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BusinessObjects™ Information Management for

Data Integration and Quality Assurance

Anthony WaiteBusiness Objects, an SAP Company



© SAP 2008 / Page 1

1. Why Information Management (IM)?

2. Data Integration and Data Quality Management with BusinessObjectsData Services

3. Wrap-up

Agenda



© SAP 2008 / Page 2

You Have Growing Mountains of

Information Across Your Enterprise

Spreadsheets

ERP

Marketing CRM

Data Warehouse

Budgeting

Data redundancy and inconsistency

Limited cross-functional analysis



© SAP 2008 / Page 3© SAP 2007/Page 3

IT

Is Your IT Organization Able to Keepup with Information Demands?

Business

Need timely access totrusted data

Changing businessrequirements

Making decisions withknowledge shadows

Limited capacityto support users

Competing priorities

Lengthy Extract,Transform, and Load(ETL) and data qualitydevelopment cycles

INFORMATIONGAP



© SAP 2008 / Page 4

Customer Challenges

Information fragmentation

Information is locked away in application silos and heterogeneous sources

Inconsistent hierarchy, dimensions, taxonomy, and definitions across the enterprise

It is too difficult to find information across the enterprise

Need to deliver trusted data

Data quality is a top issue for CIOs

Users lack information context to effectively make confident decisions or addresscompliance requirements

Information governance is a necessity for delivering trusted information yet few

organizations have this figured out

Flexibility to respond to change

IT struggling to keep up with rapidly changing business requirements

Tools complexity leads to steep learning curve and extended development cycles

Disconnect between business and IT leads to misunderstandings, rework, and unreliable

information



© SAP 2008 / Page 5

Where Information Management is

Mission Critical

Business intelligence (BI) — Aggregate and align data for operational analytics,

performance management, etc.

Master data management (MDM) — Support master data stores, publish master data changes to all consumers

Data migrations/consolidations — Single-time or ongoing translation, delivery,

etc., of data from legacy environments/applications/databases

Synchronization of data between operational applications — Ensuredatabase-level consistency across applications, uni- or bi-directional, intra- or inter-enterprise

Creation of a single view of "X" — Aggregating from multiple sources, often inreal time, for operational purposes

Delivery of data services — Providing data access, transformation, etc., asservices within service-oriented architecture (SOA)

Unification of structured and unstructured data — Extend data integrationpractices and tools across the content continuum


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Common Issues withEntry-Level Tools

Has Your Experience with Data

Management Tools Been Frustrating?

Common Issues withEnterprise-Class Tools

Months of trainingrequired

Expensive, specializedskills needed

Many product interfaces

Lengthy developmentcycles

Coding required

Weak data qualityfeatures

Many productinterfaces

Change managementis not easy


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Structured Data Unstructured Data

ERP DW RDBMS OLAP Email Docs Notes Web

Information Management Offering

BusinessIntelligence

Applications

PerformanceManagement

ERP, CRM, SCMData Migration

Synchronization

INFORMATION MANAGEMENT

Data Integration

Data Federation

Data Quality

Master Data Management

Metadata Management

Text Analytics

Provides open and agnostic information management capabilities with therichest integration for both SAP and Business Objects environments

DW = Data Warehousing


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1. Why Information Management (IM)?

2. Data Integration and Data Quality Management withBusinessObjects Data Services

3. Wrap-up

Agenda



Addressing Data Integration and Data Quality

Challenges

© SAP 2007 / Page 11

SeparateDevelopment

Teams

Not Addressing

at the Moment

35.6%

23.9%

40.4%

SameDevelopment

Team

Majority of organizations have separate development teams for dataintegration and data quality

Source: Data collected from data management professionals in recent Web seminars hosted by BusinessObjects, April 2008

How are you resourced to address data integration and data quality challenges

today?



Importance of Having a Single Application

for Both Data Quality and Data Integration

© SAP 2007 / Page 12

55.4%

7.8%

36.8%

Source: Data collected from data management professionals in recent Web seminars hosted by BusinessObjects, April 2008

Over 93% agree that is important to have a single application for both dataquality and data integration

VeryImportant

NotImportant

SomewhatImportant

How important is having a single application for both data quality and data

integration for your organization?



One Platform: BusinessObjects Data ServicesData Quality (DQ) And Data Integration (DI)

One development user interface

One runtime engine to process both DIand DQ functionalities

One metadata repository

One administration, security,

data connectivity, and Web servicesenvironment

One RuntimeEngine

One Development UI

One Metadata Repository

Transform

Cleanse

Deliver

Access

Service-Oriented ArchitectureBusinessObjects Data Services XI 3.0

ONE



SAP ERP, SAP CRM,

SAP NetWeaver MDM,SAP NetWeaver BI,

…

SOA

Shared MetadataImpact Analysis

Data Lineage

D a t a p r o f i l i n g

BusinessObjects Data Services

Architecture

DataServicesEngine

DataAuditing

Data

Validation

DataCleansing

RealTime

Batch

Files, XML,Mainframe,Excel, etc.

Oracle, SQL,DB2, etc.

PSFT,Oracle Apps,Siebel, etc.

