HANA TDI Business Case Oct 21, 2015 Hari Guleria VP SAP HANA Business Solutions PrideVel ASUG 365...

HANA TDI Business Case

Oct 21, 2015

CSF’s ROAD-2-HANACritical Success Factors 4 business excellence on HANA

Hari GuleriaVP SAP HANA Business SolutionsPrideVel

ASUG 365 SOCAL, October 28, 2015


Agenda

The SAP HANA Roadmap Think Strategy Eliminate Assumptions HANA Appliance or TDI

Buss Case 1- TDI use case Buss Case 2- Value Chain Buss Case 3 – TCO


The SAP HANA Roadmap R/1

R/2

R/3

APO Live Cache

BW Accelerato

r

SAP HANASAP VORA - Cisco Launch at TechEd Key-NoteVORA = HADOOP + HANA

1. BW on HANA2. ECC – Simple

finance3. S4/HANA

In-Memory

In-Memory + Columnar

In-Memory + Columnar 1. Appliance vs. TDI2. On Prem, Hosted,

Cloud3. Hybrid + Cloud

1. SAP HANA Strategic Alignment- Level the Playing Field- Plan Strategy, Deploy Tactics

PLAN your work, and only then WORK your Plan


HANA: Choosing the right solutionEvaluate CAPEX and OPEX based on Business Scenario and Technical Requirements

Business User

Expectations

Business Benefit Decisions

Business Ownership & Accountability

Business Benefit Focused

Reports to Analytics

Data to Visualizations

Mobil Applications

Big Data + Internet of Things

DELIVERTechnical

Optimization

MEET Business

Expectations

SELECTOperations Excellence

(CAPEX / OPEX)

ROIIT Optimization Decisions

Identify Current IT State

Inventory of Current Assets

Data & Selection Optimization

Landscape and System optimization

Performance optimization

Global Architecture Optimization

Strategic ROI Decisions

Client Assets assessment

Client Systems Inventory and Quality Audit

Appliance vs. TDI assessment

On Premise, Hybrid or Cloud Deployment

CAPEX vs. OPEX Assessment

Business Benefit Deliverables

Identify Mid-Term & Strategic Needs

Inventory of Business Expectations

Alignment of Business Expectations

Business participative HANA deployment

Business Benefit Deliverables

Global KPI’s and Metrics deployment


Plan with the SAP HANA RoadmapHANA Alternatives• Stand-Alone BI Appliance• BW on HANA• The SAP HANA Platform• ECC on HANA• ECC and BW running on a

common HANA db

Future 1Current

BW ERP

ECC & BW using same HANA

Stand Alone HANA

BW on HANA

BW

• Out of the Box SAP RDS Solutions

Fixed Time, Fixed Cost Projects• 260 SAP RDS Solutions • CO-PA – Cost and Profitability Analysis• SNOP- Sales & Operational Planning

Single database for Operations & Decisions

BW

ECC & BW on separate HANA

ERP CRM

ER

P

BW

SAP AppsUsing same HANA

CR

M

PLM

SC

M

Future 2

• 100% Custom RBS Solutions

OUR SWEET SPOT – Fixed time, Fixed Cost • 100% Custom Solutions• Non-SAP or SAP Source Systems • Driven by Business Design Thinking


How much data to keep where

Attribute HANA HOT WARM HANA COLD Data Pool

Information Access

Frequent/High Access, Daily and weekly reports

2013 Same as HOTQ2 2014 select

Infrequent access. Monthly, where speed / RT is not required

Very Large Data pools, Regulatory, Historical data

Data Slicing 2-3 years (High Usage) > 2/3 years, Low Usage Streaming & Historical Data

Cost High-Cost Included in Appliance

Lower Cost ‘(Automatic DCM*)

Low Cost, Very Large Volumes

Data Type Very-Frequent Changes Same as Hot SAP - No ChangesHP - Allows changes in COLD

Streaming – Very High ChangesHistorical- Compliance Data

Best Practice starting point (%)

All BWA Indexed Cubes (30%)Physically partition over 3 yrs.

