dr. bjarne berg

© 20010 Wellesley Information Services. All rights reserved.

What you need to know to get the most out of using

SAP NetWeaver BW as yourenterprise data warehouse

Dr. Bjarne Berg

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What We’ll Cover …

• Introduction• The EDW architectural options

Federated Data Warehouse Centralized Data Warehouse Distributed Data Warehouse

•Data Integration challengesMasterdataTransaction data conversionData cleansing

•Designing for Flexibility •The Support Organization•The top 10 EDW pitfalls •Wrap-up

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We will take a detailed look at the pros and cons of your EDW architectural options, including federated, centralized, and distributed EDW models, and explore when each approach is appropriate.

Learn how to interface The Support Organization and how to consolidate different master and transactional data.

Weigh your options for building a centralized or a decentralized EDW support organization.

Examine the top 10 pitfalls companies face when implementing SAP NetWeaver BW as their EDW and how to overcome them.

In this session.

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A Quick Definition: BI Vs. Data Warehousing

Data warehousing is the act of extracting, transferring, transforming, storing and retrieval of data for reporting and analytical purposes.

Business Intelligence (BI) is a terminology for applications that uses data stores for analytical purposes.

BI applications are not required to run on top of data warehouses, but the

majority does

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A Logical Enterprise DW Architecture

Metadata

DataExtractionIntegration

andCleansingProcesses

Custom Developed Applications

DataMining

Statistical Programs

Query Access Tools

Data Resource Management and Quality Assurance

SummarizedData

SegmentedData Subsets

Functional Area

Summation

Marketingand Sales

Purchasing

CorporateInformation

Product Line

Location

PurchasingSystems

InvoicingSystems

GeneralLedger

External DataSources

Other InternalSystems

Translate

Attribute

Calculate

Derive

Summarize

Synchronize

Source Data ExtractOperationalData Store Transform

DataWarehouse BI Applications

Source: Bjarne Berg, “Introduction to Data Warehousing”,1997

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The Federated Data Warehouse (FDW) Architecture

Metadata

SAP BOBJ OLAP

Universes

Ad-HocWebi

OLAPPioneer

DashboardsXcelcius

Batch reportsCrystal


Security

Training

User Support

Projects

Ad-hoc

Synchronization

IT Developed Semantic Layer

IT Support & Development

Business Driven BI Applications

IT Driven Data Warehouses

BEx Explorer

SAP BWA

Data Warehouse(s)

DW ODSs

DWStar-schemas

SAP BW(s)

SAP DSOs

SAPBW InfoCubes

SAP BOBJ SQL

Universes

Direct Connections

Custom and 3rd party

SAP BOBJ Data Services

BPC

External Applications

Financial Report center

Enterprise Portal

SalesReport center

ManufacturingReport center

HRReport center

Partner facingReport center

Ad-HocReport center

Customer facingReport center

Users

Employees

Customers

Partners

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Federated Data Warehouse (FDW) Architecture

• Federated Data Warehouses are best in very large organization where development is separated by geography, organizational boundaries, or where multiple data warehouses exists due to mergers & acquisitions.

• To make FDWs successful, there needs to be a rapid convergence to standardized technologies. This include:

Same type of databases and support pack levels (costs and compatibility)Same technical platforms Hardware, Backups and Archiving (costs)Shared Portal and user interface strategy (reduced training and support)Shared security design and centralized administration (risk management)

If the data is federated you gain faster response time to business needs, can execute multiple projects in parallel, and work 24/7 across the globe. But without any standardization, it can also be very costly.

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The Centralized Data Warehouse (CDW) Architecture

Metadata

SAP BOBJ OLAP

Universes

Ad-HocWebi

OLAPPioneer

DashboardsXcelcius



Security

Training

User Support

Projects

Ad-hoc

Synchronization





BEx Explorer

SAP BWA

SAP BW

SAP DSOs

SAPBW InfoCubes

SAP BOBJ SQL

Universes

Direct Connections



BPC



Enterprise Portal

SalesReport center


HRReport center


Ad-HocReport center


Users

Employees

Customers

Partners

OLTP sourcesSAP ECC

Siebel, JDEOracleOthers

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Centralized Data Warehouse (CDW) Architecture

• Centralized Data Warehouses are great for small and mid-size data warehouses (less than 15-40Tb). There are great benefits in terms of the ease to mange upgrades, support packs, enforcing development standards, transport control, master data management and the overall total cost of ownership

• To make CDWs successful, there needs to be: Adequate funding of hardware, application servers, database servers Serious consideration should be made to move BI and reporting to BWA Focus on using the database capacity on storage and data loads-- not queries No direct reporting from DSOs (takes too much system resources) Broadcasting , caching and performance tuning is a dedicated support effort A plan for data partitioning and archiving needs to be in-place as soon as the

system exceeds 5-8 TB.

