EIM201

Lothar Henkes / Brian Wood, SAP BW Product Management

Month September, 2011

EIM201

SAP NetWeaver BW 7.3 Overview and Roadmap

© 2011 SAP AG. All rights reserved. 2

Disclaimer

This presentation outlines our general product direction and should not be relied on in making a

purchase decision. This presentation is not subject to your license agreement or any other agreement

with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to

develop or release any functionality mentioned in this presentation. This presentation and SAP's

strategy and possible future developments are subject to change and may be changed by SAP at any

time for any reason without notice. This document is provided without a warranty of any kind, either

express or implied, including but not limited to, the implied warranties of merchantability, fitness for a

particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this

document, except if such damages were caused by SAP intentionally or grossly negligent.

Agenda

Highlights SAP NetWeaver BW 7.3

Enterprise Data Warehousing

SAP NetWeaver Business Warehouse Accelerator

SAP NetWeaver BW‘s use and roadmap of In Memory technology

Deploying HANA Data Mart with SAP NetWeaver BW

Next version SAP NetWeaver BW powered by SAP HANA

Summary


SAP NetWeaver BW AdoptionProductive SAP NetWeaver BW Systems – Constant Growth

Stable Product, Large installed Base,

Constant Growth

Adoption of SAP NetWeaver BW constantly

growing

Unaffected by economic down-turn in 2009

More than 12,000 customers referring to

more than 15.000 productive systems

13,910

14,217

14,450

14,693

14,952

15,243

15,533

15,671

12,000

12,500

13,000

13,500

14,000

14,500

15,000

15,500

16,000

Q3 0

9

Q4 0

9

Q1 1

0

Q2 1

0

Q3 1

0

Q4 1

0

Q1 1

1

Q2 1

1

Agenda






Next version SAP NetWeaver BW powered by SAP HANA

Summary


Executive Summary Major Benefits of SAP NetWeaver BW 7.3 are:

Enhanced Scalability & Performance for faster decision making

Remarkably accelerated data loads

Next level of performance for BW Accelerator

Increased flexibility through SAP BusinessObjects BI and EIM integration

Tighter integration with SAP BusinessObjects Data Services

Enhanced integration with SAP BusinessObjects Metadata Management

Reduced TCO and improved development efficiency

Automated creation of Semantic Partitioned Objects

Graphical data flow modeling and best practice data modeling patterns

Simplified configuration and operational management

Admin Cockpit integrated into SAP Solution Manager

Wizard based system configuration


BW 7.3 Ramp-up Program November 2010 – May 2011

Very successful Ramp-Up

program

Overall 36 customer projects

Very good feedback from

customers on:

Installation & Configuration

Quality & Reliability

Functional completeness

Currently 24 live customers

Reference customer – Bluefin, UK http://www.sdn.sap.com/irj/scn/go/portal/prtroot/docs/libr

ary/uuid/105520f5-4a93-2e10-47ac-

a0a94c354490?QuickLink=index&overridelayout=true

Quality / Stability

• The quality and stability of the release is of a good standard.

BW 7.3 seems to be stable and has very useful functionality

• Minor issues (fixed by Support). Satisfied with overall quality and stability

• . . .

Functional Completeness

• Provides several capabilities of major advantage to lowering TCO

and simplifying EDW architecture

• Great Release

• . . .

Improved data load performance

• 20 mio records loaded. Remarkable improved data load performance

seen

• . . .

