Best practices in HANA implementations - real client experience

(1)

Best practices in HANA implementations - real

client experience

Penny Silvia, IBM

June 2013

(2)

Content

 The SAP HANA Current Market



IBM’s Approach to SAP HANA

(3)

SAP HANA is addressing key needs of current leaders

(4)

Global Trends that are impacting Business Analytics

 Everywhere Mobility

 Growth of Social Networks and Collaboration

 Big Data

 Increasing pervasiveness of Analytics

Everywhere Mobility

How do you enable BI for mobility

How does that impact access and security? How does this impact

your strategy?

Social Networks and Collaboration

What new opportunities will arise to exploit BI? How will your clients

collaborate differently? What new use cases will

develop?

Big Data

How will you take

advantage of sources? Can you leverage real time

analytics?

What is the impact to your architectural strategy? In-Memory? Cloud?

Pervasiveness of Analytics

How will you embed analytics into your business process? How do you deploy

simpler, faster and cheaper?

What will self service mean for you?

(5)

Advanced Analytics Platform

End-use Applications

Analytics Visualization Big Data Analytics Warehouse

Predictive Analytics

Sense Analyze Act

Search / Explore KPIs Dashboards Drill-Downs Reports Marketing Campaigns Rules Engine Behavioral analysis Outcome Optimization Propensity Scoring Model Creation Structured / Unstructured Data

Data Governance Data Integration ETL Cha n g e Cap tu re Dat a Q u a li ty / V a li d ity / S e cu ri ty - P ri va cy F o rm a t / Uni t Con ve rsi o n Con so li d a ti o n / De -d u p li ca ti o n D a ta R e p o s it o ries Network Data

Customer Behavior Data

Cust o m e r Dat a P ro d u ct Da ta Net w o rk T o p o lo g y Dat a C o n tinu o u s Fee d S o u rc e s Usage Data Reference Data

Historical Analysis Data

Demographics Segmentation Location Past Actions Propensity scores Behaviors

Predictive Model Deployment Actionable Insight

Stream Processing

Streaming Data

Operational Systems

Big Data – Advanced Analytics

High Performance Historical analysis (Big Data Platform) Model Based Analytics - behavioral scoring, micro segmentation, correlation detection analysis

Real-time scoring, classification, detection and action

Visualize, explore, investigate, search and report

Take action on analytics

The new market drivers are driving new analytics

architectural requirements to capitalize on the

opportunities of integrating the front and back office

(6)

Game Changer: BusinessSuite on HANA

 Suite on HANA (SoH) is the latest offering of HANA from SAP, bringing

into the mainstream application space for the first time. It follows the

launch last year of HANA for standalone applications running in

‘sidecar’ mode, and of BW on HANA. SoH is in ramp-up currently.

 ECC, CRM and SCM can run directly on HANA as a database. HANA

replaces Oracle, DB2 or MS-SQL as the underlying database software

for the SAP application.

 SoH at the moment has no functional changes or enhancements, but

does incorporate some optimization of specific functions to run on

HANA (accomplished by re-writing the underlying code to run at the

HANA database level, rather than within the ABAP stack in the SAP

system).

 At one level, therefore, use of HANA with SAP Business Suite is a

straightforward technical database decision. BUT the differentiating

value, comes from the specific optimization of business transactions,

and the potential use of HANA as a platform supporting

memory-optimized applications in a range of SAP environments.

 During 2013 SAP launced SAP HANA Live to provide a logical view of

the data structures in a way that is accessible to analytics tools such

as Business Objects. SHLive will provide more capability to define and

execute both operational and analytical reports directly from the ERP

system, without use of BW.

