NW2008_Tips_tricks_EDW_v10.ppt

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In This Session ...

We will explore 6 large-scale EDW implementations, and see how to apply lessons to your strategy and projects.

Examine the difference between an evolutionary SAP NetWeaver BI data warehouse architecture and a top-driven design method.

Compare the results of using a data mart (bottom-up) approach to an EDW (top down) approach, and determine which approach best fits your requirements.

Explore the ways in which new SAP NetWeaver BI enhancements can support real-time data warehousing

We will look at common EDW pitfalls and how to leverage the SAP NetWeaver BI architecture in a large landscape using the Corporate Information Factory (CIF)

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What Well Cover
Difference between evolutionary DW architecture and a design Data marts vs. Data warehouses Real-time Data warehousingThe many mistakes of EDWsSuccesses and failures of six large-scale SAP BI-EDWsSAP NetWeaver BI architecture & Corporate Info. Factory (CIF)Wrap-up

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Level of Pre-delivered Content

Toolsets & accelerators

Analytical applications

for specific industries

Level of Embedded Analytics

Complex (score cards, budgeting, planning, KPI)

Interactive Mgmt. reporting (OLAP, MQE)

Evolution of Data Warehousing

Emerging (1st generation)

Vertical approach (2nd generation)

Horizontal approach (2nd generation)

Integrated analytical (3rd generation)

Source: Mike Schroeck, David Zinn and Bjarne Berg, Integrated Analytics Getting Increased Value from Enterprise Resource Planning Systems, Data Management Review, May, 2002;

Adapted: Bjarne Berg How to Manage a BW Project, BW & Portals Conference, 2007, Miami

Emerging

(1st generation)

A General Conceptual Enterprise DW Architecture

Metadata

Data Resource Management and Quality Assurance

Source Data

Extract

Operational
Data Store

Transform

Data
Warehouse

BI Applications

Source: Bjarne Berg, Introduction to Data Warehousing,

Price Waterhouse Global System solution Center, 1997

Data

Extraction
Integration

and

Cleansing

Processes

Custom

Developed

Applications

Data

Mining

Statistical

Programs

Query Access

Tools

Summarized
Data

Segmented
Data Subsets

Functional Area

Summation

Marketing
and Sales

Purchasing

Corporate
Information

Product Line

Location

Purchasing
Systems

Invoicing
Systems

General
Ledger

External Data
Sources

Other Internal
Systems

Translate

Attribute

Calculate

Derive

Summarize

Synchronize

SAPs Technical EDW Architecture

Source: SAP AG

UDI

XMLA

SAP

Query

JDBC

ODBO

Business Explorer Suite (BEx)

BEx Query Designer

Information Broadcasting

Web

Analyzer

Web

Application

Designer

MS Excel
Add-in

Report

Designer

BEx Web

DB

Connect

BAPI

Service

API

File

XML/A

BEx Analyzer

BI Pattern

BI Consumer Services

BI Platform

Data Warehouse

Enterprise Portal

Analytic Engine

Meta Data Mgr

KM

Visual Composer BI Kit

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SAPs EDW Enablers - Query optimization

The SAP BI accelerator makes query response time 50-10,000 faster.

You use process chains to maintain the HPA engine after each data load

Both HP and IBM have standard solutions ranging from $32K to $200K+ that can be installed and tested in as little as 2-4 weeks (+ SAP licensing costs)

SAP NW 2004s BI

HPA Engine/Adaptive Computing

Data
Acquisition

InfoCubes

Analytic
Engine

SAP

BW

Any

tool
In-memory processingDictionary-based, smart compression using integersHigh parallel data access / horizontal partitioningColumn-based data storage & access/vertical table decomposition

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SAPs EDW Enablers - Remodeling Tool Box

In older BW versions, if you forgot to include a field in your infocube, the rework was quite substantial and often involved reloading the infocube as well.