―

http://www.sdn.sap.com/irj/scn/go/portal/prtroot/docs/library/uuid/105520f5-4a93-2e10-47ac-a0a94c354490?QuickLink=index&overridelayout=true

























Enterprise Data WarehousingPerformance & Scalability

Enhanced modeling capabilities to increase scalability,

flexibility and reduce development and maintenance

overhead

Semantical Partitioning

New type of modeling object with 7.3

Single point of entry for creation and admin

Wizard based creation of data models + data flows

Easy re-modeling of the partitions on an ongoing basis

Embeddable into data flows or data models

In-Memory SAP NetWeaver BWA support

Full integration with archiving and Near Line Storage functionality

Flexible definition of partitions via customer coding (Business Add-Ins)Source1 Source2 Source3

Sem

an

tical

Part

itio

ned

Ob

ject

(SP

O)


SAP NetWeaver Business Warehouse 7.3 Development Efficiency

Graphical Data Flow Modeling introduces new paradigm of BW modeling

Graphical Top-Down Modeling

Enables fast structured modeling directly in the system via drag & drop

Supports later refinement of the same models with technical details

Structure BW implementation with the help of data flows

Group the modeling objects and save them as view of your enterprise model

Document your dataflows via HTML or attach any other document type

Fully integrated in DW Workbench navigation pane for easy access

Ability to transport according to data flows

Share and reuse models using naming conventions for the dataflow

Modeling powered by dataflow templates

Easier and faster modeling using predefined templates (copy and adapt)

Customer can create company standards defining custom templates

Pre-build templates of the Layered Scalable Architecturehttps://www.sdn.sap.com/irj/scn/index?rid=/webcontent/uuid/3032b447-b56d-2e10-8eae-c82e29bd1525

https://www.sdn.sap.com/irj/scn/index?rid=/webcontent/uuid/3032b447-b56d-2e10-8eae-c82e29bd1525























Enterprise Data Warehousing Performance & Scalability -Accelerated Data Loads for DataStore Objects

DataStore objects

Support of database partitioning by time characteristics enables faster access of the data

Activation is changed from single lookups to mass lookup of active table

Avg. 20 – 40% improvement, Max. improvement – 2.5x faster, (lab results)

Varies by data profile (# inserts/updates/deletes) and database platform

Runtime option ―New, unique data records only‖

Omits lookups during activation

Speeds up initial loads, e.g. requests which contain only new data

New rule-type for Transformations: ‗Read from DataStore‘

Fast data lookup to DataStore objects in database and Near-Line Storage.

DBMS specific optimization (DB2 DPF and Teradata)

Activation using database specific SQL commands on mass data

Improvement measured (lab results): factor 2 – 3 times faster compared to standard activation


Enterprise Data Warehousing Real-Time Business Intelligence

Automated combination of write- and read-optimized data containers

HybridProvider

Combines mass data with latest delta

information at query runtime

Consists of a DataStore object, an InfoCube and

an automatically generated data flow between

the objects

DSO object can be connected to a real-time data

acquisition DataSource/DTP

If the DataSource can provide appropriate delta

information in direct access mode a VirtualProvider can be used instead of the DSO.

Facilitates replication of DSO-/VirtualProvider data to SAP NetWeaver BW Accelerator by

switching off database persistency of the InfoCube


Enterprise Data Warehousing SAP BusinessObjects Data Services

SAP BusinessObjects Data Services is SAP’s strategic

solution for adding flexibility in extracting non-SAP data

Available today:

Data Services to schedule loading processes of SAP NetWeaver

BW

Execute and control process chains from Data Services to extract

data via Open Hub Service

SAP NetWeaver BW 7.3 / Data Services XI 4.0:

New Source System type in BW: ‗Data Services‘

Access to Data Services ‗Data Stores‘ and generation of Data

Services data flows (i.e. simple dataflow from DataStore to BW

DataSource)