2013 - Same as HOTQ2 2014 - Planned

All non-BWA Cubes (50-70%)3+ years partitioned Cubes

80% Streaming Data20% Historical Data

Query Performance

As fast or better than BWA

Slower than HOTFaster than COLD

As fast of better than BW Business Need DependentData Profiling driven

HANA HOT HANA COLD ARCHIVEColumnar/ In-Memory Columnar NA

Age of Data

On-Line / Instant Near Line / Lag Off-LineQuery Performance

Cost Merged Queries

*DCM – HANA Data Change Management

COLD

HOT

WARM

Data

Tie

rin

g

Sybase IQ

Data Pool

HadoopSan


Big-Data: Upstream to Downstream

98% of enterprise information only looks inside the firewall

MidstreamGlobal Enterprise

Leverage technology to enhance business value

Visibility

Enterprise data sources

98% of what makes Brands succeed/fail

lies outside the firewall

Upstream

Distributors

Vendors

Subsidiaries

SBU’s

Operations

E-Orders

Plant3

Plant2

Plant 1

Upstream Data

98% of what makes Brands succeed/fail

lies outside the firewall

Downsteam

$ € ¥Buye

rsTweets

Videos

Blogs

Mails

Facebook

www

Radio

Media

Tablets

WIFI

SMSSearc

h

Photos

Reps

Retailers

Volume

Variety

Velocity

Vulnerability

DownstreamData

Operation

Reportss

FixedReports

Report Based

Decisions

Real-Time

Operations

Real-Time

Analytics

Real-Time

Decisions


HW Options Scale Up

Single Node Recommended for SoH- ECC, CRM and SCM

Scale-Out Multiple Nodes Recommended for BoH - BW

2. Eliminate assumptions- HW Options for SAP HANA- The Pros and Cons of Appliance vs. TDI- Competitive Differentiators of Cisco TDI

Our biggest point of Failure is ‘Assumptions’


The Biggest Mistake

In BI, Big-Data and now SAP HANA

= ASSUMPTIONS

The CureRecommendations based on

empirical facts

According to Gartner ‘Fewer than 50% of BI ad Big-Data projects will meet business expectations between 2012-15’Our interpretation ‘Less than 50% of reports in your BI PRD environment are being used by your business users’


Typical HANA Assumptions

1. HANA is just another technical Install/upgrade

2. Minimum business participation is recommended

3. HANA costs cannot be decreased4. Optimization of SAP is not required for

HANA5. Move to HANA ‘As-Is’6. There is no need to plan for this new

platform


Portfolio for SAP HANA Deployment

Plan Build Migrate Run

Design Optimize Migrate Run

• Road 2 HANA**• SAP Discovery

• Upgrade 4 HANA • HW/db Migrations• Migrate to HANA

• Full Basis Support

• SAP Db Optimization

• Infrastructure optimization

• HW optimization

• Optimize for HANA• House Keeping• Auto Cube

Remodel• Auto ABAP

Recode

• DMO- Upgrade & Migrate

• Migrate from/to Cloud

• Augmented Basis Support

• KBS Support

• GPS Workshop*• HANA HW ROI/TCO*

• TCO Reduction • KBS Basis Support• Augmented Basis

Support

• Post Prod Optimization

• Reduce HANA TCO by 40%*

• DB Cleanup • Angel Support* • Angel Support*

• Appliance or TDI*• On Prem / Hosted /

Cloud

• TDI Validation* • Safe Passage* • Safe Passage*


Discovery Workshop 2-4 weeks per application Analyze in detail

Application landscapes across the globe Goal:

Plan your work and only then work your plan Assumptions = Technical only solutions and

higher costs Goal: Highest quality at the lowest cost

3. HANA Appliance, TDI or Cloud

- HANA Maturity- Appliance vs. TDI vs. Cloud- Competitive TCO benefits frm Appliance TDI

