TomPeters_Business Intelligence Overview

8/8/2019 TomPeters_Business Intelligence Overview

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Business Intelligence:

What Does it Mean to Me?

Tom Peters

B.I. Practice Lead

Z Y Solutions Corporation

2007 Z Y Solutions Corporation

What is Business Intelligence

The Challenge

Getting to the required information quickly and accuratelyto make decisions

The Problem

Too much information that is not in a consistent usableformat readily available to those who need it

The Solution

Business Intelligence


Components of Business Intelligence

Data

Data store collection by individual applications

Production systems

Gathering of all data stores Data warehousing

Presentation

Scorecards & Dashboards

Enterprise Reporting

OLAP Analysis

Advanced & Predictive Analysis

Alerts & Proactive Notification



2/17

What is a Data Warehouse

A generic term for a system of storing, retrieving

and managing large amounts of data

Software often includes sophisticated compression

and hashing techniques for fast searching and

filtering

Typically remote database containing recent

snapshots of data

Planners and researchers can freely use this

information without worrying about slowing down

operation of the production database


What is a Data Mart

Type of data warehouse designed mainly to address

a specific function or departments needs

Often uses aggregation or summarization of the

data to enhance query performance

Important, however, to maintain the ability to

access the underlying base data to enable drill-

down analysis as necessary


Do I Data Warehouse?

Is the production database designed to be carved up

by the desired dimensions?

Designed for Online Transaction Processing (OLTP)

Is database index for all dimensions Can I create additional indexes

What affect will vendor updates have on the solution



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Will I be replacing my production application

Design around your business NOT your source applications

Insulate from source applications allow for replacement



Do I need to aggregate data due to very high

transactions counts

Improve performance

Limit return row count



Always a good idea

Start simple

Grow as demands increase



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B.I. Terms

Relational Database (RDB)

A database that conforms to the relational model

Relational Database Management System (RDBMS)

Refers to the software used to create a RDB

Informix

Microsoft SQL Server

Oracle


B.I. Terms

Online Transaction Processing (OLTP)

Operational systems

High volume data collection


B.I. Terms

Online Analytical Processing (OLAP)

Defined as providing fast access to shared multi-

dimensional data

Used to generically refer to software and applications thatprovide users with the ability to store and access data in

multi-dimensionally



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Types of OLAP

Multidimensional Online Analytical Processing

(MOLAP) MOLAP cubes are built for fast data retrieval and are

optimal for slicing and dicing operations

Advantages

Excellent performance

Can return complex calculations

Disadvantages

Limited in scope as definition of cube creates boundaries

Limited in volume of data


Types of OLAP

Relational Online Analytical Processing (ROLAP)

Manipulates the data stored in the relational database togive the appearance of MOLAP's slicing and dicing

functionality.

Advantages

Does not constrain data

No data size limitation

Can leverage functions of RDB

Disadvantages

Each request must query the RDB

ROLAP itself is limited to RDB functionality


MOLAP vs. ROLAP

Total Revenue and Costs

In Jan 2004 and Jan 2003

At Top 10 Revenue Stores

MOLAP Manipulations Allow People To Slice-

and-Dice a Subset of Data To View It from Many

Different Perspectives

ROLAP Architecture Allows People To Drill Anywhere in The

Entire Relational Database Across All Dimensions and From

Summaries To Transactional-level Detail

ROLAP AnalysisMOLAP Analysis

Geography

Products

Tim

e

Revenue for Laptop Computers

In 2007

At All Stores

Revenue for All Electronics

In 2003 and Q1 2004

At Stores in the NE Region



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Types of OLAP

Hybrid Online Analytical Process (HOLAP)

HOLAP combines the advantages of both MOLAP and ROLAP

Advantages

Combines both MOLAP and ROLAP

Disadvantages

Not available from most B.I. vendors or vendor has limited

experience in deploying


Metadata

Data about data

Dimension A perspective that can be used to analyze the data Dimensions become more useful when there are many descriptive

attributes that can be used for analyzing the data. The termAttribute is often used to describe the extended Dimension

Examples Customer Item Date

Fact The raw enumerable piece of information about the transaction always

a numeric value (usually aggregatable)

Examples Quantity Unit Price Count


Metadata

Measure

The product of one or more Facts

Can be the result of a formula derived from the RDB or the B.I.