Tight integration of Data Services into extended SAP NetWeaver

BW data flow concept


Enterprise Data Warehousing SAP BusinessObjects Metadata Management

End-to-End Data lineage and change impact analysis in

heterogeneous environments

SAP BusinessObjects Metadata Management XI 3.1

Change impact analysis between SAP NetWeaver BW objects

such as DataStore objects, InfoSets, BEx Queries, and InfoCubes

End-to-end capabilities for all data sources through SAP

BusinessObjects Data Services

End-user access to data lineage information for trusted SAP

NetWeaver BW deployment

SAP BusinessObjects Information Steward 4.1

Include detailed field mapping information from source to target

with SAP NetWeaver BW 7.3, SP5

Relation

al

Sources

ETL

DW

SAP

NetWeaver

BW

Analysis

and

Reports

Data Lineage

Impact Analysis


SAP NetWeaver BW Integrated Planning

Continued investments for reduced TCO of SAP NetWeaver BW Integrated Planning

ABAP Planning Modeler

ABAP based Planning Modeler for a non-disruptive

customizing of planning objects and their

related BW objects

The ABAP-based Planning Modeler does not

require functionalities from the Java Stack

for BEx Analyzer based planning applications


SAP NetWeaver BW Accelerator 7.20 Taking the BW Accelerator to the next level of performance

Enhanced built-in analytical capabilities *

F4-Value help

MultiProvider calculation handling

Exception aggregation (min, max, count distinct)

BWA based InfoCube

Use DataStore Objects to create indexes

―BW Workspace‖ Analytic indexes

* Features require update/future release of SAP

NetWeaver BW to be leveraged

SAP NetWeaver BW Accelerator

Calculation Engine

Aggregation Engine

Index

SAP NetWeaver BW


SAP NetWeaver BW Accelerator 7.20 Analytic Index & Composite Provider

Flexible modeling with APD & BWA in SAP NetWeaver BW

Any output of an APD process can be materialized as a analytic

index

Analytic Indexes are exposed as an InfoProvider for Queries

definitions on top of it

Composite Provider: Simple modeling of compositions of analytic

indexes (unions, joins)

Join/Union operation is processed on the fly

CompositeProviders are exposed as standard SAP NetWeaver BW

InfoProvider for BI client

consumption

Agenda






Next version SAP NetWeaver BW powered by HANA

Summary


What is In Memory Computing?

In Memory Computing moves data and information sources from remote databases into local

memory so that results of analyses and transaction are available immediately

Answer Any Question Immediately

100x Faster Analytics

Access Current and Complete Information

Real-Time Access to Transactional Data

Discover Deeper Insights

Eliminate aggregation to interrogate granular data

Manage Large Data Volumes Cost Effectively

Groundbreaking In Memory HW Innovations

Speed

Scale

Flexible


In-Memory Computing – The Time is NOWOrchestrating Technology Innovations

HW Technology Innovations

64bit address space – 2TB in current

servers

100GB/s data throughput

Dramatic decline in price/performance

Multi-Core Architecture (8 x 8core CPU per

blade)

Massive parallel scaling with many blades

One blade ~$50.000 = 1 Enterprise Class

Server

Row and Column Store

Compression

Partitioning

No Aggregate Tables

Insert Only on Delta

The elements of in-memory computing are not new. However, dramatically improved hardware

economics and technology innovations in software have now made it possible for SAP to deliver on

its vision of the Real-Time Enterprise with in-memory business applications

SAP SW Technology Innovations


SAP HANA SAP In Memory Appliance

MDX SQL BICSSQL

ModelingStudio

Real–Time Replication Services

Data Services

SAP HANA

Other Applications SAP BusinessObjects

SAP NetWeaver BWSAP Business Suite 3rd Party

In-Memory Computing Engine

Calculation and Planning Engine

Row & Column Storage

Preconfigured Analytical Appliance

In-Memory software + hardware(HP, IBM, Fujitsu, Cisco)

In-Memory Computing Engine Software

Data Modeling and Data Management

Real-time Data Replication via Sybase Replication Server

Data Services for ETL capabilities from SAP Business Suite, SAP BW and 3rd Party Systems

Capabilities Enabled

Analyze information in real-time at unprecedented speeds on large volumes of non-aggregated data

Create flexible analytic models based on real-time and historic business data

Foundation for new category of applications (e.g., planning, simulation) to significantly outperform currentapplications in category