NW 2004s goes a long way to address the complaints that BW is a hard to maintain environment with forever fixed models.

Source: SAP AG, Richard Brown, Aug. 2006

In NW2004s you get a new tool to add characteristics and key figures to your model.

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SAPs EDW Enablers - Central EDW Adm. & Tool reductions

SAP NetWeaver has solutions for a complete EDW architecture, including an Administrator Cockpit for managing the system

In a custom data warehouse environment you need many tools:

- Data loads and transformations

- Scheduling of jobs

- Database management

- Data modeling

- Managed query environments

- On-line Analytical Processing tools (OLAP)

- Statistical analysis tools

- Data visualization tools

- Formatted reporting tools

- Web presentation tool

- Security administration tool

- EDW administration tool(s)

- Others ?

In a SAP data warehouse environment you need one tool:

SAP NetWeaver

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SAPs EDW Enablers - Global Tool Reach

The SAP Message: BO and SAP provides

Alignment, Extension & Augmentation of two leading, complementary BI & EIM solutions

After the SAPs Acquisition of Business Objects, many have questioned the long-term vision of SAP as the EDW. In Response, SAP published their tool integration vision in February 2008:

Source: SAP February 2008

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SAPs EDW Enablers - Long-term communicated vision

Notice that SAP Web Application Designer is Replaced by Xcelsius+ in 2009 and a new tool called Pioneer will be launched that year also.

SAP has a long-term commitment to EDW and has published their 3-year tool plan so that customers can plan ahead.

Source: SAP February 2008

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What Well Cover

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Design Vs. Evolution

An organization has two fundamental choices:
Build a new well architected EDW Evolve the old EDW or reporting system
Both solutions are feasible, but organizations that selects an evolutionary approach should be self-aware and monitor undesirable add-ons and workarounds.

Failure to break with the past can be detrimental to an EDWs long-term success

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ODS Vs. Data Warehouse Vs. Data Marts

To Understand the differences between DSO, Data Warehouses and Data Marts we can examine them in terms of usage, modeling and purpose:
Specific application or workgroup focusNarrow scopeCustomized or stand alone analysisInteractive queryHighly summarizedSingle subject and department orientedUses dimensional data modeling

Data Store Objects (DSO)

Data Warehouse

Data Mart
Acts as source to populate DW and martsOften used for operational reportingDetailed, atomic dataHuge data volumesIntegrated, clean dataCross-functional and cross-departmentalSupports data miningMay use denormalized form modeling (NOT dimensional)Provides mgmt reportingSummarized dataTuned to optimize query performanceMultiple departments or processesMay act as staging area for data martsUses dimensional data modeling

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Data Warehouse Vs. Data Marts - Implementation Sequence

There are several alternatives for an iterative approach to implementing the various storage structures, based upon organizational needs.

The various structures can be enterprise or departmentally focused.

They can be built first, middle, or last. They can be stand-alone or combined. The important point is to have a concept of the long term vision of the data warehouse project and how each type of structure is to be used.

A)ODS first: Start by building an enterprise data warehouse from a subject area perspective and then gradually move subsets of data to data marts. This approach may take a longer time to implement.

B)Data mart first: Start by building data marts to get data out to users quickly. This approach may encounter difficulties in integrating data from an enterprise perspective.

C) Data marts first within the framework or vision of an ODS: Start by developing a high-level enterprise or subject area data warehouse framework to guide the incremental development of the data marts or data warehouse.

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Advantages of building the data marts first

There is a significant trend in the industry today toward building data marts first, then consolidating backwards to create the data warehouse and operational data store. There are several advantages to this approach:

A) Allows faster implementation

The average data mart may take 2-3 months to implement; the average EDW evolves over many iteration and may take years to mature. Several marts can be started in parallel.

B) Reduces political liability through alignment with a specific business need.

