Analytics: Server Architect (Siebel 7.7)

Description:

Analytics: Overview (Siebel 7.7) is the required pre-requisite for this course.

The goal is to enable participants to perform the tasks required to successfully complete a Siebel Business Analytics deployment.

This course is intended for individuals on the implementation team whose major role is to define and model the data used for analytics processing. It provides step-by-step procedures for building and verifying the three layers of a Siebel Business Analytics repository: the Physical, Business Model and Mapping, and Presentation layers. Students initially use the Siebel Analytics Administration Tool to construct a simple Analytics repository to address a fictitious company's business requirements. Students import schemas, design and build logical business models, and expose business models to users in Siebel Answers. In the process, students learn how to build physical and logical joins, simple measures, and calculation measures. They also learn how to validate their results using Siebel Answers. Students then learn how to model more complex business requirements, such as dimension hierarchies, multi-sources, partitions, time series data, and slowly changing dimensions. Students also learn how to implement Analytics Server security, and manage Analytics Server cache.

This course is appropriate for Analytics 7.7 and 7.8.

Audience:

Functional Implementer
Technical Consultant

Objectives:

• To enable students to build a Siebel Business Analytics repository using the Siebel Analytics Administration Tool

Prerequisites:

Required Prerequisites:
Domain experience in business intelligence, data warehouse design, and database design
Analytics: Overview (Siebel 7.7)
Analytics: Data Warehouse Developer (Siebel 7.7)

Topics:

Building the Physical Layer of a Repository

Building the Business Model and Mapping Layer of a Repository

Building the Presentation Layer of a Repository

Using Siebel Answers to Test and Verify a Repository

Adding Multiple Sources to a Dimension

Adding Calculations to a Fact

Creating Dimension Hierarchies and Level-Based Measures

Using Aggregates

Using Partitions and Fragments

Using Repository Variables

Modeling Time Series Data

Modeling Slowly Changing Dimensions

Modeling Extension Tables

Analytics Server Security

Analytics Cache Management

Metadata Design Principles and Best Practices