Minimizes data duplication


Different Needs … Different Types of Data Marts

Architected Data Marts:

Consolidated and integral part of EDW & LSA supporting

decision making on corporate data

Centrally managed by IT, standardized data models on

corporate information, aggregated

Long term requirements in terms of stability and consistency

Operational Data Marts:

Real Time Data and volatile

Reporting on large volumes of granular, transactional data

Supporting local business execution

Agile Data Marts:

Independently of the centralized corporate EDW layers

Maximum flexibility for LOBs in data modeling

and integration of LOB specific data

Support strategic decision making in LOBs

Volatile and historical data with fluid data models

Corporate

data sources

Ad-hoc

data sources

Operational

Data Marts

Agile

Data MartsArchitected

Data Marts

SA

P N

etW

ea

ve

r B

W

Inbound data

Data Marts

BW Extractors

and ETL

Data Store Objects

Corporate Memory

InfoCubes

ETL

Operational

Real-time

replication

and ETL


HANA Agile Data Mart Scenario…Running Side by Side SAP NetWeaver BW

Providing a powerful Data Mart scenario

Centrally managed SAP NetWeaver BW solution for core EDW use cases

Provide LOBs or subsidiaries with an agile analytical environment

Load selected analytic models from SAP NetWeaver BW into the agile data mart environment

Flexible remodeling and extending of loaded analytic models

Outstanding query performance

Direct access for SAP‗s BI clients and MS Excel

SAP NW BW

RDBMS

HANA 1.0BWA

Agenda


• Enterprise Data Warehousing

• SAP NetWeaver Business Warehouse Accelerator


• Deploying HANA Data Mart with SAP NetWeaver BW

• Next version SAP NetWeaver BW running on HANA

Summary

This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This

presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP

assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.


Evolving In-Memory Footprint in SAP NetWeaver BW

Planning Engine

Data Manager

InfoCubes

DataStore Objects

Analytic Engine

Data Persistency and

Runtime

Data

Modeling

En

terp

rise

Da

ta W

are

ho

use

an

d D

ata

Ma

rt M

od

elin

g w

ith

SA

P N

etW

ea

ve

r B

W

BWA instead of

aggregates

Filter +

aggregation

BWA-only

InfoCubes

BWA reporting

for DSOs

In-Memory optimized

DataStore Objects

In-memory

planning engine

First calculation

scenarios in BWA

Additional

calculations

in-memory

MultiProvider

handling and flexible

joins

BW 7.0

DB + BWA 7.0

BW 7.3

DB + BWA 7.2Planned: BW 7.3 on HANA

SP5 and beyond

EDW Processes

In-Memory optimized

InfoCubes

Consumption of HANA

models in BW

HANA data for

BW Staging

This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This

presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP

assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.


In-Memory Computing Product - VisionSAP High Performance Analytic Appliance

In-Memory Computing Platform

SAP

BusinessSuite

Mobile

SAP

BusinessSuite

BI Clients

SAP

BusinessWarehouse

NewSAP

Applications

Further

Applications

This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change

and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.


The Evolution of SAP HANA – Landscape Options

SAP HANA is an appliance to the application (e.g. SAP ERP).

Its major benefit is increasing performance of transactional

reporting for one system.

SAP HANA replicates /loads data using SAP LT Replicator or

DataServices

SAP HANA , SPS3 is the primary persistence for SAP NetWeaver

BW 7.3, SP5.

