01AA_SiebelAnalyticsOvervie

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Module 1: Siebel AnalyticsOverview

Analytics: Server Architect for AnalyticalApplications (Siebel 7.7)


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Module 1: Siebel Analytics Overview 2 of 33

Module Objectives

After completing this module you will be able to:

Define and describe business analytics and businessintelligence

Identify the analytics business challenge and the solutionprovided by Siebel Analytics products

Define and describe data warehousing

Define and describe data modeling

Identify and describe the Siebel Analytics products andcomponents used to support business intelligencerequirements

Why you need to know: Provides a big picture overview to set the context for the

remainder of the course


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What Is Business Analytics or Business Intelligence?

Provides the data and tools users need to answer questions that

are important to running the part of the business for which theyare responsible

Determine if the business is on track

Identify where things are going wrong

Take and monitor corrective actions

Spot trends

Examples:

Show me the most effective promotions

Show me customers most likely to switch

Show me products that are not profitable Compare sales this quarter with sales a year ago

Show me sales for each district by month


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Business Analytics Challenges

Large and changing data volumes

Differing requirements Ineffective tools


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Large and Changing Data Volumes

Large amounts of data need to be accessed to provide

meaningful results Data may reside in many systems

Results may require accessing millions of records

Data volumes are ever-increasing

Data require changes based on business requirements

Data organization may make access difficult, time consuming,

and resource-intensive


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Differing Requirements

Based on the role of the user, different questions need to be

answered

Level of data detail required will vary

Summarized data is appropriate for executives, but details are

required at lower levels


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Ineffective Tools

Analysis tools are often difficult to master, not easy to use, and

specialized

May require detailed knowledge of the data layout and special

syntax

May require manual consolidation of results from multiple sources

Are often complex and single-purpose: query versus analysis

Reporting tools are often static or fixed and do not allow forinteractivity

Questions may be asked, but cannot be answered

Drill down is often impossible, making causes difficult to determine



Solution: Siebel Analytics

Provides insight, processing, and prebuilt solutions that allow

users to seamlessly access critical business information and

acquire the business intelligence to achieve optimal results

Assess

Identify

Act

Diagnose

Optimal

Results



Siebel Analytics

Is a next generation business intelligence platform

Provides optimized intelligence to take advantage of relationaldatabase technologies

Accesses data regardless of its organization or layout

Leverages and extends common industry techniques

Data warehousing

Dimensional modeling



Data Warehousing

Brings together data from many sources

Organizes data for analytical processing Denormalize data: Duplicate and flatten data structures

Reduce joins: Reduce the number of tables and relationships

Simplify keys: Use surrogate keys such as a sequence number

Employ star schemas: Simplify relationships between tables


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Transactional vs. Analytical Systems

Transactional System Analytical System

Database Manages individual transactions

Write-intensive

Constant updates, inserts, and

deletes

Queries return small datasets

Little data aggregation

Reports require calculation Data optimized for storage and

read/write performance

Data relatively normalized

Multiple table joins

Responds to analysis queries

Read-intensive

Static data loads

Queries return large datasets

Data highly aggregated

Data pre-calculated for reporting

Data optimized for Queryperformance

Data denormalized, flattened

Minimal table joins

Use Data entry

Data retrieval

Reports

Charts and pivot tables

Examples Siebel Sales, Siebel Call Center

Siebel database

Siebel Analytics

Siebel Relationship Management

Warehouse



Transactional schema optimized for read/writemultiple joins

Analytics schema optimized for querying large datasetsfewjoins

Transactional vs. Analytical Systems: Database Schema



Star Schema

Organizes data into a central fact table with surrounding

dimension tables

Each dimension row has many associated fact rows

Dimension tables do not directly relate to each other

Dimension

DimensionDimension

Dimension

Fact



Fact

Contains business measures or metrics

Data is often numerical

Is the central table in the star

Dimension

DimensionDimension

Dimension

Fact

Dollars Units

Shipments



Dimension

Contains attributes or characteristics about the business

Data is often descriptive (alphanumeric)

Qualifies the fact data

Customer

ProgramProduct

Time

Sales

Name

Address

Name

Product Line

Month

Year

Name

Format



User Friendly

Models the way users think about data

Enables data to be understood and analyzed

Customer

ProgramProduct

Time

Sales

Dollars, Units, Shipments by

Customer


Product


ProgramDollars, Units, Shipments by

Time



Star Schema Example

Sales fact table with dimension tables and relationships

SALES FACT

ROW_WID CUST_ID PER_ID PROD_ID QTY_ORDERED QTY_SHIPPED AMT

1 17023 26031 12093 5 4 100

2 17054 26031 12091 15 10 150

3 17023 26033 12091 5 3 50

CUSTOMER DIMENSION

ROW_WID CUST_NAME OTHER

17023 A. K. Parker . . .

17054 Betta Builders . . .

17056 CostCutter Stores . . .

PRODUCT DIMENSION

ROW_WID PROD_NAME OTHER

12091 Widget . . .

12093 Super Widget . . .

12095 Lite Widget . . .

PERIOD DIMENSION

ROW_WID DATE OTHER

26031 1/1/2004 . . .

26033 2/1/2004 . . .

26075 3/1/2004 . . .

Fact table

contains

measures to be

analyzed

Dimension

tables contain

characteristics

that qualify the

facts



Dimensional Modeling

Is a technique for logically organizing business data in a way

that helps end users understand it

Data is separated into facts and dimensions

Users view facts in any combination of the dimensions

Allows users to answer Show me X by Y by Z type questions

Example: Show me sales by product by month

Fact Dimension Dimension


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Siebel Analytic Components

Siebel Intelligence Dashboards

Siebel Answers Siebel Delivers

Siebel Analytics Server and Siebel Analytics Web

Siebel Relationship Management Warehouse (SRMW)

Siebel Analytics Administration Tool


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Siebel Answers

On-demand user interface to analytical information

Point-and-click

analytics

On-demand

query requests

Easy charting

Real-timeanswers to

requests


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Siebel Analytics Administration Tool

Tool to build a metadata model

Outputs a repository file that is used by the services to resolverequests in an optimized fashion


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Module 1: Siebel Analytics Overview 30 of 33Operational Sources

Siebel Analytics Complete Solution

Summary of Siebel Analytics as defined in this module:

SiebelDB

Browser eMail WAP/Voice PDA Pager

XML

MerchantRDBMS

Legacy/Host

Existing ODS,Data Warehouses,

Data Marts

SRMWserverwith

prebuiltETL

programs

Siebel Analytic ApplicationsVerticals:Auto, Consumer Sector, Life Sciences, CME, Finance, Insurance

Horizontals:Customer, Sales, Service, Marketing, Executive, PRM, ERM

Siebel Answers &Intelligence Dashboard

(Interactive Access)

Siebel DeliversiBots Technology(Proactive Alerts)

Siebel AnalyticsServer Access(Open Interface)

Siebel Analytics Server

SiebelRelationshipManagementWarehouse

Analytical Sources


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Certification

Three role-based certifications and one master-level certification

are available

Certified

Master Analytics

Consultant

Certified

Analytics

Server

Architect

Certified

Analytics

Data Warehouse

Developer

Certified

Analytics

Application

Developer


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