Business intelligence the next generation of knowledge management

Chapter Eleven Business Intelligence:The Next Generation ofKnowledge Management Introduction Knowledge neither product nor capability; a critical framework of a fully evolved information economy WalMart has a 100-terabyte data warehouse to monitor and capture each transaction in each store for better inventory mgmt; improved collaboration with supplier; merchandise on individual store basis; and to provide superior shopper satisfaction Success from anticipating customer needs before they do First gen e-business apps focus on buying and selling goods via Web Second gen apps focus on gaining insights from organizations’ data collected from each transaction Customer loyalty, enhance profitability Interpret past transactions and use the knowledge to support decisions about the direction the company should be headed Evolution of Knowledge Mgmt Apps Companies want apps to make sense of the data gathered How to make effective use of raw data How to convert raw data into revenue Foundation of KM: information sorting, extraction, packaging, and dissemination Reactive, data centric to proactive, query-driven knowledge world Group Memory Systems Corporate Intranets & Decision Support Portals Extranets & Inter-Enterprise Portals E-commerce & Click Stream Analysis Wave 1 Wave 2 Wave 3 Wave 4 Business Intelligence Wave 5 Wave 1: Group Memory Systems Sharing of info throughout the company Buzzword of 90s Discussion boards, bulletin systems, corporate intranets Instant access to data and reporting info that had previously taken days or weeks to obtain Core: Lotus Notes and Instraspect apps Failed to live up to promise Few can define it Software vendors distancing themselves from GM Costly efforts not delivering expected ROI Wave 2: Corporate Intranets and Decision Support Portals For complete and uniform linkage of data resources scattered throughout the organization Intranets alone don’t create knowledge Data analysis necessary Decision support portals to automate predictable components of decision maker’s routine Pre-requisite for responsive business model: Decision support portals built on intranets Home Depot Decision makers can ask and answer mission-critical questions about business using transaction data assets that have been captured, not exploited to fullest extent Wave 3: Extranets and Interenterprise Portals Fast info access, customized data and responsiveness driving extranets New requirements: manage huge data volumes, data breadth coverage, cross-platform support, response-time speed, and broad range of interface choice DaimlerChrysler Extranet apps encourage trading partners to improve profits by managing inventories in supply chain preferential treatment for visibility Lexmark Wave 4: e-Commerce and Click Stream Analysis User click stream analysis Marketers need every customer activity and purchase; to be able to analyze, understand their buying preferences; to anticipate their changing expectations Testing limits of conventional database mgmt; new DBs emerging Intelligent E-mail management Kana Communications, eGain, and Siebel Tracks and manages millions of daily interactions for analyzing, reporting and launching customized initiatives in response Wave 4: e-Commerce and Click Stream Analysis Knowledge Portals Brio, Business Objects, Cognos, DataChannel, Plumtree, Portera Call center mgrs to understand historical service trends and customer service patterns, identify problem areas; ultimately increase customer retention rates Wave 5: Business Intelligence Data analytics, coupled with broadcast engine technology, foundation for proactive business intelligence Anytime, anywhere, any place BI is proactive and data driven Automates delivery of info to customers using exception conditions and recurring schedules as triggers for communication Traditional decision support apps do not personalize info MicroStrategy’s DSS Broadcaster include new personalization and distribution capabilities New BI apps turning traditonal query-and-response paradigm of decision support on its head Wave 5: Business Intelligence Next gen BI apps to use ecommerce technology to open up data warehouse to hand-held devices Prior models relied on static info about customer transactions Corporations will shift to sense and respond infrastructure to serve customers better For info-based BI models to function well, integration framework necessary to tie knowledge apps Wave 5: Business Intelligence: Elements of BI Apps Real-time Personalization Engine Broadcast, Retrieval, and Interaction Engine Performance Monitoring and Measurement Engine Data Organization & Collection Analysis & Segmentation Engine Prospect or Customer Fax Email Telephone VRU Web Enterprise Architecture Data Organization and Collection First