SAP Curriculum Congress 2010 - PBworksathensstatesapfaculty.pbworks.com/f/Congress_2010_SAP...BEx Analyzer BEx Query Designer SAP BI Enhanced Analytics (Data Mining) Session B: Business

Business Intelligence with SAP BI and SAP BusinessObjects Software

Christine Davis – University of ArkansasNitin Kale – University of Southern California

SAP Curriculum Congress 2010

© SAP AG 2010. All rights reserved. / Page 2

Overview

Introduction to Business Intelligence

Session A: Business Intelligence with SAP BI (presented by Nitin Kale)SAP NetWeaver Reporting ToolsBEx AnalyzerBEx Query DesignerSAP BI Enhanced Analytics (Data Mining)

Session B: Business Intelligence with SAP BusinessObjects Software(presented by Christine Davis)

SAP BusinessObjects UniverseSAP BusinessObjects Web IntelligenceXcelsiusSAP BI and Portal Integration

Session ABusiness Intelligence with SAP BI

Nitin Kale

SAP NetWeaver Reporting ToolsBEx AnalyzerBEx Query DesignerSAP BI Enhanced Analytics (Data Mining)


Business Intelligence with SAP BI

•SAP NetWeaver Reporting Tools

•BEx Analyzer

•BEx Query Designer• Query Definition• Exception Reporting

•SAP BI Enhanced Analytics (Data Mining)


SAP NetWeaver BI Architecture

Source: Deng, J. & Uhle, R. (2006). SAP NetWeaver 2004s: Enterprise Data Warehousing. Retrieved from https://www.sdn.sap.com


Reporting: Business Explorer Suite


Business Explorer Suite


Data Analysis & Reporting: Overview


Data Analysis & Reporting: Personalized BI


BI Accelerator

SAP NetWeaver BI SAP NetWeaver BI Accelerator

Data Acquisition

InfoCubes

BI AnalyticEngine

BusinessExplorer

Any Tool

AnySource

Query &Response

Indexing

BI Accelerator responds to queries:joins and aggregates in run time

… indexes loadedinto memory

… creates and storesindexes for InfoCubes


BI Accelerator – Technical information


Business Content

Queries

Web ReportsWorkbooks

Transfer rules

InfoObjectsOpen invoices

CustomerCustomer

InfoSources

Extractor

InfoProvider

RolesStandard-Analysis“Ready-to-go Solutions”

Standard-Analysis“Ready-to-go Solutions”

Standard Content,easy to extend

maintenance via SAP

Standard Content,easy to extend

maintenance via SAP


Business Content

BusinessContent

Usewithout

adjustments

Refiningor

Coarsening

Template foryour own

Business Content




•BEx Analyzer

•BEx Query Designer– Query Definition– Exception Reporting

• SAP BI Enhanced Analytics (Data Mining)


Starting BEx Analyzer

Two Options:1. Via Start Menu

Start MenuAll Programs

Business ExplorerAnalyzer

2. Via SAPTransaction Code “RRMX”


BEx Analyzer Menu & Toolbar

BEx AnalyzerMenu functions

BEx AnalyzerToolbar functions


BEx Analyzer Areas

Columnheaders

(Characteristics)

Key figureareaRow

headers(Characteristics)

Filter values(Characteristics)

Information

TitleDisplay options


Bike Company Data Model



Quantitative Data:-> Key figure(s)Bike Company:

• Sales Quantity• Revenue• Discount• Net Sales

• Cost of Goods Sold

Qualitative Data:-> Dimension(s) /

Characteristic(s)Bike Company:

• Time• Distribution Channel

• Division• Material

• Material Group• Sales Organization

• Country …




BEx Analyzer: Key Analysis Methods

Key analysis methods supported by BEx Analyzer

Slicing:Applying a filter to create a “slice” of data

Dicing:Applying a filter in more than one dimension to create a “smaller” cube (subset)

Drill-acrossRotating the cube (switching the axes)

Drilldown:Displaying more detailed information (opposite of roll-up)

Roll-up:Displaying aggregated information (opposite of drilldown)

Source: Egger, N, Fiechter, J.-M. & Rohlf, J.(2004). SAP BW Datenmodellierung. Bonn: Galileo-Press


Slicing


Slicing Example

Slicing:Filtering by AUS2


Dicing


Dicing Example

Dicing:Filtering by AUS1+AUS2 and Wholesale


Drill-across


Drill-across Example

Drill-across:Switch of axes


Drilldown and Roll-up


Drilldown and Roll-up Example

Drill-down:Detail by month

Roll-up:Aggregation for time


Navigation and Filter Context menu (1/3)

Context menu for Characteristics:1. Back One Navigation Step

Undo for the last navigation.2. Back to Start

Returns to the original values of the query atfirst execution.