(7)

Possible Areas of Value for SoH

Current situation New way of thinking, with Suite on HANA

Client has difficulties with long-running batch processes

Accelerate batch processing and remove operational problems

Analysis is based on summaries, categories and aggregates

Analysis can be done at a granular level, by transaction

Reporting is periodic and after-the-fact Reporting can be on-demand and immediate

Data is out-of-date because of latency in populating reporting systems

Can perform analysis on operational data in SAP ERP

Analysis is based on periodic single runs (e.g. MRP)

Can execute multiple, iterative runs to fine tune analysis

Reports are used to support transaction processing, providing secondary information

Reports aren’t needed. Critical analysis can be done in-line, as part of transaction processing

Reporting is backward-looking Analysis is forward-looking, with predictive

analytics enabling modelling and planning Reports are the main tool for monitoring the

business

The business can monitor itself, looking for key exception conditions, patterns, triggering events and alerts that in turn initiate new processes

(8)

(REAL) Examples of outcome from HANA-enabled solutions

Object Type Test Case

Conventional SAP BW 7.3 on Oracle 10 SAP BW 7.3 on HANA 1.0 SP03 DSO 2 key fields 0 Navigation attributes

 Full load of 3.5 mio records from PSA 18 min 3.5 min

 Activation of 3.5 mio records 34.5 min 0.3 min

DSO

2 key fields

210 Navigation attributes

 Activation of 3.5 mio records not tested 2.5 min

DSO

8 key fields

0 Navigation attributes

 Full load of 3.5 mio records from PSA 18 min 9 min

 Activation of 3.5 mio records 38 min 2.5 min

Infocube  Full load of 1.5 mio records 60 min 12 min

Info Object  Selective Update of 14.6 mio master data records not possible 90 min

 SAP BW on HANA will speed up the access to information, with limited investments to the

current landscape.

 IBM has experience in migrating current BW environments to HANA, both technical as

functional

 Also we see clients taking it on as a technical project and clients who use the migration as a

opportunity to clean up the data structures in BW, as they typically have grown over the

years. The latter get the best results.

(9)

(10)

IBM’s Data and Analytics Factory for a Maturity Leap

IBM’s Data and Analytics Factory offers:

•Labor Arbitrage applied to Data Management and Operational BI •Factory Model allows for maximum allocation of resource capacity •Platform standardization allows for higher reusability

•Cloud based infrastructure

•Pre-configured IBM content to accelerate capability deployment

•Standard KPIs, Drilldowns and Benchmarks for accelerated data visibility

•Industry Use Cases for Real Time reporting •Predictive Algorithms for Process Optimization •Pre-defined mapping for fast data integration •Standard Mobility deployment model

Year 2: Mobility

Real Time Analytics on Device

Step 2: Reporting & Metrics Embedded Operational Analytics

Reporting Packs by Facility

Year 3: Predictive Insight

Predictive Algorithms & Pattern Recognition

Step 3: Mobility & Real Time Real Time Analytics on device

HANA Enabled Used Cases

Real Time Metrics on device, Real time Analytics Powered by HANA

Embedded Analytics, Self Service Model

Executive Dashboards

Cascading Executive Dashboards, BDR

Data Operational Support (COE)

Management of Master Data

Data Process Management

Path to World Class Business Analytics

Testing Testing A na ly ti ca l S ol utio ns

Year 1: Data Efficiency

Data Quality & Data Visibility

Step 1: Data Efficiency Data Quality & Data Visibility

Information Governance, Use Case Dev

Year 4: Optimization

Embedded Analytics

Step 4: Predictive Insight Predictive Algorithms & Pattern

Data Governance

Data Governance Data Intelligence Management_{Data Intelligence Management}

D ata Fa ct o ry Data Technology Data Technology Information Consolidation Information Consolidation

Process Embedded AnalyticsSupply Chain Optimization power by ILOG

Recognition

Analytics Services

Data Services Testing

Path to World Class Business Analytics

IBM Data and Analytics Factory enables higher reusability and standardization with embedded Leading Practices

(11)

Clients

• SAP continues to evolve and

extend their application platform in key areas such as mobility, analytics, HANA, and the cloud. These capabilities can drive new business outcomes, but cannot be looked at in isolation.

• The LSS serves as a focal

point for all of IBM’s capabilities to enable SAP customers to understand what is possible AND how they can get there.