The mart can deliver value to the organization in a much shorter period of time and can focus on a specific business function or problem. The business sponsors will see faster results and can affirm their decisions with benefit analysis and feedback. This is important to maintaining interest and adequate funding levels for the program. This is in contrast to the time and complexity of building an enterprise data warehouse.

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Advantages of building the data marts first (continued)

C) Limits risk while learning how to implement data warehouse.

Building very large databases of several Terabytes is inherently complex. Backup and recovery systems may require specialized hardware and software. Complex tuning may be necessary to achieve satisfactory query performance levels.

Identifying and defining data from many different sources creates opportunities for users and sponsoring departments to disagree. The ultimate business goals may be overshadowed by the technical and political difficulties of building the large warehouse. Starting small with a data mart, experimenting, and using the implementation as a learning experience, will reduce the risk and may actually result in a higher quality deliverable.

D) Costs less than an EDW.

Initially, the economics of smaller scale hardware, software, and development staff may contribute to lower costs for marts than EDWs.

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Major Risks of building the Data Marts first

Data marts do not replace data warehouses.

The data mart is not the next step in data warehouse evolution. It must be planned and implemented as part of the overall architectural vision.

To be effective, you must maintain centralized control of data distribution to the mart in order to support the enterprises overarching warehouse goals of data quality, consolidation, and sharing.

Data marts also increase the complexity of the data warehouse environment with multiple extract, transform, and transfer routines.

There are some great risks of succumbing to political pressures.

Business units that demand a quick hit and a stovepipe implementation of data marts may only serve to undermine the best laid plans for an integrated and durable data warehousing program.

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Risks of building the data marts first

If the IT department agrees to a bottom-up EDW, a strictly application specific approach, they may end up with multiple data marts that can not be integrated into a larger EDW/ODS view and which can not support analysis across different marts.

The bottom line is plan and build a reusable data and technical foundation (technology standards, data modeling principles, and integrated databases).

The Gartner Group estimates that resources required to manage a disjointed data mart environment are three times greater than an integrated data warehouse architecture.

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SAPs Vision of Data Marts

If you insist on building data marts, you can

also use SAPs newly acquired Rapid Marts tool from Business Objects.

Built with Data Integrator, SAP Rapid Marts are ready-made data marts for SAP. It has pre-built data flows, business logic, and schema that understand the SAP meta-data.

SAP Rapids Marts also include content that is immediately consumable by business users and can be deployed independent from an EDW implementation.

It supports data profiling and cleansing and can be the first step toward a holistic EIM program or global EDW strategy. In a prototype environment it can also provide early understanding of data quality problems.

Source: SAP, Feb 2008

SAP has now inherited a tool for Data Marts that is independent from the SAP NetWeaver Platform

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What Well Cover

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Real-time SAP Enterprise Data warehousing gets better

NW 2004s has more features for updates that does not follow the typical asynchronomous (batch) updates. This include:

1. We can use XML to fill the PSA directly

2. Daemon-based update from delta queue (BW API)

3. Daemon-based update of the ODS and minimal logging

Note: XML documents creates many tags that will slow down large dataloads due to the size of each XML record (relatively large)

However, it works great for smaller streams of data.

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Limitations of Real-time SAP Enterprise Data warehousing

There are some limitations depending on the version of SAP BI/BW you use. For versions 3.5 and higher, there are few limitations and they include:
You can only use real-time to load ODSs or PSAA normal delta update and a real-time update cannot happen at the same time for the same DataSource and/or ODSFor data targets that subsequently store the real-time-supported ODS objects, real time data transfer cannot be usedInfoPackages that use real-time updates cannot be associated with InfoPackage Groups or Process Chains
Consider Using SAP Exchange Infrastructure (SAP-XI) to generate the XML documents from non-SAP Systems. This can help build a corporate data hub center that can reduce the number of custom interfaces in the organization
3.unknown

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What Well Cover

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Common EDW Mistakes Not Using Standard SAP Solutions

In the 1950s, you could buy a standard Sears house for $2,065 and pay $935 more to have it implemented on your own land

The customers who selected to buy the standard house were either extremely happy or totally disappointed.