Cloud

Have a strong Infra & Basis Team or need to leverage Assets


Infra + HW Decisions On- Premise, Hosted, Hybrid or Cloud

Appliance or TDI

CAPEX or OPEX


HANA Evolution : Appliance to TDI

TDIAPPLIANCE

SP1 SP3 SP4 SP5 SP6 SP7 SP8 SP9 SP10 SP11 SP12SP2

TDIAnnounced

Past

FUTU

RE2010 2012 2013 2014 2015 2016

Evolution from Appliance to TDI

Greater Maturity > More Openness > More Choice > TDI

According to SAP

Read SAP’s Presentation “The New Economics of SAP Business Suite Powered by SAP HANA”, Delivered in Palo Alto, on October2014 for the “SAP HANA and IoT Event”

SAP HANA TCO Optimization

Appliance

ModelHybridModel TDI

Model VirtualModel

Decreasing TCO

HA

NA

TC

O

2010 2013 2014 2015

Cleanup

Greater Benefits > Lower TCO > TDI > Virtual Cloud

CLOUD

CLOUD


Appliance vs. TDI

BLACKBOX

(Do not Touch)

Man

dato

ry

APPLIANCE

Compute

Network

Admin

Storage

OSRed Hat/

SuseHANA

SW

T D I

Man

dato

ry

Cust

om

er

Sele

ctio

nsCisco UCS

Cisco Nexus

Admin

EMC/ NetApp

SUSE/ RHEL HANA TDI Modern Approach TDI - Mature Technology vHANA- Virtual HANA

HANA Appliance Maturing Technology Legacy Approach 100% Hosted Box

Utility Business Case 1 - TDI

1. Think IQDCT (Increase Quality, Decrease Cost and Time)

2. TDI Build Advantage


HANA Business Case -1- BW, ECC & CRM to HANA

Major US Utility• Need Flexible Infra• Leverage existing

partners• Leverage existing

knowledge• Support HANA in 6-

12months• 100% On-Prem model• 31 million customer

analytics

IT Needs• Strong Infra & Basis Team• Built with world-class

components• Phase 1 BW• Phase 2- ECC and CRM• Phase 2- Downstream

data from > 32 million customers• Phase 3: Upstream

data from Plants and Suppliers

Delivered• Lowered BW TCO by

42%• Landscape TCO by 48%• Delivered• BW + ECC + CRM• Big-Data Hadoop• Enterprise Integration


Each System in the landscape is a separate TDI ‘Open Systems’ = 8 vs. 21 boxes

SAP BW +

Hadoop +

SAP ECC +

SAP CRM +

HANA TDI Build

BDR

SAP ECC Landscape

EDR

SAP ECM Landscape

CDR

SAP BW Landscape

NP 2

BQ1

BQA

EQ1

EQA

CQ1

CQA

HQA

NP 1

BD1BW Project’sPipeline

BDEVBW Production

Pipeline

ED1ECC Project’sPipeline

EDEVECC Production

Pipeline

CD1CRM Project’sPipeline

CDEVCRM Production

Pipeline

HDEVHadoop

ProductionPipeline

BPRD

EPRD

CPRD

HPRD

O&G Business Case 2 – Value Chain

- Business Case- The Roll-up Architecture for Information

Quality- Building the Value Chain Bridge components


HANA Business Case -1- Data Quality & IoT

Business Needs• Executive numbers did

not match operational reports

• Deploy Upstream Analytics• Deploy Predictive Maint.• Aim for Zero Maintenence• 17 million data records per

hour• 99% of the data said ‘I am

good’

IT Needs• Improve Business

Confidence• Mature Infra & Basis Team• Build with customer partner

components• Build Data Lake for very

large streams • Identify Predictive

Maintenance models

Delivered• Hadoop for Streaming Data• Pattern and filtering at

Source• Delivered• BW + ECC + CRM• Big-Data Hadoop• Value Chain Information


Roll Up Informatics Architecture for O&G

Enhance Business Benefit Information

Supply Chain Network Design

Executive Informatics

Upstream Capability Analysis

Route Profitability

Downstream Competitive

Analytics

Assets Lifecycle Management

Supply Chain Visual Analytics

Forecasting, Planning & Predictive Maintenance Support

Assets Performance

Repair Ops Performance

Maint Performance

Engineering Performance

Service Performance

Global O&G SCM

Performance Metrics

MaS Maintain &

Support Analytics

Consolidated Operational Performance Metrics

Global Assets ‘Class -A’ Critical Process Metrics ATP

Rig DetailsCrew Service

SupportOn Time Setrvice

In Process Analytics

Crew Analysis

Real-time Service

Analytics

Downstream Customer emtions

Value Chain VisibilityRigs & Platforms Type Partners Leases Crew Operations Deviances Issues Marketing Support