All features of SAP NetWeaver BW can and should be used with

SAP HANA , SPS3

„SAP HANA vision― is the next evolution step and replaces the DB

of the ERP system

(*) Migrating to further SAP HANA releases is optional

Option „HANA“

SAP ERP

RDBMS

SAP ERP

RDBMS

HANA 1.0

Option „HANA , SPS3“

SAP ERP

RDBMS

SAP ERP

„HANA vision“

Option „HANA vision“

SAP NW BW

SAP NW BW

RDBMS

SAP NW BW

HANA 1.0

BWA




Planned: SAP NetWeaver BW7.3 powered by SAP HANA Added Value

Accelerated performance

Excellent query performance as proven with BWA

Accelerated In-Memory planning capabilities

Performance boost for ETL processes

Simplified administration and infrastructure

DB and BWA merging in one instance for lower TCO

Simplified administration via one set of admin tools e.g. for Data Recovery and High Availability

Column based storage with highly compression rates and significantly less data to be materialized

No special efforts to guarantee fast reporting on any DB object

Simplified data modeling and reduced materialized layers

Integrated and embedded flexibility for Datamarts Speed

Scale

Flexible




Planned: SAP NetWeaver BW7.3 powered by SAP HANA How does BW 7.3 running on HANA differ from BW running on xDB ?

SAP NetWeaver BW 7.x on xDB

Standard DataStore Objects

Data Base server and SAP NetWeaver BWA

Standard InfoCubes

BW Integrated Planning

HANA Data Marts running side-by-side BW

SAP NetWeaver BW 7.3 on HANA

In-Memory based DataStore Objects

SAP HANA In-Memory platform

In-Memory based InfoCubes

In-Memory planning engine

Consumption of HANA artifacts created via HANA studio

BW staging from HANA



Migration without reimplementation - no disruption of existing scenarios


DataStore Objects in SAP NetWeaver BW 7.30 Overview and challenge

DataStore Objects are fundamental building blocks

for a Data Warehouse architecture

They are used to create consistent delta information

from various sources

Reporting can be done on a detailed level

In today's RDBMS-based implementation, the

activation and querying operations are extremely

performance-critical

Active Data TableChange Log

Activation Queue

QueryDelta upload

Parallel Upload

Activation


DataStore Objects in SAP NetWeaver BW 7.30 Creation of Consistent Delta Information

Current architecture

Activation algorithm calculates the changes of

each record and creates heavy load on the DBMS

Delta calculation performed on the application

server, too complex to push it down to the DBMS

as SQL / Stored Procedure

Roundtrips to application server needed for delta

calculation

Activation Queue

Sorted Full Table Scan

Data

Packages

LookupCalculate

DeltaUpdate

Active Data Table Change Log


Planned: In-Memory Optimized DataStore Objects Accelerated data loads

In-Memory optimized DSOs

Delta calculation completely integrated in HANA

Using in-memory optimized data structures for

faster access

No roundtrips to application server needed

Speeding up data staging to DSOs by factor 7-10

Avoids storage of redundant data

After the upgrade to BW on HANA all DSOs

remain unchanged

Tool support for converting standard DSOs into IN-

Memory DSOs planned

No changes of Dataflows required

Database

Layer

Database

Layer

User interface

LayerUser interface

Layer

Application

LayerApplication

Layer

Presentation

DSO Objects

Activation

Data

Presentation

DSO Objects

Activation

Data

SAP NW BW

SAP NW BW SAP NW BW

SAP NW BW

SAP HANA xDB




In-Memory Optimized DataStore ObjectsPerformance Figures

BW 7.30 - RDMBS based

Runtim

e in

seconds


In-Memory Optimized DataStore ObjectsPerformance Figures

BW 7.30 - RDMBS based In-Memory optimized

Using in-memory computing technology

… one of the most time consuming staging

operations – the request activation – was

speed up tremendously by factor 7 - 10

... storage of redundant data was prevented

Runtim

e in

seconds


Planned: Query performanceProven query performance as known from BWA

Query acceleration on BW InfoCubes

No replication – fast query access directly on primary

data persistence

Indexes on InfoCubes and InfoObjects no longer required

-> No Roll-ups, Change runs

In-memory Calculation Engine

– TopN, BottomN,

– Exception aggregation

– Currency conversion

– . . .