requirement of a successful BI strategy Requires visibility into organization’s activities with both internal and external constituencies data from multiple locations Factors critical to success of large-scale data integration Scalability Flexibility Performance Analysis and Segmentation These apps offer tools for data mining Goal is to improve pricing, retain customers longer and find new revenue streams Travelocity Many Internet businesses do not have a clue about customer behavior on their Web sites Collect gigabytes of customer clickstream data every day For e-business marketing, emphasis on order size and margin Several industries eager to exploit opportunities Telecom; BC Telecom Real-Time Personalization Personalization apps emerging to make businesses responsive to customers’ needs reduced marginal cost of personalization Personalization apps allow you to provide each customer with personalized Web page display only info you want individual customers to see proactively notify customers of product improvements and relevant upgrades, promotions, and service enhancements tailor information and recommendations according to each customer’s individual preferences deliver personally relevant information related to products that the customers own Infrastructure for Broadcast, Retrieval, and Interaction To proactively deliver info to every customer via medium of their choice Sabre Multiple devices proliferating Both prefabricated and custom-made software can be integrated into the platform Profiling Show people what you have to offer Ask customers what they want Matching Give people what they want Match content to customer needs Transacting Allow people to service themselves Make it easy to do business with you Listen Incorporate customer feedback Measure effectiveness Performance Monitoring and Measurement These apps provide managers the info they need to improve operations and strategy Use KPIs linked to a balanced scorecard BT SEM from SAP BI in Telecom: Combating Customer Churn Churn factor forcing providers to process a steady stream of service starts and stops most acute in ultracompetitive wireless Churn mgmt to ensure profitable customer stay with company advanced techniques include ability to predict a given individual’s tendency to select another provider and to define correct course of action to retain the customer BI in Retails: Capturing and Reporting Sales Data Sears, Roebuck and Co. was caught by surprise in 1980s with advent of specialty stores and discount merchandisers Adopting new technology to support regeneration as a more flexible, market responsive company In early 1990s, tech infrastructure based on old sales information systems redundant, conflicting, sometimes obsolete data BI in Retails: Capturing and Reporting Sales Data To survive, forced to embrace BI on a dramatic scale single data source to capture sales data and generate reports Built Sales Knowledge app 1.7-terabyte data warehouse Replaces 18 major databases running on separate systems Tracks sales by item and location on a daily basis Technical Elements of BI Framework Three-Layer BI Solutions Architecture Relationship Management SOLUTIONS Personalization Billing / Payment Systems Performance Measurement Supply Chain Management Advertising / Promotions Data Mining ENABLING TECHNOLOGIES HTML/XML Messaging CORBA / IIOP OLAP Security CORE TECHNOLOGIES Networking Data Warehousing RDBMS Scalability Knowledge Management COM/DCOM/DNA Enabling Technologies: OLAP Provide means to analyze complex data by using a more intuitive set of business rules and dimensions profitability analysis by product, channel, geography, customer or fiscal period Insulate user from technical aspects of data storage and data structures Core process: data entered into a DB is offloaded, reformatted, or accessed in specialized ways to enhance the processing of complex queries Core Technologies: Data Warehousing DWs are repositories of summarized historical data, often extracted from disparate departmental or enterprise DBs Companies of all sizes finding that data warehouses are essential to running their businesses GM Design Transform Integrated Data Warehouse Applications { Deliver Transaction Data Extract. Data Scrubbing& Cleansing Load, Index & Aggregate Complete Integrated Solutions Partial Functional Solutions ProcessLifecycle Extract Publish & Subscribe Data Mining Data Replication Meta-Data Histories& Summaries Data AccessTools Roadmap for Managers Identify goals Determine knowledge sources Determine info needs Collect, clean, prepare data Balance external, internal data Develop new approaches to categorizing information Build the data model Deploy model Monitor model Measure ROI E-Business Strategies, Inc. www.ebstrategy.com contact@ebstrategy.com 678-339-1236 x201 Fax - 678-339-9793