Industry Specialists IBM Business Analytics & Optimization Services and Solutions IBM Global Delivery Centers IBM Research IBM SAP Services

• Pre-built Assets & Tailored Solutions • Fully integrated mulit-nodal architects

On Premise Virtual

Briefings Centers IBM

Vision,

Roadmaps,

& Solutions

Goals &

Requirements

• Full SAP Business Objects, Enterprise HANA, BW on HANA, SAP Mobility, SAP ECC, and SAP LOB

solutions Integration

• Production level, multi-node infrastructure powered by • Programmatic Collaboration with IBM Research

(12)

IBM In Memory Solutions through HANA Services

Design/Build

•SAP Mobility & Analytics Innovation Center

•HANA POC/Pilot/Implementation •Visualization and Analytics Design & Development

•BW Upgrades & HANA Migration •Solution Accelerators

•# 1 Ranked SAP Practice

Strategy

• In Memory Analytics Advisory Services/IBM Research

• HANA Roadmap and Assessment • In Memory Workshops and Value

Identification services • Analytics Mobility Planning • Analytics Competency Center/Factory • Information Governance Infrastructure • Infrastructure Planning • Solution Architecture • System Optimization/Modernization • High Availability and Disaster

Recovery

• Installation Services • Hosting & Support

Enterprise Information Management

• IBM Ready-to-launch

• Data Governence & Security • Data Security

• Performance Optimization • Enhanced Workflow- and

Tracking systems • Data Modeling • Data Provisioning

Govern

Sustain

12 Operations • Application Management • Strategic Outsourcing • Infrastructure Management • Data & Analytics Factory

Innovate

_Design

Deploy

Sustain

IBM is the only provider with

competencies and

experience across the full

spectrum from physical

transformation layer to

algorithm design to ongoing

operations

(13)

Selective IBM Experience in SAP HANA

(14)

1. Improving Procurement Analytics through Enterprise HANA Implementation

Function Areas Covered

Procurement Analytics

- Provide vendor delivery performance - Enhanced variance analysis and detailed procurement analytics

- Real-time analysis of goods movement. -Material& vendor master reporting

including audit & adhoc reporting

14 6/19/2013

 IBM India involved in end-to-end

implementation of the SAP HANA solution

 Used Agile Methodology

 Performance of complex reports in

Procurement Analytics and Material/Vendor master has improved remarkably

Challenge

 The client is one of the major US based manufacturer of thin film photovoltaic (PV) modules.

 Client had performance issues with existing reporting solution and decided to deploy a new solution with

enhanced reporting capabilities & improved performance – Flexible with changing business requirements

– Big data & real-time reporting capabilities

– Detailed line item level reporting

 The first phase of the project focused on supply chain

management. Finance and inventory models are planned in

later phases.

Solution

 IBM implemented a scalable in-memory business analytics solution with SAP HANA, BusinessObjects 4.0 and SLT to provides real-time reporting to support key business

decisions

 Solution focused on prioritized business use cases  Team structure – 1 onsite SME, 3 offshore resources

(15)

Lessons Learnt

Enterprise HANA Implementation – Technical Overview

15

• Complex logic for GR Date and IR Date created through SQL. SQL Script the implementation in HANA context different

• Single Sign On (SSO) using Kerberos and Windows AD authentication for HANA

• Data Loading through SLT sometimes stopped, the problem solved through applying SAP notes

• SAP HANA is evolving, frequently check SAP Notes for new features and fixes

• Expert functions in SLT monitoring dashboards ‘do not’ do everything, housekeeping is required

• HANA SQLScript – Implementation is different compared to other SQL scripting languages, only “Read Only”

procedures are supported in Calculation view

• Currency Conversion supported only for ‘base’ measures, calculated attributes and calculated measures are not supported

ECC Dev ECC QA ECC Prod

SLT Dev SLT QA SLT Prod BO Dev BO QA BO Prod DEV QA Prod System Landscape

SAP HANA SAP HANA

Technical Components

ECC (NetWeaver 701 Support Package

135)

SLT (NetWeaver 702 Support Package 300) HANA (SP4, Revision Level 41)

BO 4.0 (IDT, Web Intelligence and BO

(16)

Enterprise HANA Implementation – Methodology Overview

Evaluation Preparation Blueprint

Realization

Final Preparatio

n

Go-Live Hyper-_care Detail Desig n Develo p Test & Validat e Lessons Learnt

• Perform a complete assessment of the landscape, not just for upgrade or migration but from Data Volume optimization, HA/DR strategy, Archiving Strategy, Reporting Strategy

• Maintain flexibility in the plan

• Business transformation and IT should be closely aligned

• Process knowledge is mandatory for proper HANA modeling

Methodology used

• Modified Agile (with discipline) methodology has been adopted

• Scope finalized during the Blueprint phase

• Iterative approach followed during the realization phase

• Master data reporting (3 weeks)

• Procurement Analytics (4 weeks)

IBM India offshore team

involved in every stage

(17)

Business Analytics is important to First Solar

First Solar panels produce power at a cost of $0.75 / watt. This is the ultimate measure of the company’s success.