Snapshot Indexes for Virtual- and QueryProvider

Query acceleration on BW DataStore Objects(DSO)

Acceleration via In-Memory column storage

Additional acceleration via Analytic Views on top of DSO

No changes of processes, MultiProvider, Queries required

SAP NW BWQuery on

InfoCube, Masterdata

AnalyticIndex,

CompositeProvider

Query on

DSO, BW InfoSet

SAP HANA SQL Engine Calc Engine

Aggregation Engine on In-Memory data




Planned: In-Memory Optimized InfoCubesFaster data loads and easier modeling

Facts

MD MD

MD MD

F

Facts

D

D

MD MD

MD MD

FE

Migration/New

Traditional InfoCubes tailored to a relational DB consist

of 2 Fact Tables and the according Dimension tables

In-memory Optimized InfoCubes tailored to HANA represent

―flat‖ structures without Dimension tables and E tables:

Up to 5 times faster data loads (Lab Results)

Creation of DIM Ids no longer required

Simplified Data modeling

Faster remodeling of structural changes

After the upgrade to BW7.3, SP5 all InfoCubes remain unchanged

Tool support for converting standard InfoCubes

Preliminary lab result: 250 Million records in 4 minutes

No changes of processes, MultiProvider, Queries required




Planned: Consumption of SAP HANA data modelsTight integration SAP HANA Datamart scenarios and SAP NetWeaver BW

CompositeProvider

InfoCubeTransient

Provider

QueryQuery

BW schema HANA schemas

AnalyticViewSAP HANA

SAP NW BWHANA Datamarts and HANA In-Memory

platform for BW can run in one instance

Tight integration between HANA DataMart

scenarios and SAP NetWeaver BW

Providing additional flexibility by combining ad-

hoc data models from Datamarts with

consolidated data in the EDW

No need to manually create/maintain Metadata

for Analytic Views in SAP NetWeaver BW

Transient InfoProider dynamically generated on

top of Analytic Views during Query runtime

Query: e.g. Analysis, Xcelsius, Webi

Integration BW Analysis Authorization Concept




Planned: BW In-Memory PlanningAccelerated planning functions

Database

Layer

Database

Layer

User interface

LayerUser interface

Layer

Application

LayerApplication

Layer

Presentation

Orchestration

Calculation

Data

Presentation

Orchestration

Calculation

Data

SAP NW BW

SAP NW BW SAP NW BW

SAP NW

BW

SAP HANA xDB

Traditional Planning runs planning

functions in the App. Server

In-memory Planning runs all planning

functions in the SAP HANA platform

Performance boost for planning capabilities

like:

Aggregation, Disaggregation

Conversions, Revaluation

Copy, Delete, Set value, Repost, FOX

Performance boost for plan/actual analysis

No changes of planning models, planning

processes, MultiProvider, Queries required




Planned: BW In-Memory Planning Simple Disaggregation Example

Traditional Approach

1. Determine the delta +50

2. Disaggregate (in appl. server)

per week (52)

per branch (500)

26000 combinations / values

3. Send 26000 values to DB to save

HANA-Based Approach

1. Determine the delta +50

2. Send 1 value to DB

+ instruction to disaggregate and how

3. Disaggregate (in DB engine)

per week (52)

per branch (500)

create + save 26000 values

user changes

a plan value


Align Showcase portfolio

Identify | Validate | Approve

Collaborate to Innovate

Key Products | Business Champion

IT Solution Lead

Mobilize Community

Educate | Engage | Develop

Deliver

Corporate Strategy | Global IT Strategy

Evangelize

Customer Engagement | Events | Media

Social Media

SAP Runs SAPAn Engine for Role Modeling and Competitive Advantage


Implementing HANA: High Adoption Rate at SAP

Side-by-Side

• Sales Pipeline

• Profitability Analysis Reporting

• Rapid Deployment Solutions

• Business Process Management

• Customer Product Usage

Real-time Data Store

• BW 7.30 on HANA

New Applications

• SAP Dynamic Cash Management

• Strategic Workforce Planning


HANA Enables New Business ScenariosExample: Sales Pipeline Reporting

Yesterday

in the life of

a Sales

Executive

• 650.000 Opportunities maintained in

different transactional systems (12 million

records, 700 million historical data

records)