Tool analytical engine

Examples

Quantity

Unit Price

Count

Quantity * Unit Price

Average(Unit Price)

Minimum(Quantity)



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Metadata

Report Templates

Filters Prompts

Transformations

Consolidations

Drill Maps

Hierarchy


Metadata

Customer

PK Custom erNumber

CustomerNameCustomerAddress

InvoiceLine

PK,FK2 InvoiceNum berP K L in e Nu mb er

F K1 I t em N um be r QuantityUnitPrice

InvoiceHeader

P K I n vo i ce N u mb e r

InvoiceDateFK1 CustomerNumber

ItemMaster

P K I t em N u mb e r

ItemDescription

ItemSKU

PK,FK1 Item NumberP K ,F K 2 S K U

SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation

P K ,F K 2 S K UPK,FK1 Location

Quantity

OrderHeader

P K O r de r N um b er

OrderDateFK1 CustomerNumber

OrderLine

PK,FK1 OrderNum berP K L in e Nu mb er

F K2 I t em N um be r


Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine



Item



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Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine

PK,FK1 OrderNum ber

P K L in e Nu mb er


Item


Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine



Item

Customer

InventoryLocation

StockKeeping

Unit


Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine



Item

Customer

InventoryLocation

StockKeeping

Unit

Dimensions



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Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine

PK,FK1 OrderNum ber

P K L in e Nu mb er


Item

Customer

InventoryLocation

StockKeeping

Unit

InventoryQuantity

InvoiceQuantity

InvoiceUnit Price

Dimensions


Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine



Item

Customer

InventoryLocation

StockKeeping

Unit

InventoryQuantity

InvoiceQuantity

InvoiceUnit Price

Dimensions

Facts


Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine



Item

Customer

InventoryLocation

StockKeeping

Unit

InventoryQuantity

InvoiceQuantity

InvoiceUnit Price

InventoryQuantity

InvoiceQuantity

InvoiceUnit Price

Dimensions

Facts



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Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine

PK,FK1 OrderNum ber

P K L in e Nu mb er


Item

Customer

InventoryLocation

StockKeeping

Unit

InventoryQuantity

InvoiceQuantity

InvoiceUnit Price

InventoryQuantity

InvoiceQuantity

InvoiceUnit Price

InvoiceRevenue

Dimensions

Facts


Metadata

Customer

PK Custom erNumber


InvoiceLine



InvoiceHeader



ItemMaster


ItemDescription

ItemSKU


SKUMaster

P K S KU

LocationMaster

P K L o ca t io n

SKULocation


Quantity

OrderHeader



OrderLine



Item

Customer

InventoryLocation

StockKeeping

Unit

InventoryQuantity

InvoiceQuantity

InvoiceUnit Price

InventoryQuantity

InvoiceQuantity

InvoiceUnit Price

InvoiceRevenue

Dimensions

Facts

Measures


Designing your Data Warehouse

What information do I want to derive

Design to business needs, NOT just a replicate of the data

source

Consider performance, de-normalize where appropriate



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Data Warehouse Schema Format

Third Normal Form (3NF)

Classical RDBMSmodeling technique

Minimizes data

redundancy

through

normalization



Star Schema

Consists of a

single Fact table

Compound

primary key

One segment for

each Dimension


Customer

P K C ustomer Number

Customer Name

Customer Address

Item

P K I te m Nu m be r

ItemDescription

Sales

P K ,FK 1 C ustomer N umber P K ,FK 2 I tem N umber P K ,FK 3 Terr i to r y N umber

P K ,FK 4 WarehouseN umber

QuantityUnit Price

Territory

P K Terr i to r yN umber

Warehouse

P K Warehouse N umber


Snowflake Schema

Variation on the

star schema

Very large

dimension tablesare normalized

into multiple

tables



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Extract, Transform and Load (ETL)

Extract

Data extraction and staging

Minimize impact on production data sources

Transform

Convert to format required by data warehouse

Cleanse data to ensure accuracy

Validate primary keys against defined owner

Convert to different numbering schema

Load

Load data to data warehouse

Follow guidelines as outlined by data warehouse


ETL Tools

Vendor tools

Informatica

DataMirror

SAS ETL Solutions

Microsoft

SQL Server 2000

Data Transformation Services (DTS)

SQL Server 2005

SQL Server Integration Services (SSIS)


BI Tool vs. Query Tool

Whats the Difference?