When Sears examined why, they found a strong correlation between level of modifications to the home and unhappiness

You buy SAP NetWeaver for its pre-built content and connections to other SAP applications.

The more you add to the standard solutions, the harder it will become to realize the benefits you sought in the first place.

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Leveraging SAP Standard Content in The EDW
As a guiding principle, map requirements to standard content before customizingHowever, youll probably also have external data sources that require custom ODSs and InfoCubesCustomizing lower level objects will cause higher level standard objects to not work, unless you are willing to customize these also.
BW Content available (BI 7.0)
Cockpits ???Workbooks2,211Queries4,325Roles934MultiProviders402InfoCube783DSO objects687InfoObjects 14,368
An example from a large manufacturing company

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Standard content

How to Leverage Standard BI Content in the EDW

Storage Requirements

Storage Objects

Map functional requirements to the standard content before you make enhancements

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1. Create a model based on pre-delivered SAP BW content

2. Map your data requirements to the delivered content, and identify gaps

3. Identify where the data gaps are going to be sourced from

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Common EDW Mistakes No Tailored Approach

Each organization has different corporate cultures and considerations.

The Top-down approach is preferred in centralized organizations, and the bottom-up is preferred in decentralized organizations. Pick one approach and stick with it.

TOP-DOWN APPROACH

Build a global data warehouse for the company, and proceed sourcing data from old legacy systems driven from a top-down approach.

BOTTOM-UP APPROACH

Focus on a bottom-up approach where the BW project will prioritize supporting and delivering local BW solutions, thereby setting the actual establishment of the global Data Warehouse as secondary, BUT not forgotten.

CONTINUE

CHANGE

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Common EDW Mistakes loose data standards

Some Many organizations place little value on enforcing data standards.

This include InfoObject, DSO and InfoCube naming standards. It also include naming conventions for queries and InfoAreas.

As a result, these organizations often have a mess where it is hard to understand what is available without researching every field and data store.

It may also lead to problems integrating data with different data types and data lengths due to lack of enforcement

Develop your data standard and have an architect enforce them throughout the lifetime of the EDW.

Vchar2(15)

Char(18)

Jims Query

AA Z0986 Query

X0C_K01

0SD_C03

0FIAR_O05

SUDHIRC99

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Common EDW Mistakes Lack of environment management

Some organization have a hard-time to say No to the business community.

As a result, their architecture often looks like mix-and-match of systems that was acquired to put out urgent needs.

In these organizations, multiple portals are common and overlapping reporting systems is the rule, not the exception.

EDWs are like marriages between IT and Business. You have to work at it constantly, give it attention, and be faithful to the solution.

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Common EDW Mistakes lack of transport controls

Most companies have strong change management of their R/3 systems. However, it is common that the same organizations have very loose approval processes for their BI systems.

BI is becoming a mission critical system for most organizations and the same processes placed on the R/3 system should be applied to a production BI system.

Dont allow quick-fixes and untested service packs and notes to be applied to the production box without adequate testing. BWQ is not for window dressing!!

If you want a stable BI system, you have to enforce testing and controls

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Common EDW Mistakes Poor Performance

When you build an enterprise data warehouse, you should plan for at least 10-15% of your project time for performance testing and tuning.

Click-stream analysis have shown the 50% of your casual audience will hit the refresh button or navigate away from your web site if the reports take more than 7 seconds.

If your query takes more then 20 seconds to run, you have major problems.

Get substantial amount of memory for caching and make sure your have a fast network and hardware resources.