3. Select Filter Value4. Remove Filter Value5. Add Drilldown According to … in Rows6. Add Drilldown According to … in Columns7. Sort …8. Properties

Display details for Characteristic (e.g. key andtext, sorting, results, attributes to be displayed…)

9. Query Propertiessee one of the next slides

10. Add Drilldown According to … in NewWorksheets

11. Toggle Condition State



Context menu for Key Figures:1. …2. Add Local Formula

Creation of a calculated key figure based on aformula.

3. …



Query Properties:1. Navigational State2. Data Formatting3. Presentation Options

Display of Result summary.4. Display Options5. Currency Conversion6. Zero Suppression7. Properties8. Conditions

Order in which conditions should beapplied.


Navigation and Column header Context menu

Context menu for Column headers:1. …2. Convert to Formula

Displays the Excel based formula instead of the value.3. Add Local Formula4. Keep Filter Value5. Select Filter Value6. Filter and Drill Down By …7. Add Drilldown According To …8. Swap Axes9. Query Properties10. Goto …

Allows navigation to assigned documents, a moredetailed query to be executed, a website …using thereport-to-report interface (covered in BI-2 Module 4Enterprise Reporting)


Navigation and Row header Context menu

Context menu for Row headers:1. …2. Convert to Formula

Displays the Excel based formula instead ofthe value.

3. Keep Filter Value4. Select Filter Value5. Filter and Drill Down By …6. Swap … with …7. Add Drilldown According To …8. Remove Drilldown9. Swap Axes10. Sort …11. Properties12. Query Properties13. Goto …

Allows navigation to assigned documents, amore detailed query to be executed, a website…using the report-to-report interface (coveredin BI-2 Module 4 Enterprise Reporting)


Navigation and table value Context menu (1/3)

Context menu for table values:1. …2. Properties

Scaling, Decimals, Result display, Sorting…3. ..4. Key Figure Definition

Executes a Web Query of the key figure.5. Create Condition

Restricting display based on values… (e.g. Top 10%, Bottom 20%,Top 5, Bottom 20, Value less than …, Value above …)

6. Goto …Allows navigation to assigned documents, a more detailed query to beexecuted, a website …using the report-to-report interface (covered inBI-2 Module 4 Enterprise Reporting)





Creating a condition:1. Setting a Threshold value

2. Selecting a condition




•BEx Analyzer


• SAP BI Enhanced Analytics (Data Mining)


Starting BEx Query Designer

Via Start MenuStart Menu

All ProgramsBusiness ExplorerQuery Designer


BEx Query Designer Menu & Toolbar

BEx Query DesignerToolbar functions

BEx Query DesignerMenu functions


Queries can be created by performing the following steps:• Mandatory Select the InfoProvider for which the query is developed.

• Mandatory Select characteristics from the InfoProvider as default values.

• Optional Define restrictions for characteristics.

• Optional Use variables for characteristic values, hierarchies, hierarchy nodes,formulas, or texts.

• Mandatory Select key figures from the InfoProvider.

• Optional Define formulas for calculated key figures.

• Optional Define exceptions for key figures.

• Mandatory Arrange the characteristics and key figures in rows or columns todetermine the layout of the query.

• Mandatory Save the query.

Query Definition Procedure


Creating a Query (1/3)






Executing a Query via BEx Web


Advanced Queries

•To make querying more useful, key figures and characteristicscan be defined in complex ways

Restricted key figures (also called selection when designed at the querylevel)Calculated key figures (also called formula when designed at the query level)Currency translation

•Exceptions

•Conditions


Restricted Key Figures

Eg. You want to list sales volume (key figure) for individual 2008 and2009(characteristic) next to each other in a report.

You can create a restricted key figure at the InfoProvider level

The key figure is restricted by one or more characteristic selections

This is unlike a filter, whose restrictions are valid for the entire query (all keyfigures)

A restricted key figure, only the key figure in question is restricted to its allocatedcharacteristic value.

When defining a restricted key figure, select the restricting characteristics. You canselect value ranges using – between, greater than, etc.

You can include values in the selection or exclude values from the selection

A restricted key figure can also be created locally (query level) – called a Selection


Calculated Key Figures

You can use a formula to calculate key figures that are not in the InfoProviderEg. Percentages, Counts, Max, Division etc.Eg. Boolean operators

Calculated Key Figures can be defined at the InfoProvider level (New calculatedkey figure) or the query level (New Formula)


Constant Selection

The function Constant Selection allows you to mark a selection in the Query Designeras constant.

This means that navigation and filtering have no effect on the selection.