Rigs & Platforms Manufacturer Spares Tankers Monitoring Fill-Rate/Consumption/Repairs OSHA Maintain Support

Supply Chain Administration

Reports Analytics Real-Time IoT enabled Predictive Maintenance


The IoT Value Chain Architecture for O&G Reports Analytics Real-Time IoT enabled Predictive Maintenance

TA

CTIC

AL

STR

ATEG

ICO

PER

ATIO

NA

L

Crew Service ANALYTICS

In Process ANALYTICS

Predictive ANALYTICS

Global Service DATA

In Process DATA

Predictive DATA

Global Assets ‘Class -A’ Critical Process Metrics ATP

Manufacturing DATA

Rigs & Platforms Manufacturer Spares Tankers Monitoring Fill-Rate/Consumption/Repairs OSHA Maintain Support

Full VALUE-CHAIN Optimization Matrix

Total VALUE-CHAIN VisibilityRigs & Platforms , Inbound Logistics, Type Partners Leases Crew Operations Deviances Issues Marketing Support

Rig Parts DATA

Crew DATAOn Time Setrvice

DATA

In Process DATA

HR & DATA

Operations Data

Spares & Service

DATA

Raw Material

DATA

Summarized Upstream Data for UPSTREAM Feeds

Assets DATA

Service DATA

Repair Ops DATA

Engineering DATA

Assets Performan

ce

Service Performan

ce

Repair Ops Performanc

e

Engineering Performanc

e

Upstream Capability Analysis

SUPPLY Signal Planning

Data Summarization

& Filtering

Upstream Forecasts & Predictive Analytics

TA

CTIC

AL

STR

ATEG

ICO

PER

ATIO

NA

L

Finished Products

DATA

Customer Tweets DATA

Cust Satisfaction

DATA

Logistics DATA

Retail Service DATA

Customer Emotions

Wholesale StockDATA

Spares & Service DATA

Summarized Upstream Data for DOWNSTREAM Feeds

Assets DATA

Service DATA

Repair Ops DATA

Engineering DATA

Assets Performan

ce

Service Performan

ce

Repair Ops Performanc

e

Engineering Performanc

e

Downstream Competitive

Analytics

Downstream Customer Analytcs

DEMAND Signal

Forecsting

Data Summarization & Filtering

Downstream Forecasts & Predictive Analytics

Forecasting, Planning & Predictive Maintenance Support

Assets Lifecycle Management

TOTAL Value- Chain Visual Analytics

UPSTREAM DOWN STREAMMIDSTREAM

Route Profitability

Supply Chain Network Design

Demand Signal

Management

Executive Informatics (Trends)

Downstream Corporate Data

Feeds

Upstream Corporaate

Analytic Data Feeds

Data Mish-Mash

Summarizations

Hi Tech Business Case 3 - TCO1. 3 SAP BW environments

2. 2 ECC Environments

3. Define Options to Move to HANA

4. Increase Quality and Decrease Cost & time for Migration


HANA Business Case -3 – 3 BW’s and 3 ECC’s

Business Needs• Define Road-2-HANA• 3 BW Systems in US, LATM

and APO• 3 ECC Systems US, LATM

and EMEA• Define the optimal path to

HANA• Opportunities to lower

Costs

IT Needs• Start with BW • On 7.01 running 3.5

content• Old Browser on One system• Special SPM on another BW

7.01• Opportunities to reduce

TCO

Delivered• Reduced BW footprint by

42%• HANA migration costs by

67%

• Improved Data Quality• Improved Information

Quality• Decreased Costs• Decreased Time


4 week - Discovery workshop results

BW

Pre Project Mandatory Tasks

Reduced BW db by 42%

Lowered HW costs by 40%

Migration Used DMO to

Upgrade 2 BW systems to 7.4

Merge 2 BW Systems

Migrate 2 BW systems

Click icon to add picture

Questions?