#1 complaint of EDW is lack of performance. Consider BIA as part of your infrastructure

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What Well Cover

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SAP EDW in 6 large Companies - Overview

In this EDW case study we are going to look at 6 diverse organizations and see their lessons learned in their own words

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SAP as the EDW in an Insurance Company

Go-live Year: 2003 (BW v. 3.0b)

Mistakes Made: Under estimated the time it would take to get the staff up to speed and trained in BW. Had no SAP web skills in-house and went with the wrong portal choice (non-SAP)

Successes: Built foundation data stores first (AP, AR, GL, etc. before we started the individual department needs. This created a real EDW foundation instead of data marts. Now we are building more multiproviders and fewer new data stores. Because we built the EDW first, we can now deliver solutions faster.

Technology Challenges: Needed 3 app servers and more memory than first anticipated.

Next Steps: Performance tuning (BIA) and cockpits

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SAP as the EDW in Oil & Gas Company

Go-live Year: 2001 (BW v. 2.1c)

Mistakes Made: Stated with wrong area (MM). Should have done FI first and then HR. MM, APO and Motor Vehicle Fuel Tax reporting was too complex and ambitious for the first implementation when we were learning.

Successes: Met budgets, deliverables and timelines. User satisfaction was very high when we went from only BEx workbooks to the web templates. Upgrade to BI 7.0 was well received by developers and users.

Technology Challenges: Did not know how to performance tune the workbooks when we upgraded. They went from kilobytes to Megabytes. Needed on-line user training (CBT)

Next Steps: Adding the subsidiaries and corporate entities in Asia and Europe (650 more users)

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SAP as the EDW in another Oil & Gas Company


Mistakes Made: No formal commitment to the EDW, that evolved over time (3 years). Did not have the top C-level commitment until 2003 and had to do a lot of rework to accommodate the new global vision.

Successes: We are 8 years into the EDW and it has been adapted as the core platform for global HR, finance and sales reporting. We have most divisions on the system and have retired six legacy reporting environments.

Technology Challenges: Needed more HW than originally planned. Performance was a real problem until 2006 when we started using the Broadcaster and cached some reports in memory.

Next Steps: Adding European training and rollout (2 more R/3 systems)

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SAP as the EDW in a Manufacturing Company


Mistakes Made: Started too early with too ambitious scope. BW was not ready for EDW in 1999. Not until version 3.0b (2002) did we get a real ODS and could realize our earlier ideas of the EDW.

Successes: We kept the scope small and manageable, and had good consultants. The turnover rate on the project team has been low and the system was allowed to mature without business disruptions. We have consolidated three reporting groups into one and saved hundred of thousands of dollars in licenses each year.

Technology Challenges: Data integration was the hardest. We had to spend most of our project time on masterdata mapping & consolidation.

Next Steps: Add more functionality (purchasing) and rollout to purchasing group and the sales reps.

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SAP as the EDW in a High-Tech Company

Go-live Year: 2003 (BW v. 3.1c)

Mistakes Made: User interface was not prioritized high enough. Executives and casual users hated BEx workbooks. We had to relauch the EDW in 2006 with a new web interface.

Successes: After the relaunch we have had success with user adaptation and have a functional steering committee and CFO sponsorship. Closing the financial books have gone from 5 days to 3.

Technology Challenges: Was unsure on how to interface our existing portal with SAP BI content (SSO). Security setup was hard and advise was too divergent. Process chains ran very slow until we tuned the ABAP.

Next Steps: Add 2 more acquired companies to SAP R/3 and BI.

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SAP as the EDW in a Government Organization

Go-live Year: 2005 (BW v. 3.5)

Mistakes Made: Source data was in too many diverse old system with no real standards. We under estimated the time it would take in integrate nine different mainframes, some that was 20+ years old. Should not used a big-bang go-live.

Successes: Civilian and uniformed personnel worked well together and training was well received. The data collection and reporting that used to take 14 days each month to produce, now takes 30 minutes.

Technology Challenges: During the BI 7.0 upgrade, the unicode conversion took long (did not complete over the weekend). The BSP web templates had to be rebuilt completely.

Next Steps: Add another maintenance organization and work on web cockpits.