Exceptions

Exceptions are used to highlight deviations (in key figures) in reportsDeviations from expected values should be noticed by users.Threshold values with different colors are used to define queries

To create an exception, go to Exception in the Query DesignerNew ExceptionProvide a descriptionChoose the key figuresDefine Exception valuesSave and execute the query

Exception can be made active or inactive



Conditions

•Conditions are used to limit the report to the most important results (filter)• Choose New condition in query designer• Provide a description, Edit• Choose either all or single characteristics• Then the key figures and the condition rules• In BEx, you can activate some or all conditions


Charts

•You can add an Excel chart to your report

•Click on Chart button

•You can modify the chart properties using Excel functionality




•BEx Analyzer


•SAP BI Enhanced Analytics (Data Mining)


Introduction to Data Mining

Data Mining ProcessData Mining Methods

Data Mining Case Studies

Resources

SAP University AlliancesModule BI1-M6



The majority of reports are based on knownfacts

BUT

We don’t know what we don’t know


What is Driving Data Mining?

Changes in Technology:Increased usage of the InternetAppearance of data warehousesIncrease in computing powerBetter modeling approaches

Changes in Competition:Evolution of strategies:

Mass marketing vs. One-to-Onemarketing

Increased competitionFast-paced environmentEmergence of niche players

Changes in CustomerBehavior:

Better informedMore demandingIncreased willingness to switch tocompetitorsEvolution of needs: morecomplex, harder to satisfy


Definition

Data mining is the process of discovering meaningful newcorrelations, patterns and trends by "mining" large amounts ofstored data using pattern recognition technologies, as well asstatistical and mathematical techniques.(Ashby, Simms (1998))


Data Mining Examples

Market BasedAnalysis and Up-

Selling/Cross-Selling

PharmaceuticalIndustry:

Drug Effectivenessby Patient Type

Defect Analysisin

Manufacturing

University andEmployee

Recruitment

EmployeeTurnover

Predictions

CreditRisk

Determination

CreditCardFraud

CustomerGrouping and

BehaviourPrediction





Resources

SAP Business IntelligenceModule 6


CRISP DM: Overview


Knowledge Discovery in Databases (KDD)

Knowledge Discovery in Data is the non-trivial process of identifying –valid -novel -potentially useful -and ultimately understandable patterns in data..

Advances in Knowledge Discovery and Data Mining, Fayyad,Piatetsky-Shapiro, Smyth, and Uthurusamy, (Chapter 1), AAAI/MIT Press 1999


SAP BI Analysis Process Designer (APD)


APD





Resources



Data Mining Models – PredictiveSupervised Learning


Data Mining Models – ExplorativeUnsupervised Learning


Customer Income Age Credit Rating Etc. BuyingBehavior

SelectedCustomers -HistoricalData(query)

Mick Jagger $ 10 000 48 Excellent … Yes

Elton John $ 3000 22 Fair … No

Tina Turner $ 8000 36 Excellent … Yes

Etc. … … … … …

How willotherCustomersbehave?New Data(query)

Willie Nelson $ 6500 34 Fair …

Carol King $ 2000 63 Excellent …

Etc. … … … …

• Identify the factors driving customer behavior andpredict future behavior

?

?

?

Predictive: Decision Tree


Model process:

A record in the query starts at the root node

A test (in the model) determines which nodethe record should go to next

All records end up in a leaf node

Interpreting the ResultsRead the tree from top to bottom

Rule:If Age is less than 35 and

Income is greater than $5000 andCredit standing is Fair, then the customer

has a 35% chance of buying theproduct

Age, then Income and credit rating, are themost influential attributes determining

buying behavior.

Age

IncomeBuy100%

Won’t Buy100%

CreditRating

Buy35%

Won’t Buy65%

Leaf Nodes

Root Node

DecisionNode

<35>= 35

>$5000<=$5000

FairExcellent

Test

Predictive: Decision Tree


Decision Tree: Practical Applications

How can we reduce customer fraud?Analyze customer characteristics:

Fraudulent behavior (Y or N), age, education, occupation, frequency of purchase,dollar value of purchase, etc.

Who is likely to “churn” (stop buying from us)?Analyze customer characteristics; who is:

(1) still with us, and(2) no longer “on board”,Plus other demographic or transactional attributes...

Who is likely to be a credit risk?Analyze customer characteristics: who has:

(1) not been a credit risk in the past, and(2) who has been a credit risk in the pastInclude relevant customer characteristics


Weighted Score Tables

Customergroups)

Age Points(Age)

Income Points(Income)

Region Points(Region)

Weight 30% 50% 20%

1 10 – 19 7 25 000 2 South 5

2 20 – 29 10 50 000 5 West 3

3 30 – 39 2 120 000 8 East 7

Calculated score for Customer 2:= (10 x 30%)+ (5 x 50%) + (3 x 20%) = 6.1

Use weighted scoring to rankcustomers according to the

importance of certain attributes.