THANK YOU

Simplifying SAP Upgrades and HANA Migrations on UCS

Your Professional Partner in Business Excellence

SAP’s official HANA Group

World’s largest HANA Group


HADOOP INFRASTRUCTURE• Established Big Data infrastructure• Migrated and normalized data sets• Developing visualizations, tools and predictive

analytics

DISPARATE DATA SETS• Integrating 15+ siloed data sources in multiple

file formats• 10 terabytes of data• 3 year historical data ecosystem

MINING COMPANY

PROJECT SCOPE• 252 trucks• 200 sensors per truck• 7 mine sites• 11,000 readings per second

DATA LOGGER

DATA LOGGER

DATA LOGGER

Stratifying Alarms:1. Urgent component problem2. Critical sensor problem3. Important/not urgent

component/sensor problem4. Not important component/sensor

problem5. Noise – ignore

Urgent component failure models: engine, transmission, differentials, torque converters, final drives

Data/analytics-driven timing for preventative maintenance (e.g. oil changes) on individual trucks

BUSINESS IMPACT• Higher mining equipment up-time• Reduced critical component failures • Predictive Analytics • Preventive maintenance= Increased

productivity

IoT &

Big

Data

EQUIPMENTMAINTENANCE

(SAP)

DISPATCH &OPERATOR(TERADATA)

FUEL, OIL,ANALYSIS, ETC.

(SQL SERVER)

TRUCK SENSOR

DATA(Osi Pi

SERVER)Phase

1

SAP HANAIntegration

(SAP)

Phase

2


HADOOP INFRASTRUCTURE• Build Data Lakes for large streaming data

feeds • Source filtering enhanced in the Data Lake

areas• Ability to query atomic and summarized feeds

to HANA• Alerts triggered by event types from HANA

feeds

DISPARATE DATA SETS• Integrating 75+ siloed data sources in multiple

file formats• 120 terabytes of data• 24 month historical data ecosystem

OIL & GAS COMPANY

PROJECT SCOPE• 2,624 Oil Rigs • 2,700 sensors per rig• 38 global sites• 264,000 readings per

second

OIL RIGS

TANKERS

DATA LOGGER

Stratifying Alarms:1. Video Alerts from Underwater

Well Heads2. Sensor enables Signal Data 3. Filtering out ‘I am Good’ data

close to source4. Passing through total data in

Batches5. Real-time Alerts and Analytics

Urgent component failure models: Rig, Bearing, Drill, transmissions, diferentials, torque signals, etc…

Data/analytics-driven timing for predictive maintenance, breakdown prevention and deep sea well-head monitoring along with Value chain visualizations

BUSINESS IMPACT• Reduction of Manual observation from

24 personnel to 5• Automated Pattern identification to

eliminate failures. Build 1100 algorithms for patterns

• Automated Deep sea well head Alerts • Faster Alerts than before by 68%

IoT &

Big

Data

DATA LOGGER

OIL RIGSDATA LOGGER

DATA LOGGER

PIPELINES &U/W OIL HEADS

Intelli-Sensors (Cisco)

RIG’s & EQUIPMENTMAINTENANCE

(SAP)

STREAMINGDATA LAKE(HADOOP)

WELL HEADVIDEOS

(HADOOP)

TANKERS SENSOR

DATA(Osi Pi

SERVER)

SAP HANAIntegration

(SAP)

Phase

2

HANA TDI Business Case Oct 21, 2015 Hari Guleria VP SAP HANA Business Solutions PrideVel ASUG 365...

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Transcript of HANA TDI Business Case Oct 21, 2015 Hari Guleria VP SAP HANA Business Solutions PrideVel ASUG 365...