If the data is centralized it is faster to develop new solutions for the business and merging from different data sources are easier

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The De-centralized Data Warehouse (DDW) Architecture

Metadata

SAP BOBJ OLAP

Universes

Ad-HocWebi

OLAPPioneer

DashboardsXcelcius



Security

Training

User Support

Projects

Ad-hoc

Synchronization





BEx Explorer

SAP BWA

SAP BW(s)

SAP DSOs

SAPBW InfoCubes

SAP BOBJ SQL

Universes

Direct Connections



BPC



Enterprise Portal

SalesReport center


HRReport center


Ad-HocReport center


Users

Employees

Customers

Partners

SAP BW(s)

SAP DSOs

SAPBW InfoCubes

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De-centralized Data Warehouse (DDW) Architecture

• A Decentralized Data Warehouses makes sense if there are logical division between business units, geographies and little shared reporting I.e. in a conglomerate organization with diverse business units.

• The benefits of DDWs include the flexibility of the FDW with the technology standardization and lower cost of ownership of the CDW. To make DDWs successful, there needs to be:

A formal Masterdata Management (MDM) strategy with clearly defined standardsA rule based data cleaning and data integration plan for centralized reportingA shared hardware location to keep costs lowerTight integration with upgrades, support packs and interface standards

With DDWs there is a risk of creating stove-pipe data marts that cannot be integrated at the corporate level without very high costs.

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Recommendations CDW, FDW and DDW ArchitecturesFederated Data Warehouse

(FDW)Centralized Data

Warehouse (CDW)De-centralized Data Warehouse (DDW)

Best for very large organization where development is separated by geography, organizational boundaries, or where multiple data warehouses exists due to mergers & acquisitions.

Best for small and mid-size data warehouses in organizations.

If there are logical division between business units, geographies and little shared reporting I.e. in a conglomerate organization with diverse business units.

Max. Size Virtually unlimited 40+ Tb Virtually unlimited

Use same type of databases, ETL tools and support levels (costs & compatibility)

Adequate funding of hardware, application servers, database servers

A formal Masterdata Management (MDM) strategy with clearly defined standards

Use the same O/S, Hardware, Backups and Archiving systems (costs)

Implement BWA A rule based data cleaning and data integration plan for centralized reporting

Shared Portal and user interface strategy (reduced training and support)

Use the database capacity on data loads not queries

Use a shared hardware location to keep support costs lower

Shared security design and centralized administration (information risk management)

Direct reporting from DSOs should not be allowed

Tight integration with upgrades, support packs and interface standards

Performance tuning should be a dedicated support team effort

Issues

Without any standardization, it can be very costly.

Performance can be poor. An archiving plan is essential when the system exceeds 5-8 Tb.

There is a risk of creating stove-pipe data marts that cannot be integrated at the corporate level without very high costs.

Success factors

Organization

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The 3-Tiers of Information Management

For all data warehouses 60-80% of the effort is to move, store, retrieve and integrate data from various source systems.

From a SAP perspective, Information management is six distinct efforts. Therefore, several SAP BI tools exists with different capabilities

ApplicationsERP, SCM,

CRMBusiness

IntelligenceData Synchronization &

MigrationPerformance Management

Information Management

Data Federation

Data Integration

Text Analysis

Metadata Mgmt.

Masterdata Mgmt.

Data Quality

Structured UnstructuredData Data

RDBMS

ERP

RDBMS

ERP

Notes

Email

Web

Docs

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The XI Data Services Architecture

Data integration in an EDW can be done with SAP BOBJ Data Services. The tool architectural can be illustrated in terms of source data, process and target data.