• Sales Pipeline reporting compiled into

Business Warehouse

• Multiple reports to conduct trend analysis

• Low Performance and End-User

Experience leads to limited usage

Long Data latency and

data inconsistencies

No sufficient decision

support

No full historical data for

probability analysis

Limited validation of

opportunities

Multiple systems and

applications

High operations and

maintenance Costs

No drill down to single

opportunities

No insights in critical

opportunities

CRM ERP

Business

Warehouse

Data

aggregation

Specific

Reports


HANA Enables New Business ScenariosExample: Sales Pipeline Reporting

Today!

based on

HANA

Live since May 13,

2011

• Data latency reduced from 2 hours to

seconds

• Enables real-time decisions and

significantly increase usage

• Exception based analysis to focus

priorities

• Mobile Device Usage: user specific

access to information anytime anywhere

Increased overall Quality of

Information available

High performance

reporting

Real time decision

Support

Predictive analysis of

historical data

More confidence in

sales forecast

Simplified Architecture

Overall cost reduction

Access to all details

early corrective action

to influence opportunities

CRM ERP

Historical dataHANA

Sybase

Replication

Server


New BI HANA Architecture

Classic

DB

CRM BW

Classic

DB

ECC

HANA

Replication Replication

Extraction Extraction

Business Objects


New BI HANA Architecture

Classic

DB

CRM BW

Classic

DB

ECC

HANA

Replication Replication

Extraction Extraction

Business Objects

Pipeline

Data

CO-PA

Data

ADRM Report

Demo


Take Aways

• Great opportunity to rethink the way we do our business today and leverage this new Technology for redefining the

business processes.

• Distinguish between operational reporting and corporate reporting

• Operations reporting on HANA 1.0

• Corporate analytics on BW and later with BW on HANA 1.0 SP3

• SAP Global IT continues investing in BW solutions

• Usage today for Operational and Agile Data Marts scenarios to replace existing solutions with HANA 1.0

(to analyze huge volume of data in real-time)

• No downtime required on source system side.


Summary

• SAP NetWeaver BW 7.3 offers additional flexibility and modeling capabilities tohighly reduce development efforts

• SAP HANA 1.0 will run non-disruptively side by side SAP NetWeaver BW providing Agile and operational Data Mart scenarios

• SAP NetWeaver BW 7.3 powered by SAP HANA will provide a performance boost for data loads and planning capabilities and a BWA like query performance

SAP NetWeaver BW evolving to a fully In-Memory enabled EDW solution on top of

HANA




Further Information

SAP Public Web:

• SAP Developer Network Enterprise Data Warehousing(general):

http://www.sdn.sap.com/irj/sdn/edw

• EIM 206, The New Planning Modeler and In-Memory Planning Application: Powered by SAP

HANA

• EIM 207, Upgrade to SAP NetWeaver Business Warehouse 7.3

• EIM208, Customize and Automate the EDW LSA Implementation in SAP NetWeaver BW7.3

• EIM 261,Ad-Hoc modeling of In-Memory accelerated data in SAP NetWeaver Business

Warehouse

• EIM 300, SAPNetWeaver Business Warehouse: Powered by SAP HANA

• EIM202, Deep Dive into the SAP In-Memory Technology, Strategy, and Roadmap

http://www.sdn.sap.com/irj/sdn/edw

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Appendix

EIM201

Documents

general product direction

agile data marts

sap netweaver bw7

sap nw bw

sap netweaver bw

sap business suite

memory computing

data services