Chapter Eleven

Chapter ElevenChapter ElevenBusiness Intelligence:The Next Generation ofKnowledge Management

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IntroductionIntroduction

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 AppsEvolution 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 SystemsWave 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 Wave 2: Corporate Intranets and Decision Support PortalsDecision 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 Wave 3: Extranets and Interenterprise PortalsPortals

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 Wave 4: e-Commerce and Click Stream AnalysisAnalysis

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 Wave 4: e-Commerce and Click Stream AnalysisAnalysis

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 IntelligenceWave 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 IntelligenceWave 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: Wave 5: Business Intelligence: Elements of BI AppsElements 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

Enterpris

e Archite

cture


Data Organization and CollectionData 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 SegmentationAnalysis 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 PersonalizationReal-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 Infrastructure for Broadcast, Retrieval, and InteractionInteraction

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 MeasurementPerformance 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 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 BI in Retails: Capturing and Reporting Sales Data 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 BI in Retails: Capturing and Reporting Sales Data 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 FrameworkTechnical Elements of BI Framework

Three-Layer BI Solutions Architecture

Relationship Management

SOLUTIONSSOLUTIONS

Personalization

Billing / Payment Systems

Performance Measurement

Supply Chain Management

Advertising / Promotions

Data Mining

ENABLING TECHNOLOGIESENABLING TECHNOLOGIES

HTML/XML

Messaging

CORBA / IIOP

OLAP

Security

Networking Scalability RAS UNIX Security Open Standards

Networking Scalability RAS UNIX Security Open Standards

CORE TECHNOLOGIES CORE TECHNOLOGIES CORE TECHNOLOGIES CORE TECHNOLOGIES

Networking

Data Warehousing

RDBMS

Scalability

Knowledge Management

COM/DCOM/DNA


Enabling Technologies: OLAPEnabling 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 WarehousingCore 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

DataReplication

Meta-Data Histories& Summaries

Data AccessTools


Roadmap for ManagersRoadmap for Managers

1. Identify goals

2. Determine knowledge sources

3. Determine info needs

4. Collect, clean, prepare data

5. Balance external, internal data

6. Develop new approaches to categorizing information

7. Build the data model

8. Deploy model

9. Monitor model

10. Measure ROI

E-Business E-Business Strategies, Inc.Strategies, Inc.

www.ebstrategy.comwww.ebstrategy.com

[email protected]@ebstrategy.com

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Chapter Eleven