Process Data Validation

Data Cleansing

Data Auditing

Data Profiling

SourceData

PeopleSoft

Oracle Apps

Data Services Engine

Siebel

SAP R/3

Oracle DB

SAP BI NetWeaver

SQL DB

DB2

XML

Files

Mainframe Excel

OthersSAP ECC

TargetDataTargetData

PeopleSoft

Oracle Apps

Siebel

SAP R/3

Oracle DB

SAP BI NetWeaver

SQL DB

DB2

XML

Files

Mainframe Excel

OthersSAP ECC

Pre-delivered connectors to systems and databases

Databases1.Oracle2.SQL Server3.IBM DB24.Sybase & IQ5.MySQL6.Informix7.Teradata8.Netezza9.ODBC

Databases1.Oracle2.SQL Server3.IBM DB24.Sybase & IQ5.MySQL6.Informix7.Teradata8.Netezza9.ODBC

Applications1.SAP R/3 & ECC

– ABAP– BAPI– Idoc

2.SAP NetWeaver BI3.JD Edwards4.Oracle Apps5.Siebel6.Salesforce.com7.PeopleSoft

Applications1.SAP R/3 & ECC

– ABAP– BAPI– Idoc

2.SAP NetWeaver BI3.JD Edwards4.Oracle Apps5.Siebel6.Salesforce.com7.PeopleSoft

Transports & File formats1.XML2.SOAP -Web Service3.Cobol4.HTTP5.JMS6.Excel7.EBCDIC8.Text fixed width9.Text delimited

Transports & File formats1.XML2.SOAP -Web Service3.Cobol4.HTTP5.JMS6.Excel7.EBCDIC8.Text fixed width9.Text delimited

MainFrames1.Enscribe2.ADABAS3.IMS/DB4.RMS5.VSAM6.ISAM

MainFrames1.Enscribe2.ADABAS3.IMS/DB4.RMS5.VSAM6.ISAM

Non-Structured Data•30+ languages•Any fileformat

Non-Structured Data•30+ languages•Any fileformat

All major platforms are supported with pre-delivered connectors that can be installed for data movement

The high-performance parallel data processing also supports gridcomputing platforms for batch and real-timeexecution

Extraction and data movement may take 30-50% of the time in a process chain. Therefore, do not plan to build an EDW with slow ‘non-native’ connectivity to the source systems.

Reconciliation Between Systems

The majority of time spent on maintaining a complex EDW is the time spent on reconciliation of the data

You have to prove that the data in the warehouse is equal to the data you extracted, or your financial reporting systems will have no credibility.

You are also legally required to have a reconciliation process that can be tracked, if you use the warehouse for financial reporting to external entities.

Reconciliation Between Systems- Dashboards

Many companies invest in developing manual control queries, while others use reconciliation products that are powered by SAP NetWeaver

An example of a reconciliation Dashboard built on SAP BW. In this example:

1. A reconciliation memo was written on Feb. 1st 2. PCA reconciliation between BW and R/3 failed

on Feb. 16th

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Interesting use for SAP NetWeaver BI

Using BOBJ Data Services you can consolidate data from many source systems, cleanse and integrate them before you send it to SAP BI. This avoids multi-nested DSOs and complex load logic.

Source systems- Oracle- JDE- Peoplesoft- Baan- Siebel- Custom- Hyperion- Other.

http://www.changing-dimensions.com/Cube.jpg/Cube-full;init:.jpg

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Interesting use BOBJ Data Services

Using BOBJ Data Services you integrate, cleanse and merge data from source systems during

1) ECC implementation projects, 2) Retirement of legacy systems, 3) Mergers and Acquisitions.

Source systems- Oracle- JDE- Peoplesoft- Baan- Siebel- Custom- Hyperion- Other.

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Data Cleansing Capabilities

The Data Profile Tab in BOBJ Data ServicesThis tab in the “view data” screen contains data profile statistics on each column that can help you decide on the quality of the input data.

The system automatically captures the following statistics in a profile grid.

1. Column Name2. Number of distinct values in a column3. Number of records with a NULL value in this column4. Maximum & Minimum value of the column

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The ValidationValidation allows you to create rules for cleaning data prior to loading it to the system. You can have a pass rule and an 'Action on Failure' that can provide complex logic.

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The AuditThe Auditing selection allows you to take complex actions when the data quality is poor.

You can:

1. Send an email to an administrator

2. Load the data to a table for later correction

3. Modify the data through scripts

4. Create custom functions for your own processing logic

Universal Data Cleansing: Example of Enhanced Party Masterdata

Source: SAP AG, 2009

You can also add new items such as geocodes for visualization in SAP BI I.e. maps

You can add new characteristics to the data such as:

1) Legal tax jurisdictions 2) Census track ID3) Block group ID4) Insurance rating territories5) Tax authority name6) Tax authority FIPS codes7) Longitude & Latitude8) City type9)...

GREAT FEATURE: The Census track ID allows you to analyze your customers and partners using government census information

Universal Data Cleansing: Customer Aggregating & Discovery

A common way to look at customer data is by Households instead of single records.