Drive down

manufacturing costs

Analyze all data

sources pertaining to

panel performance to

maximize output

Use real-time tactical

information to drive

value

 Analyze manufacturing data to glean insights into how yields can be

improved and processes optimized

 Get real-time information related to manufacturing performance by

line and by plant, and quickly spot and resolve manufacturing performance problems and implement corrective action

 Ensure that sales related information is available real-time

 Empower Finance with the capability of doing rapid variance analysis

to quickly understand why costs are higher than expected

 Improve the overall efficiency through which operational information

is disseminated throughout the organization

 Large volumes of data from services installations must be analyzed

to help understand how to maximize power outputs and minimize service disruptions

 Help predict when failures are imminent

(18)

Several business value cases have been identified …

SCM Analytics Power Generation Data Analysis Customer Service Enablement Continuous Improvement Analysis Finance Analytics BPC Acceleration

 Rapid access to tactical information

 Enhanced variance analysis and detailed procurement analytics

 Analyze large volumes of information gathered from the field, pertaining to panel

installations

 Gain an understanding into what conditions affect power output

 Integrate data from Sales orders, Contracts, Warranties, Rebates, and

Manufacturing

 Gauge the efficiencies in Customer Service, and enable analysis

 Enable continuous improvement of professionals to perform data analysis

against large volumes of manufacturing data quickly and effectively, to drive improved yields and manufacturing efficiency

 Implement RDS for operational reporting

 Leverage finance models and bring G/L reporting onto HANA to facilitate faster

month-end closing processes

 Accelerate BPC on HANA

(19)

Challenge

 UK’s leading retailers, few million people visiting their 700 UK stores each week.

 Products sourced from around 2,000 suppliers globally.  Daily data load were about 20 million stock movements.

Query runtime for sites was running for couple of hours and Users were not able to run it

 Refresh of stock position to external systems for all 720 sites were taking 3 to 4 days .

Solution

 BW on HANA implemented for Inventory report with Non Cumulative Key Figures only modelled in Info cubes & HANA Optimized

 IBM & SAP jointly executed this POC

 Material Movements loaded into the cube a round

1.3 billion.

 Reduction of Application Level maintenance - No Performance / Rollup Tabs in the Manage screen .  No DB statistics / Index/ Compression Roll Up

required.

 Faster data loads 577.2 million records from PSA to Info cube 4hours 34 minutes.

 Faster Standard DSO Activation times.

Results



All 720 Sites drill down on Non

Cumulative Key Figures at site level

took 1.24 Seconds on 1.3 billion records

.



A comparative analysis with BW 7.0

showed 6 times faster APD runs on

BW-On-HANA

.

(20)

3.Emergency & In-patient Services Run better in A Spanish Healthcare

 Scenario details:

Business problem:

In order to get answers to specific questions that can arise

related to one or several healthcare areas, reporting over large tables with high

granularity becomes a need for healthcare managers. For this POC,

Emergency and

Inpatient Services are the two key areas identified where data availability becomes a

must in order to grant the best performance at the right time.

Proposed Solution:

The solution is created on a HANA Enterprise and covers both queries over millions of

records and Real-Time Data acquisition.

• Queries run 85 – 90% faster

• Patients diagnosed with diabetes who needed dialysis and were admitted during the last month (60M records) • Home hospitalization patients admitted to emergency and destination at discharge is not conventional

hospitalization (30 M records)

 Load incremental data every 30 minutes

(21)

4. HANA Support for a Medical devices major in USA

Client Overview

 Our client is the world’s leading medical technology company, developing products that help patients

with a wide variety of chronic diseases.

Challenges

 The CRM-BW project is very critical for the client as the FDA and other regulatory body reports from different countries will be generated by using this data.