Predictive: Regression

Linear Regression

Nonlinear Regression

Use regression to predictthe impact of one (or

more) on another.

Example: impact of pricereduction on sales in

Regions NY, PA and TX.

Example: Impact of age,income, HH size, region,length of subscription oncanceling a subscription


Informative: Clustering

Clustering is a data mining technique that creates groups ofrecords that are:

Similar to each other within a particular groupVery different across different groups

The degree of association between members is measured by allthe characteristics specified in the analysis

Clustering helps the user explore vast amounts of data andorganize it in a systematic way


Income

Age

High

Low High

Informative: Clustering


Informative: Clustering Process


Informative: Association Analysis

Association Analysis uncovers the hidden patterns, correlationsor casual structures among a set of items or objects.

It is typically used for Market Basket Analysis (MBA).

It allows the user to:Understand and quantify the relationship between different items (e.g.products, clickstream, etc...)Group different items by affinityCreate readily-understandable rules describing ....Organize web pages in order to optimize user accessibility


Association AnalysisData Mining

Cross-SellingRules

C

D

D

AB

E

E

E

A

Customers

Products

B

C

D

What products /services are

typically boughttogether?

Export rulesto Web Shop

Use inmerchandising

Informative: Association Analysis - Example


Informative: Association Analysis - Measures


Informative: ABC Classification

Use ABC to classify objects (such as customers, employees, vendors orproducts) based on a particular measure (such as revenue or profit).

Examples:Customers with revenue >$100M = Class “A”, etcCustomers who generate top 20% of our revenue = Class “A”, etcRank customers by their revenue:

The top 20% on the list = Class “A”, etc ORThe first 50 customers = Class “A”, etc

Practical applicationsClassify customers into Platinum, Gold, SilverRank vendors based on product quality (returned goods)


Informative: ABC Analysis - Example





Resources



Data Mining: Terrorism

On September 14, 2001

Seisint’s ArtificialIntelligence

Billions OfPublic Records

FAA Public RecordInformation

Seisint’s DataSupercomputer

+ + +

• Five Were Active FBI TerroristInvestigations

• Including Hijacker:Marwin Youseff Alsherri

• Delivered List to AuthoritiesPrior to Names Being Made

Public

Within 16 Hours Seisint Delivered 419 Names of Interest


Data Mining: Examples

Banking

Lloyds TSBSaved $35 million by reducing credit card fraud

HSBC4x more leads, 37% more asset potential

Bank Financial7x increase in response rates, 80% reduction incosts

Insurance

AegonGenerated $30M additional revenue in servicecall center

FBTODecreased direct mailing costs by 35%,increased conversion rates by 40%, increasedprofit by 29%

Telecommunications

Verizon WirelessCut churn by 20%, saved 33% of“at-risk” clients and reduced marketing costs by 60%

TelstraIncreased sales in call centers by 120%

Other industries

ExperianGenerated $2.5 million in catalog revenue whilereducing hardware and software maintenance costs by80%

Center ParcsAdded $3 million to their bottom lineReduced mail costs by 46%

Sofmap.com (retail)Tripled profitability of online store

De Telegraaf (media)Reduced acquisition cost per subscriptionby 90%

www.spss.com/events/e_id_2247/presentation.ppt





Resources



Data Mining: Resources

Data Mining Resources Bloghttp://dataminingresources.blogspot.com/

Data Mining@CCSUhttp://www.ccsu.edu/datamining/resources.html

The Data Warehousing Institutewww.tdwi.org

Session BBusiness Intelligence with SAP BusinessObjectsSoftware

Christine Davis

SAP BusinessObjects UniverseSAP BusinessObjects Web IntelligenceXcelsiusSAP BI and Portal Integration


Resources

SAP University Alliances community http://www.sdn.sap.com/irj/uac

Collaboration workspace from SAP https://cw.sdn.sap.com/cw/index.jspa

Business Intelligence workspace: content and discussionshttps://cw.sdn.sap.com/cw/community/uac/bi

SAP BusinessObjects Community http://www.sdn.sap.com/irj/boc

University of Arkansas, Walton College Enterprise Systemshttp://enterprise.waltoncollege.uark.edu/

University of Southern California, Viterbi School of Engineering, InformationTechnology Program/SAP Program http://itp.usc.edu/sap

ContactChristine Davis Nitin Kale

University of Southern California3650 McClintock Ave, OHE 412Los Angeles, CA 90089

T: +01 (213) 740 – 7083F: +01 (213) 740 – 1051

[email protected]


Thank you!


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Documents

SAP Curriculum Congress 2010 - PBworksathensstatesapfaculty.pbworks.com/f/Congress_2010_SAP...BEx Analyzer BEx Query Designer SAP BI Enhanced Analytics (Data Mining) Session B: Business