BOBJ DQ allows you to look at customer's addresses and create shared master records, customer mapping keys, aggregating data (i.e. aggregated sales data for the household), check "no-call" lists, examining churn (apparent customer turn-over).

You can also integrating all master data from many records into a single "super record" that contains all the unique master data you have about a single customer or partner.

Universal Data Cleansing: Data integration & BAS

SAP Data Quality Management has pre-delivered content for many solutions including CRM -> ECC integration, including:

1) Across platform search capabilities2) Automated address correction 3) De-Duplication of records4) Direct system connection (no file extraction)5) Supported for all major releases: R/3 4.6c; ECC 5 and 6; CRM 4 and 5

The Business Address Service (BAS) feature can:1) Use Postal reference files from 190 countries to clean address, including

suggestion lists2) Data scans and searches in SAP for duplicate records using partial user input.

"Data Quality Management for SAP provides a prepackaged native integration of data quality best practices within the SAP environment using the BOBJ Data Services platform"

SAP AG, 2009

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What is New in BOBJ Data Services

Expanded matching capabilities to allow the business user to select other fields (beyond street name and zip code) within the generation of break keys.

An improved method to install the functionality of this product into your IC WebClient or CRM IC WebClient environment. To do so, you add a Component Usage to the Component to which you want to add Postal Validation.

If you have purchased the geocoding option for this product, geocoding allows you to return latitude, longitude, and relevant status information for a U.S. address record

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SPO in SAP BW 7.2 can Partition Objects Automatically

• In BW 7.2 a new feature called "Semantic partitioned object" (SPO) is introduced to help partition InfoCubes for query performance, and DSOs for load performance.

SPOs can be added to MultiProviders for easy query administration and to mask complexity

Source: SAP AG, 2010

• BW 7.2 provides Wizards to help you partition objects by year, business units or products.

• BW also generate automatically all needed DTP such as transformation rules and filters to load the correct infoProvider.

• Maintenance is easier since any remodeling only need to change the reference structure.

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With BW 7.2, you can have data in BWA, InfoCube are not required.

Once you exceed a few hundred critical users and/or 3-4 Tb of data you should seriously consider BWA

Some of SAP reference clients

BWA is no longer exotic. Many large SAP-BI customers

have already implemented BWA & projects are under way in Europe, Asia and the Americas.

BWA is becoming mainstream and enhanced in BW-7.2

NikeNike

NikeNike

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IT cannot hold BI ‘hostage’ with long delivery times and slow responses to changing user demands.

The only way to be successful is to provide flexible data structures and cleansed, integrated data to the business and let the business groups take over the BI development.

So what is needed is a stronger emphasis on scalable, fast IT solutions and a ramp up of BI capabilities of the business units.

Keeping BI front-end solutions such as Webi, Visual Composer and Pioneer in the hands of IT instead of the business will create inflexible systems that are unlikely to succeed.

Separate the Data Warehouse from the BI solutions

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BI Support Organization — Big Picture

You need to separate the operations of BI systems from the project work

If there is no support organization, the BI system quickly becomes an orphan when the project ends

Without a support org. there is a risk that future BI projects are delayed sincethe project team has to support previous projects

The BI Help Desk — Level 1 Support

The first level support should be done by Power Users in the organization

You will have to train these resources, empower them to make changes, and leverage them as much as possible, even when it is easy to “jump to solutions”

Query related support tickets from a central location/Web site should be routed to the

power users in each department.

The power user can escalate the ticket to Level- 2 support if he/she is unable to resolve it.

The BI Help Desk — Level 2 Support

The second level support is used for issues that are not related to queries, presentations, reports, and formatting

This include data loads, performance, security, availability, training schedules, etc.

This is addressed by the central support team

Some support ticket types are always routed to Level 2 support.

It is important to have a generic email address for Level 2 support that is not related to an individual. Emails to this address should not be deleted.

TrainingProject Stack

Break fix and Production stack

Break-Fix - Splitting Projects & Support Environments

By Introducing a Break-Fix (BWB) environment, the support team can correct break-fixes and move code into the Testing environment (BWQ) and Production environment (BWP) without impacting the project team

Transports can be captured in the buffer and moved to the Development environment (BWD) on a periodic basis

BWD

BWSBWT

BWB BWQ BWP

The Break-Fix and production stack as well as the training environment is owned by the support team.

The project teams own the development and Sandbox environments (BWS and BWD).