SAP ERP,SAP NetWeaver ® BI

Query,

Reporting,Analysis& Dashboards

Data Migration,Synchronization, …

SAP NetWeaver BI



Oracle

DB2

Sybase ASE & IQ

SQL Server

Informix

Teradata

ODBC

MySQL

Netezza

HP NeoView

JD Edwards

Oracle Apps

PeopleSoft

Siebel

Salesforce.com

SAP BI

SAP ERP

– ABAP – BAPI

– IDoc

Text delimited

Text fixed width

EBCDIC

XML

Cobol

Excel

HTTP

JMS

SOAP(Web services)

ADABAS

ISAM

VSAM

Enscribe

IMS/DB

RMS

Both direct andchange data

Enterprise-Wide Data Access

Any text file type

32 languages

Support for structured and unstructured data

Databases Applications Files and transportMainframe(with partner)

Unstructured data



Other

Changes to source systems

System errors

Data entry by customers

External data

Mixed expectations by usersData migration or conversion projects

Inconsistent definitions for common terms

Data entry by employees

Sources of Data Quality Problems

46%

7%

40%

75%

20%

25%

26%

38%

75%

Source: TDWI, March 2006, based on 399 respondents

Data Entry and Inconsistent

Definitions



*MNC = multinational corporation

What Type of Data is Most

Problematic?

The types of data most susceptible to data qualityproblems

Customer data 74%

Product data 43%Financial data 36%

Sales contact data 27%

Data from ERP systems 25%

Employee data 16%International data in a MNC* 12%

Other : 10%



Increase value of data assets with

Data Quality

Increase the value of data

assets

Measure and analyze datathrough data assessmentand continuous monitoring

Cleanse and enhance

customer and operationaldata anywhere across theenterprise

Match and consolidate data at multiple levels withina single pass for individuals,households, or corporations

Improve and automate thedelivery of direct mail andgoods



Data Profiling in Data Services

Need to understand the data before creating an ETL process

Check for missing values (NULL)

Get possible list of values

Visualize the data distribution

Find patterns

Get data ranges (min, max, average) – Identify data domain outliers

Uniqueness of data (distinct values) Referential integrity – Understand relationships

Can also be used to:

Verify results of an ETL load during development

Analyze data for system migrations

Loading additional data such as potential leads or purchased lists



Data Cleansing

Cleanses and standardizes party data such as

names/addresses, emails, phone numbers,Social Security Numbers (SSNs), and dates

Manages international data for over 190countries and reads and writes Unicode data

Removes errors to uncover the true content of

the database

Improves integrity of data to identify matchesand ultimately create a single customer view

Parses and standardizes non-party data

Such as account numbers, product codes,

product descriptions, purchase dates, part

numbers, SKUs, etc.

Utilizes a rule-based parsing and rule editing

architecture for even greater customized results



Data Cleansing (Party Data)

Maggie.kline@future_electronics.comMargaret Smith-Kline phd

FUTURE Electronics

5/23/03

101 6th ave

manhattan

ny

10012

001124367

Salutation: Ms.First name: Margaret

Last name: Smith-Kline

Postname: Ph. D.

Match standards: Maggie, Peg, Peggy

Gender: Strong Female

Company name: Future Electronics

Address 1: 101 Avenue of the Americas

City: New York

State: NY

ZIP+4: 10013-1933

Email: maggie.kline@future_electronics.comSSN: 001-12-4367

Date May 23, 2003

Input record Output record



Data Cleansing (Product Data)

Description

Kallkyle screw

test steel plate 20 x 35 mm

wire 23.33 x 40.50 cm

34 x 60 mm steel plate

steel plate 34,0 60 mm

34.0 x 60,0 mm steel plate

34 x 60 mm steel plate ?

plate

steel plate

Input Parsed output

Product Dimension Type Form

screw Kallkyle

plate 20x35 mm steel test

wire 23.33 x 40.50 cm

plate 34 x 60 mm steel



plate 34 X 60 mm steel

plate

plate steel



Completes records with directory information by

appending name, address, phone number, or email

address

Provides geocoding capabilities for geographic and

demographic marketing initiatives

Provides geo-spatial assignment of customer addresses

for tax jurisdictions, insurance rating territories, andinsurance hazards, etc.

Data Enhancement



Data Enhancement

Phone: (222) 922-9922

Latitude: 40.722970 Longitude: -74.005035

Match quality: Highest quality address

FIPS Code: State: 36 - New York

FIPS Code: County: 061 - New York

FIPS Code: Place: 51000 - New York

Special District: No

City Type: City

Class Code: C1

Incorporation Flag: 1

Taxing Authority Name: New YorkTaxing Authority FIPS Code: 3606151000

Taxing Authority Remittance: 3600000000

Census Tract ID: 360610051001.01

Block Group ID: 360610051001012

Margaret Smith-Kline, Ph.D.Future Electronics101 Avenue of the AmericasNew York, NY 10013-1933

Appended information:Input



Householding data to identify members of same household,

corporation, or any other hierarchy

Identifying ―snowbirds‖

I.e., individuals or households with multiple residencesCreating a panoramic single best record

Preventing firms from doing business with entities on

government watch lists

Providing identity resolution to uncover non-obvious

relationships for fraud detection

Matching and Consolidation

Unlocking the relationships between distinctly different sets

of data



Matching and Consolidation

Ms Margaret Smith-Kline Ph.D.

Future Electronics

101 Avenue of the Americas

New York NY 10013-1933

maggie.kline@future_electronics.com

May 23, 2003

Name: Ms. Margaret Smith-Kline Ph.D.

Company name: Future Electronics Co. LLC

SSN: 001-12-4367

Purchase date: 5/23/2003 Address: 101 Avenue of the Americas

New York, NY 10013-1933

Latitude: 40.722970

Longitude: -74.005035

Fed code: 36061

Phone: (222) 922-9922

Email: maggie.kline@future_electronics.com

I n p u t r e c o r d s

Consolidated record

Maggie Smith

Future Electronics Co. LLC

101 6th Ave.

Manhattan, NY 10012

maggie.kline@future_electronics.com

001-12-4367

Ms. Peg Kline

Future Elect. Co.

101 6th Ave.

New York NY 10013

001-12-4367

(222) 922-9922

5/23/03



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