Introduction to Building a BI Solution

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Agenda

Introduction to Building a BI Solution

Defining Data Warehouse Structures

Populating the Data Warehouse

Creating Simple and Parallel Data Loads

Copying, Managing, and Transforming Data

Storing, Managing, and Executing Packages

Agenda

Building Cubes

Understanding Analysis Services Architecture

Designing OLAP Dimensions

Using Measures

Implementing Calculations Using MDX

Applying Virtual Cubes

Case Study

Agenda

Designing Aggregations

Processing Cubes and Dimensions

Optimization and Performance Tuning

Advanced Analytical Features and Security

Using Office for Reporting and Analysis

Jumpstarting BI Solutions with SSABI

Understanding Business Intelligence (BI)

The Primary Goal of BI Is to Impact the Bottom Line

Strategic decisions protect and enhance competitive advantage

Tactical decisions manage and measure specific operations or employee behavior

BI Requires the Integration of Several Components

Data that companies collect in its daily operations

Technology which collects and organizes data

People who can analyze data and make effective decisions

Components of Multidimensional Analysis

Measures

Numeric values that are of interest to business analysis

Base measures, Key Performance Indicators (KPIs), and benchmark metrics

Dimensions

Categorical view of measures

All members of a dimension belong together as a group

Single-Dimension View

Each view appears homogenous

Product TotalApples 8000Cherries 8000Grapes 8000Melons 8000Grand Total 32000

Quarter TotalQtr 1 16000Qtr 2 16000Grand Total 32000

Region TotalAtlanta 8000Chicago 8000Denver 8000Detroit 8000Grand Total 32000

Multi-Dimension View

Interaction reveals variations

Units RegionProduct Quarter Atlanta Chicago Denver Detroit Grand TotalApples Qtr 1 4000 4000

Qtr 2 2500 1500 4000Apples Total 4000 2500 1500 8000Cherries Qtr 1 1000 3000 4000

Qtr 2 2000 2000 4000Cherries Total 1000 3000 2000 2000 8000Grapes Qtr 1 1500 2500 4000

Qtr 2 1000 3000 4000Grapes Total 1000 3000 1500 2500 8000Melons Qtr 1 2000 2000 4000

Qtr 2 2000 2000 4000Melons Total 2000 2000 2000 2000 8000Grand Total 8000 8000 8000 8000 32000

Benefits of Multidimensional Analysis

Useful approach for viewing information

Allows for flexible business analysis

Ability to analyze measures simultaneously categorized across many dimensions

Especially powerful when measures and dimensions include combined data from multiple data sources

OLTP Data Sources

OLTP System Characteristics

Processing real-time transactions of a business

Containing data structures optimized for entries and edits

Providing limited decision support capabilities

OLTP System Examples

Order tracking

Customer service

Service-based sales Banking functions

Silos of Data

CallCenter

CallCenter

MarketingCampaign

Mgmt

MarketingCampaign

Mgmt

CRM and eCRM

CRM and eCRM

Internet

Internet

Financial/ Accountin

g

Financial/ Accountin

g Procurement

Procurement

HRHR

Inventory

Inventory

Data Warehouse

Data Warehouse System Characteristics

Presents Data for Business Analysis Processes

Provides a Consistent Historical Data Store

Stores Data in Structures that Are Optimized for Extraction and Querying

Integrates Data from Heterogeneous Source Systems

Combines Validated Source Data

Organizes Data into Non-Volatile, Subject-Specific Groups

Data Warehouse System Components

Data Warehouse

Data Access

End User Data AccessData

Sources StagingArea

Data Marts

Data Extract, Transform, and Load

The Microsoft Data Warehousing Framework

Microsoft SQL Server 2000

Relational Database Management Systems

Contain and manage large quantities of data

Foundation of a data warehouse

SQL Server 2000 Roles

Online Transaction Processing System

Data Staging

Data Warehouse

Data Mart

Data Transformation Services

Extract, Transform, Load, and Management (ETLM) Tools

Extract data from heterogeneous source systems

Transform source data to load into a destination

Data Transformation Services

Copies and transforms data from a variety of sources

Creates reusable transformations and functions

Automates data loads

Analysis Services 2000

Online Analytical Processing (OLAP) Databases

Provide an intuitive, multidimensional view of data Provide fast data retrieval Robust calculation engines

Analysis Services 2000

Creates multidimensional cubes Optimizes aggregations to provide rapid response Supports multidimensional expressions (MDX) to retrieve and

manipulate multidimensional data Includes PivotTable service, an OLE db-compliant provider, for

reporting applications

Extensible Markup Language for Analysis

XML/A is a Data Access Protocol Extending BI to Microsoft .NET Platform

Supports Exchange of Analytical Data Between Clients and Servers

Available on any device or platform

Using any programming language

End User Data Access

End User Applications

Data Access and Distribution Mechanisms

Ad-hoc Query Tools

Report Writers

Modeling Applications

Portals and Dashboards

Excel for Business Intelligence

Excel Provides a Familiar Interface for Data Analysis

OLAP PivotTables and PivotCharts Allow Access to Large Data Sets

Offline Cube Files Allow Analysis When Disconnected from the Network

Office Web Components for Business Intelligence

OWC Delivers PivotTable and PivotChart Functionality to Web

OWC Facilitates Flexible, Customizable Solutions

Data Analyzer for Business Intelligence

Data Analyzer Adds Rich Visualization and Analysis Capabilities

Integration with Excel Facilitates Exploration Before In-Depth Analysis

Full Support for OLAP features in Analysis Services

SharePoint Portal Server

Organizing Documents

Finding Documents

Implementing Approval Processes

Ensuring Document Security

Searching for Documents Collaboration and Update Notification Providing Scalability at the Enterprise

Level

Microsoft SQL Server Accelerator for BI

Extension of Microsoft BI Platform Used to Build Customizable BI Solution

SQL Server Accelerator for BI Components

Analytics Builder Workbook to configure data model

Analytics Builder utility to create an analytical application based on the configured data model

Templates for business analysis with front-end tools

Imagine the Possibilities