data warehouses (DW) and data marts (DM), packaged/turnkey analytical applications (AA), custom built DWs and DMs, custom built AAs, data mining, online analytical processing (OLAP) tools, query and reporting (Q&R) tools, production reporting tools, data quality tools, extraction transformation and load (ETL) tools, system management tools, information delivery tools, enterprise information portals, reporting systems, knowledge management systems, database systems, etc. The federated BI architecture is the "big tent" that provides the foundation and environment to facilitate and enable business information flow, analysis and decision making. As the internet is a network of networks, a federated DW architecture is an architecture of architectures (see figure three). It provides a framework for the integration, to the greatest extent possible, of disparate DW, DM and analytical application systems. A federated DW architecture is the most pragmatic route to provide the maximum amount of architecture possible given the political and implementation realities of real-world sites. A federated DW architecture shares as much core information among the various systems as possible. This is accomplished by sharing critical "master files" or dimensions, common metrics and measures and other high impact data across all systems that can make use of the information. It is usually accomplished via an enterprise class ETL tool, which provides a common meta data repository, and the use of common data staging areas. 2.3 Sample Telecommunications Architecture A packet based telecommunications company has high demands for BI, and often has a business model based on core BI functionality, such as bandwidth/utilization based billing, real time configuration, etc. To accommodate these needs, a federated BI architecture is required to accommodate the heterogeneous BI requirements inherent in providing the near-real time

analysis required by the networking organization along with service / support team requirements and the billing, utilization and analysis needs (see figure four). Telephony and packet BI systems face special challenges in the areas of data volume and realtime data streams. While typical data warehouse systems are considered large if they contain a terabyte of data, a packet system can easily contain ten terabytes or more. To provide support for provisioning, support and dynamic billing the system must also manipulate very large volumes of data in near-real time. The data must be gathered from a worldwide network of devices, cleansed, integrated and aggregated within minutes. These requirements are well beyond the sundry run-of-the-mill architectures, ETL tools and server systems found in everyday data warehouse systems and require special expertise, experience, techniques and technologies to be successful. 3 Solutions No BI system, regardless of its technical elegance or purity of design vision, has a prayer of survival if it does not provide direct business value and solve a specific business problem. The most popular ways to achieve this goal are via analytical applications and data mining. 3.1 Analytical Applications The most popular form of BI utilization from the business perspective is via packaged, turn-key analytical applications. A true, high-business-impact, analytical application is defined by the following characteristics: 1. Architected, integrated data from multiple sources (internal & external) An analytical application includes (or, at a minimum, can include) information from multiple sources, both native OLTP applications, as in the case of an analytical application offered by an ERP vendor, and external information from heterogeneous OLTP systems or 3rd party vendors. Note that many ERP vendor supplied analytical application offerings have no capability to

capture, leverage or utilize external data of any kind. This shortcoming cannot be overly emphasized as you consider the implications of an environment made up of disparate, nonarchitected analytical applications, each with its own semantics, business rules, etc. 2. Flexible, multi-dimensional analysis, drill (up, down, across) and reporting Analytical applications allow business users a flexible environment to view business metrics and measures by any number of pertinent dimensions, with any required number of members. Analytical applications allow seamless drill through into pertinent detailed transactions and flexible and easy movement across dimensions and measures. They also provide the capability to view and report information in all forms required by the applicable business processes, i.e. detailed lists as well as summary cross tab. 3. Turnkey package / short time to market Analytical applications feature rapid deployment, with easy data extraction and/or integration into OLTP packages and data sets; indigenous OLAP or native support for industry standard OLAP engines; pre-formatted, pre-defined relevant business metrics, measures, Key Performance Indicators (KPI), etc.; and implementation ready agents, reports, and aggregations. 4. Integrated business processes Analytical applications provide domain specific solutions to specific business challenges, including internal representations of relevant business processes. Analytical applications provide an interactive environment to interact with the business process by presenting applicable metrics and measures of processes, as well as the ability to interact with, and alter, process values and measures. 5. Self measuring (internally monitored ROI, etc.) Analytical applications provide internal value measurement of the relevant business processes and of the analytical application itself. They monitor the ongoing utilization of the analytical application, and it's effects on the business process. In doing so, they provide ongoing ROI analysis of the business process, and the analytical application. In addition, they monitor the utilization of the analytical application, and provide an active monitor into the propagation of the tool throughout the organization, the relative sophistication of the usage of the system, optimization of the system and identification of best practices regarding usage of the system. 6. Closed loop system An analytical application provides a closed loop, feeding new inputs back into the host OLTP or data warehouse / data mart system. As the users interact with the business process, they introduce new information or alter existing information, as in a budgeting and forecasting system. These new values are then fed back into the source systems as new or modified information for use by all users of the source system and all downstream BI systems. Note that this new or altered information must flow back into the analytical application in real time or near real time. This places extraordinary challenges on the technical infrastructure of data warehouse and data mart systems more accustomed to relatively leisurely monthly, weekly or daily information refreshes. It also places heavy demands for massive re-calculation and reallocation of data, as in budget vs. actual calculations or performance against plan. An even greater challenge is that these write-back, flow-through prerequisites require a level of process rigor and structure that is

diametrically opposed to the free-form flexibility required of a successful BI system. This is a key technological and cultural hurdle that many teams cannot overcome. 3.2 Data Mining Data mining solutions are a key weapon in the BI arsenal. They are used to reveal trends and relationships, and predict future outcomes. They are built on variations of artificial intelligence such as neural networks, machine learning and genetic algorithms. Data mining tools are a powerful technological and competitive weapon and form the underpinnings of powerful product offerings, and infrastructure and support capabilities for packet companies. Most organizations use data mining tools for the discovery of previously unknown relationships, trends and anomalies, as well as to predict future outcomes. On the customer side of the house these capabilities are used for target marketing, churn management, fraud detection and promotion management. Packet content BI systems can also use data mining tools to track, trend and predict network volumes, spot significant outlier behavior, optimize system configuration and performance, and optimize the structure and design of customer offerings. 4 Conclusion A federated BI system is a prerequisite to survive and thrive in today's fast changing and evolving market. Without the capabilities provided by integrated data, powerful analytical tools and insightful data mining applications companies are at a tremendous disadvantage and find themselves unable to compete with their better informed and capable competitors. With the players, the customers and the fundamental possibilities of the market changing daily, no organization can afford to deny itself the power of business intelligence. [end copy] Enterprise Group, Ltd. www.egltd.com info@egltd.com Enterprise Group, Ltd. is a servicemark and should be treated as such. We build business intelligence is a servicemark of Enterprise Group, Ltd. and should be treated as such.