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What Well Cover

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The Corporate Information Factory (CIF)

In 2001, Bill Inmon (the father of DW) and Claudia Imhoff proposed a reporting architecture known as the CIF.

At the heart CIFs reporting strategy is the EDW. It is the source of:
Decision Support System applications (APO, CRM, OLAP, Reporting etc).Data Mining and APDDepartmental Data MartsAccess Media Accelerators (BIA)
Bill Inmon is a SAP BI technology advisor. He has advised SAP on how to develop NetWeaver BI

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Using the CIF Reducing number of Platforms

NetWeaver helps by:

Reducing number of hardware servers

Consolidates the platform needs for budgeting, planning, forecasting and scheduling

Simplifies the platforms for web access, security, reporting and analysis.

A major CIF decision is how to integrate the solutions in as few platforms as possible.

CIF provides a corporate framework for the EDW; NetWeaver provides the capabilities to do so with one platform
6.unknown

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SAPs Conceptual Enterprise Data Warehouse Architecture

SAP recognizes that we do not build EDWs, we are doing Enterprise Data warehousing. This is an on-going activity that merges information systems, people and processes.

EDW is an on-going activity with continuous investment needs.

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What Well Cover

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COMERIT (Presentations, articles and accellerators)

www.comerit.net

Enterprise Wide Data Warehousing with SAP BW

https://www.sdn.sap.com/irj/sdn/go/portal/prtroot/docs/library/uuid/5586d290-0201-0010-b19e-a8b8b91207b8

Enterprise DataWarehousing SAP Help http://help.sap.com/saphelp_nw70/helpdata/en/29/d9144236bcda2ce10000000a1550b0/frameset.htm

Resources
7.unknown

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7 Key Points to Take Home
Plan Your Target EDW Architecture before you start the project.Enforce Standards and pick the right tools for the jobSAP BI is no longer leading or bleeding edge and is used extensively as the EDW for large organizationsIf you are still on BI 3.5: Upgrade!SAP BI has many new tools that will enhance the front-end for end users. Your EDW will need themCritical to EDW success: reduce number of competing reporting system very quicklyHire an EDW Technical Architect if you have not already.

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Your Turn!

How to contact me:

Dr. Bjarne Berg

[email protected]

Tip

Solution

Resource

36%

33%

31%

Mostly standard storage objects

Some customization

Highly customized storage objects

Billing

Number of billing documents

Number biling line items

Billed item quantity

Net weight

Subtotal 1

Subtotal 2

Subtotal 3

Subtotal 4

Subtotal 5

Subtotal 6

Subtotal A

Net value

Cost

Tax amount

Volume

Customer

Sold-to

Ship-to

Bill-to

Payer

Customer class

Customer group

~ Customer country

~ Customer region

~ Customer postal code

~ Customer industry code 1

End user

Material

Material number

Material entered

Material group

Item category

Product hierarchy

EAN/UPC

Time

Calendar year

Calendar month

Calendar week

Calendar day

Unit

Currency Key

Unit of Measure

Base unit of measure

Sales unit of measure

Volume unit of measure

Weight unit of measure

Billing information

Billing document

Billing item

Billing type

Billing category

Billing date

Creation date

Cancel indicator

Output medium

~ Batch billing indicator

Debit/credit reason code

Biling category

Reference document

Payment terms

Cancelled billing document

Divison for the order header

Pricing procedure

Organization

Company code

Division

Distribution channel

Sales organization

Sales group

Logistics

Plant

Shipping/receiving point

Document details

Sales order document type

Sales deal

Sales docuement

Accounting

Cost center

Profit center

Controlling area

Account assignment group

Personnel

Sales rep number

LEGEND

Delivered in standard extractors

Delivered in LO extractor

Not in delivered Content -but in R-3

OrganizationCompany 1Company 2Company 3Company 4Company 5Company 6

IndustryInsuranceOil & GasOil & GasManufact.High-TechGov.