 Multiple SAP and Non-SAP sources used for extracting data both into ECC and BW systems causing heavy duplication and thus adding up to data volume optimization scenarios (point in time 15 Tb data in BW).

 DSO Activation in Business layer taking lot of time & Reporting performance problems - reports both in ECC and BW taking up to 40 minutes.

Solution

 SAP HANA as Side Car implemented : complex reports in CRM & Global sales reporting area has been improved remarkably. BW on HANA being implemented for some of the problematic reports.  IBM is currently supporting their Analytics landscape which includes HANA, BODS and HANA-Admin

work. SAP CRM BW Legacy Data Source HANA 1.0 BO Reports (WebI, Dashboards) BODS ETL BODS ETL BO Universe

(22)

5. Sales Order Reporting Gets Faster on HANA for a leading international

paper and packaging group

• Client has BW with BWA on top of four SAP ECC and other 3rd party ERP systems • Reporting front end is BOBJ and BW BEx

• Biggest SAP kernel has extensive reporting in BW and BOBJ

Initial situation

• ETL processes take

a lot of time (night too short)

• Reporting on critical sales data has a time lag of up to 24 hours

• Client sales orders are created but results not immediately available for reporting – no fast reaction on price changes possible

• Report refresh takes up to 30 Minutes

Issues

• Phase 1 in IBM Lab – copy of productive customer ERP system • HANA PoC based on sales data

• Online replication using SLT

• PoC based on IBM hardware, and IBM consulting services

(23)

• Functionality which was done by BW, BW – OLAP, MDX Interface and BOBJ Report was moved to HANA DB. • Navigation time reduced from up to 30 Minutes to 5 – 10

seconds

Performance

• Set up Online replication via SLT for all data needed in HANA • Each change in SAP ECC Data is immediately replicated to

HANA DB BOBJ reports

• No impact on ECC performance

Online Data

• HANA compresses ECC tables with an overall ratio of 8 – 10.

Compression

5. Sales Order Reporting Gets Faster on HANA for a leading international

paper and packaging group – contd..

(24)

 For a mid-sized Canadian food processing company with annual sales of $5.2 Billion. The SAP program at this client consists of a 4.5 year roadmap designed to provide a common ERP-based framework across its Agribusiness, Bakery, and Meat Products groups.

 3 years into their program, MLF was still unable to

effectively extract profitability information and produce crucial analytical reports. Due to a high volume of records, the existing Business Intelligence architecture was taking nearly a full day to extract the data; often, the system would crash during report generation. With a week’s worth of billing documents totaling up to 109 million records, MLF needed a way to quickly extract this data from the system and turn it into valuable

business information.

Solution

 As MLF’s systems integrator, IBM partnered closely with SAP to deliver the first SAP HANA go-live in Canada in December 2011.

 The pilot implementation extracted profitability and trade spend data from ECC and CRM into SAP HANA and presented this information in a user-friendly manner through BOBJ Web Interface (WEBI).



With the enhanced SAP HANA

solution, the same information

that now takes 6-10 seconds to

extract

.



MLF intends on continuing to

partner with IBM and SAP to

implement SAP HANA across

many other Business Units and

reporting areas

6.Profitability Analysis for a Canadian Food Processing company Runs in

few Seconds on SAP HANA Enterprise

(25)

7. Client Reference – SAP HANA POC in collaboration with SAP

Background

 A US-based bank had problems with the BW loading time as well as report performances and wanted to use the SAP BW on HANA capabilities

 The need was to demonstrate the capabilities and migration context of introducing HANA in the Bank’s DB

layer with the ultimate purpose of improving performance, scalability and reporting flexibility.