Why Should I Care?



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B.I. Tool vs. Query Tool

Knowledge of Database Structure Not Required

Knowledge of Table Names & Relationships Not Needed

Ability to Predefine Formulas

Define once for everyone

Multi-Pass SQL

Produces Results Not Otherwise Possible

Improve performance for complex queries


B.I. Tool vs. Query Tool

Single Version of the Truth

Empower end users to create their own reports withoutfear of varying results

Control who sees what

Row level security

Object level security (including columns)

Write one report to facilitate multiple user groups

Manage and monitor usage


Deployment Objectives

Ease of administration

One metadata layer definition shared by all 5 styles of BI

One administrative interface for all 5 styles of BI

Ease of deployment Zero foot print web interface

Ease of use

User friendly / intuitive interface. Web, drag and drop,context sensitive menus, user definable help text

Business terms familiar to the organization deploying

solution



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Deployment Objectives

Reduce I.T. involvement

Empower end-users to maintain existing and create newreports

Empower end users to schedule execution of reports and

select delivery method

Web

E-mail

External Device

Give end-users immediate access to data

Allow end-users to mine for new data


Full Feature BI Tool Example

MicroStrategy


5 Styles of B.I. within 1 Unified Architecture

SAPBW

DataMart

DataWarehouse

OperationalDatabase

(CRM,RFID)

OperationalDatabase

(ERP)

O pe ra ti on al D a ta ba se s D ec is io n Su pp or t Da ta ba se s

Integrated Backplane

Services Oriented Architecture

WebBrowsers EnterprisePortals

MicrosoftOffice

Scorecard

s

&

Dashboards

Reporting

OLAP

Advanced

Analysis

Alerts&Proactive

Notificatio

n

Unified Web Interface



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Reusable Metadata

MicroStrategy

Report-Specific

Design

ReusableComponents

REPORT DESIGN

Layout

Format

Calculations

REPORT COMPONENTS

Parameterization

Templates

Filters

Autostyles

BUSINESS ABSTRACTION

Metrics

Hierarchies

Custom Groupings

Transformations

DATA ABSTRACTION

Attributes

Facts

Tables

Aliases

Range of Metadata

Other BI

Technologies

Report-Specific

Design

Reusable

Components


Development Efficiency


MicroStrategy Architecture

ODBCODBC

Metadata Warehouse

TCP/IPTCP/IP

MicroStrategy Intelligence ServerMicroStrategy Intelligence Server

MicroStrategy OLAP ServicesMicroStrategy OLAP Services

MicroStrategy Report ServicesMicroStrategy Report Services

MicroStrategy Narrowcast ServerMicroStrategy Narrowcast Server

MicroStrategy DesktopMicroStrategy Desktop MMicroStrategyicroStrategy DesktopDesktop

MicroStrategy ArchitectMicroStrategy Architect

MicroStrategy AdministratorMicroStrategy Administrator

MicroStrategyMicroStrategy

WebWeb

BrowsersBrowsers

HTTPHTTP

Web ServicesWeb Services

MicroStrategy OfficeMicroStrategy Office

Email, Wireless,Email, Wireless,

PortalPortal

Print & FaxPrint & Fax

SMTP/SMSSMTP/SMS/Portal/Portal



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WebBrowser

HTTP

Web Browser

Web

ServerMicroStrategyDesktop

TCP/IP

HTTP

WebBrowser

HTTP

MicroStrategy

Intelligence

Server

TCP/IP

ODBC

Data

Wareh

ouse

Metad

ata

ODBC

MicroStrategy Desktop

MicroStrategy Architect

MicroStrategy Administrator

MicroStrategyIntelligenceServer

MicroStrategyOLAPServices

MicroStrategyReport Services

MicroStrategyNarrowcast Server

Web

Services

WebServices

Web

Services

SMTP/

SMS/Portal

SMTP/SMS/Portal

Mobile

E-Mail

FaxPrinter


Interactive Demonstration


Questions & Answers

Tom Peters

[email protected]

(847) 487-2300, x204


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