SystemBW 3.5BI 7.0BI 7.0BI 7.0BW 3.5BI 7.0

Number of Executive Users* 25342211426

Number of Casual users* 9523,1182,4801,3981,122409

Number of Power users* 34144623897

Number of non-SAP sources 641131324

Number of SAP sources 3110786241449

EDW data content (0-100%)** 80%70%75%50%50%30%

Lessons learned "Start with content

in finance and do

few enhancements

in the beginning"

"Have strong

executive support

and think very long-

term; 3-10 years"

"Spend serious

time on end user

training and

support. Sell the

EDW internally"

"Shut-down

competing

reporting

systems; don't

allow access

databases"

"Users look at the

query tools &

don't care about

the EDW. Use

web tools"

"Data integration is

70% of the project.

Look at source

systems early"

Overall satisfaction*** 788879

Future Plans

BI Accelerator and

web cockpits

Global rollout

(Asia & Europe)

Global rollout

(Europe)

Add new

divisions in US &

purchasing

Rollout and add

subsidiarie's

content

Rollout to the

whole organization

* = actual users logged in within a 30 days period

** = estimated amount of organizational reporting done with EDW data

*** = Scale 1 to 9 (9 being highest and 5 being neutral)

OrganizationCompany 1

IndustryInsurance

SystemBW 3.5


Number of Casual users* 952




EDW data content (0-100%)** 80%

Lessons learned"Start with content

in finance and do

few enhancements

in the beginning"


Future Plans

BI Accelerator and

web cockpits


** = estimated % of org. reporting done with EDW data



IndustryOil & Gas

SystemBI 7.0


Number of Casual users* 3,118





Lessons learned"Have strong

executive support

and think very long-

term; 3-10 years"


Future Plans

Global rollout

(Asia & Europe)





IndustryOil & Gas

SystemBI 7.0







Lessons learned"Spend serious

time on end user

training and

support. Sell the

EDW internally"


Future Plans

Global rollout

(Europe)





IndustryManufact.

SystemBI 7.0







Lessons learned"Shut-down

competing

reporting

systems; don't

allow access

databases"


Future Plans

Add new

divisions in US &

purchasing





IndustryHigh-Tech

SystemBW 3.5







Lessons learned"Users look at the

query tools &

don't care about

the EDW. Use

web tools"


Future Plans

Rollout and add

subsidiarie's

content





IndustryGov.

SystemBI 7.0


Number of Casual users* 409





Lessons learned"Data integration is

70% of the project.

Look at source

systems early"


Future Plans

Rollout to the

whole organization




FI

Inv

Factory

Dist

Web

Order

.

SOA / WS

FI

Inv

Factory

Dist

Web

Order

.

SOA / WS

2008

Other Enterprise Applications

Distributed

Apps

mySAP

ERP*

1

FI/CO,

HR

mySAP PLM*

1

mySAP

SRM*

1

mySAP SCM*

1

mySAP

CRM*

1

SAP NetWeaver

Portal

Sec.

EDW

Enterprise

Platform

Simplified

Integration

TCO =

Enterprise Platform Cost

Cost of Applications

Cost of Integrating

Apps & Platforms

+

+

End-to-End

Service

Predictability

End-to-End

Service

Predictability

Web Presentation/Portal/Mgmt Reporting

Modeling and

Optimization

Consolidation

Balanced

Scorecard

Budget

Plan/Forecast

Statutory

Reporting

Ad Hoc Query

and Reporting

DataMartDataMartDataMartDataMart

Data Warehouse

Integration Broker

ERP/CRM/SCM/External Sources

Business Proc. Management

Content Management

Knowledge Management

Process

Integration

SAP NetWeaver

People

Integration

Information

Integration

Source: SAP

NW2008_Tips_tricks_EDW_v10.ppt

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Transcript of NW2008_Tips_tricks_EDW_v10.ppt