IBM Solution / Results

 As the client’s systems integrator, IBM partnered closely with SAP to demonstrate the following

successfully in June 2012

– Demonstrate performance improvement in data load to BI through the substitution of DB layer through HANA

– Provide benchmark of effort related to migration from DB2 to HANA based on POC so that Client can have a reliable estimate on overall effort to migrate

– Demonstrate scalability of HANA solution to a data volume and

landscape that is reasonably representative of the Bank’s production – Demonstrate SLA improvement in data flow source to report

– Demonstrate reporting capabilities form HANA available through SAP BO



Key Findings

–

Loading time decreased by a factor of 5

–

Reporting time decreased by factor of 48

25 6/19/2013

25

Solution Components

SAP BW on HANA SAP BOBJ

(26)

8. Client Reference – SAP HANA PoC in collaboration with SAP

Background

 A major European Automobile manufacturer

 Over 360 Million Records had been loaded into HANA

IBM Solution / Results

 IBM partnered closely with SAP to execute the HANA POC as well as do HANA Basis

maintenance and support



It was also done using IBM’s Data centre

– Ca. 356 Million Records are used in and utilized by the Data Model and

Explorer shows all Line Items of GLPCA

– Posted Data was faster replicated into HANA

26 26 Solution Components SAP HANA 1.0 SAP BO 4 SAP ECC SAP PI SLT

(27)

9. Implementation of SAP HANA at MLF



The Company:

Maple Leaf Foods (MLF) is a mid-sized Canadian food

processing company with annual sales of $5.2 Billion. The SAP program at

MLF consists of a 4.5 year roadmap designed to provide a common

ERP-based framework across its Agribusiness, Bakery, and Meat Products groups.



The Issue:

3 years into their program, MLF was still unable to effectively

extract profitability information and produce crucial analytical reports. Due to a

high volume of records, the existing Business Intelligence architecture was

taking nearly a full day to extract the data; often, the system would crash during

report generation. With a week’s worth of billing documents totaling up to 109

million records, MLF needed a way to quickly extract this data from the system

and turn it into valuable business information.



The Resolution:

As MLF’s systems integrator, IBM partnered closely with SAP

to deliver the first SAP HANA go-live in Canada in December 2011. The pilot

implementation extracted profitability and trade spend data from ECC and CRM

into SAP HANA and presented this information in a user-friendly manner

through BOBJ Web Interface (WEBI). With the enhanced SAP HANA solution,

the same information that now takes 6-10 seconds to extract.

(28)

Implementation of SAP HANA at MLF (2)



The Business Details:

• This solution is an SAP Business Warehouse (BW) BEx Query and is

primarily used to extract invoice sales details from BW for analysis.

• The end-user reports are extremely flexible and user-friendly with over

90 dimensions to leverage for different views and visualization.

• The implementation took 15 people and roughly 8 weeks.



The Technical Details:

• Costing, trade, freight, and logistics data comes from ECC and CRM

through BW. This data is sourced from BW Data Store Objects (DSOs) and Cubes and goes through BW Open Hub and SAP HANA.

• The data goes through a Universe and eventually into BOBJ.

• Reports are static, tabular, and come through WEBI.

• The WEBI report is used to identify business rules to highlight anomalies

and help the end-users easily process the millions of records.

• This was based upon 21 reports that were identified for the

implementation.

MLF intends on continuing to partner with IBM and SAP to implement SAP HANA across many other Business Units and reporting areas.

Business Warehouse BW Open Hub SAP HANA Data Services Universe BOBJ/WEBI

(List Invoice and Key Figured)

(29)

(30)

From a business perspective…

– HANA enables faster access to data (near real-time)

– HANA enables access to detailed data (DSO reporting)

Performance Improvements

– Data loading  up to 30% faster

– Reporting (all reports; not just BWA)

A positive NPV project  lower development costs

– Support organization and project delivery

• LSA++ reduces maintenance burden:

(31)

31

References

Recent projects and proof of concepts (for internal use)

Use Case

 The recent acquisition of Carlton United Breweries

(CUB) by SABMiller has provided an opportunity for CUB to begin the transition to SABMiller operating practices which require the adoption of global reporting standards.

Solution

 IBM will be responsible for performing iterative capability builds in the SAP BI (Business Warehouse and Business Objects) on the SAP HANA in-memory database

platform.

Business Challenge

Benefit

 IBM will add value to CUB's business by providing a Business Intelligence technology and tools platform that is 100% aligned with the SABMiller application roadmap, self -service data mining capabilities and improved report performance with the SAP HANA database.

 CUB is challenged by existing legacy assets that are under performing with a relatively high cost of ownership and significant complexity across its financial reporting.

 Move key business intelligence reporting functions onto the latest SAP Business Warehouse (BW) and SAP Business Objects software running on the SAP HANA in-memory database platform.

(32)