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Pitfall #1: Lack of Reasonable SLA with EDW Support Team

Some examples of reasonable performance include:

1. 90% of all queries run under 20 seconds2. System is available 98% of the time3. Data loads are available at 8am — 99% of the time4. User support tickets are answered within 30 minutes

(first response)5. User support tickets are closed within 48 hours — 95% of the time.6. System is never unavailable for more than 72 hrs — including

upgrades, service packs, and disaster recovery7. Delta backups are done each 24 cycle and system backups are

done every weekend

More EDW Pitfalls….

Pitfall #2: Jack-of-all-trades Master of none….BI is complex with many different tools and technologies. Don’t rely

on a single person with no specialized skills. Make each person responsible for a focused technology/task.

Pitfall #3: An army of ‘Architects’ who don’t understand SAP.Have one ‘architect’ – quality is more important than quantityArchitecture is technical by nature. PowerPoints only gets you a

small part of the way.The BI architect should know the technology better than anyone in

the room and be able to design solutions.

More EDW Pitfalls….Pitfall #4: Not separating the Support Team from the Project team

Keeping the ‘lights-on’ is a core focus area. Many EDWs fail because of lack of training, production and user

support, and by having nobody around to do continuous improvements.

Pitfall #5: A Firm Belief in Monolithic Data WarehousesGoogle runs on over 500,000 servers, why must your data warehouse

run on one?Divide and concur when the performance becomes a too-large

problem.Separate BI onto SAP BWA and use the data warehouse for data

movement and data storage.You don’t need a monolithic castle, but storage & performance


Pitfall #6: Analysis Paralysis.You will never have perfect EDW requirements – get over it….The business will change and so will the BI system. Change is a sign

of success not failures (people who cares wants to make it better).Not moving forward and keep analyzing is a costly decision…

Pitfall #7: A Single User Interface will solve all my EDW problems..There are no magic bullets. Most companies need 2-3 end user tools.Start with OLAP (Pioneer) web, then continue with ad-hoc querying

(Webi), and finalize with dashboards (Xcelcius). All other tools are great, but not a starting point.

Remember you first crawled and walked before you ran.


Pitfall #8: Enforce EDW StandardsStandards are not a word document buried in a file cabinet If you allow ‘exceptions’ the standards quickly become meaningless. It costs to keep your house clean, but data management and data

integration will benefit greatly from it. Remember: “the road to hell is paved with good intentions” - unknown.

Pitfall #9: Keep Your EDW Support Team motivatedThe average application developer stays on the job for 47 months, the

average support person is only there for 25 months! It is very expensive to use the support team as a training ground for

technical staff and it hurts performance.Make the support team a ‘cool’ place to work with flexible hours and defined career paths.

Final EDW Pitfall.

Pitfall #10: Not Creating a ‘BI Technology Advisory Board’ for the EDW Use ad-hoc best practice advise from external experts on an

periodic basis. If you are struggling with something, there are many others who

have ‘cracked the nut’ already – leverage their experiences. Attend BI conferences, take good notes and leverage the many

experts at the booths, the speakers and the forums. You are not alone, but your team needs to get ‘plugged into’ the

many ASUG, BI Expert, SDN and SAP BI communities.

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Resources

• Support Organizations - ppt download with more details http://www.comeritinc.com/Downloads.htm

• Implementing Enterprise Data Warehousing: A Guide for Executives by Alan Schlukbier

• Efficient SAP NetWeaver BI Implementation and Project Management by Gary Nolan

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7 Key Points to Take Home

• There are more than one way to architect an EDW. However, you need to make sure your BI solution is designed, not evolutionary

• Consider FDW and DDWs when data volumes are extremely high or your company just underwent a merger or acquisition

• Make the front-end independent from the backend

• Formalize a data integration strategy with MDM and Reconsolidation as key focus areas

• Invest in people, not just technology –Great support staff is key to EDW success

• SAP BWA should be part of your EDW strategy unless you are a tiny company

• Create a BI technology advisory board and have periodic meetings

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Your Turn!

How to contact me:Dr. Bjarne Berg

[email protected]

Disclaimer

SAP, R/3, mySAP, mySAP.com, SAP NetWeaver®, Duet™, PartnerEdge, and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG in Germany and in several other countries all over the world. All other product and service names mentioned are the trademarks of their respective companies. Wellesley Information Services is neither owned nor controlled by SAP.

dr. bjarne berg

Documents

transactional data

data extract

data stores

retrieval of data

data warehouses

yourenterprise data

edw pitfalls wrapup

distributed edw models