Big Data Mining with SAP HANA Reinventing Businesses through Innovation, Value & Simplicity

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1 Big Data Mining with SAP HANA Reinventing Businesses through Innovation, Value & Simplicity Dr Asadul Islam Senior Researcher Strategic Customer Engagement, Product & Innovation

2 Innovate with speed SAP Strategic Customer Engagement team Established in We are a highly focused, global team of engineers, designers and business experts specializing in In-memory Big Data Analytics (on SAP HANA). We co-innovate with our customers to develop and design new solutions with the potential to transform their business. Deep Expertise Development Support Speed & Innovation Close Partnership Most experienced Direct access to our Global Big Data Experts and Design Thinking consultants at SAP In-memory Big Data Platform Development team and high sponsorship support. Agile approach to drive innovation and rapid solutions in complex business scenarios. Sitting side by side, building solutions with our customer SAP AG. All rights reserved. 2

3 Agenda The Existing Data Management Conundrum SAP HANA Real-Time Analytics & Application Innovations SAP HANA handles Big Data SAP HANA Transformational Impact at Customers 2013 SAP AG. All rights reserved. 3

4 Agenda The Existing Data Management Conundrum SAP HANA Real-Time Analytics & Application Innovations SAP HANA handles Big Data SAP HANA Transformational Impact at Customers 2013 SAP AG. All rights reserved. 4

5 The future holds many transformational opportunities Capitalize on the new technology frontier Retail: From transactions to 1:1 engaging relationships Manufacturing: From mass production to custom 3-D printing Healthcare: From generic treatments to personalized medicine 2013 SAP AG. All rights reserved. 5

6 Information Processing is at a critical inflection point Point optimization is not enough to meet the new frontiers of real-time business IMPACT ON BUSINESS Slow Response Times Usability Challenges Lack Of Adaptability Real-time Business Scenario Sales Real-time bonus calculations for consumers Customer Service Customer overdue credit calculation by product areas Finance and Operations Iterative period end closing with new posting into accounts constantly Manufacturing New ATP strategies; MRP run for individual ATP check/instant re-planning Planning Predict Order Processing Operational Reporting Real-time Risk & Fraud Trend Analysis Sentiment Analytics Predictive Analytics Pattern Recognition Location Intelligence Monitor Communicate Analyze Summarize Aggregate ETL Staging Clean-Data Quality 1 Collect Transact 0 Transactional Datastore Data Warehouse Sensors Data Mobile Data Archives Social & Text Geo-Spatial IMPACT ON IT High Latency Complexity High Cost of Solutions 2013 SAP AG. All rights reserved. 6

7 Technology today requires tradeoff A breakthrough in today s information processing architecture is needed DEEP Complex & interactive questions on granular data OR DEEP Complex & interactive questions on granular data HIGH SPEED Fast response-time, interactivity BROAD Big data, many data types HIGH SPEED Fast response-time, interactivity SIMPLE No data preparation, no pre-aggregates, no tuning REAL -TIME Recent data, preferably realtime SIMPLE No data preparation, no pre-aggregates, no tuning 2013 SAP AG. All rights reserved. 7

8 Next-generation Software & Hardware Architecture Removing data processing bottlenecks using latest innovations in computing CPU Multi-Core Architecture 8 CPU x 10 Cores per blade Massive parallel scaling with many blades 64bit address space 1 TB in current servers Dramatic decline in price/performance L3 Cache L3 Cache L3 Cache L3 Cache L3 Cache L3 Cache L3 Cache L3 Cache MEMORY OLTP+OLAP in column Store Partitioning Insert Only on Delta No Aggregate tables (Dynamic Aggregation) Compression Logging and Backup STORAGE Solid State Flash HDD 2013 SAP AG. All rights reserved. 8

9 Re-think Data Management with in-memory computing Need to eliminate redundant data copies, materialization and models A Common Database Approach for OLTP and OLAP Using an In-Memory Columnar Database Hasso Plattner Separated Transactions + Analysis + Acceleration processes One in-memory atomic copy of data for Transactions + Analysis ETL ETL Cache VS SAP HANA (DRAM) Transact Analyze Accelerate 3 copies of data in different data models Inherent data latency Poor innovation leading to wastage Eliminate unnecessary complexity and latency Less hardware to manage Accelerate through innovation and simplification 2013 SAP AG. All rights reserved. 9

10 SAP HANA Platform More than just a database Any Apps Any App Server SAP Business Suite and BW ABAP App Server SQL MDX R JSON Open Connectivity SAP HANA Platform Extended Application Services Supports any Device Application Development Process Orchestration App Server UI Integration Services Web Server Processing Engine OLTP OLAP Search Text Analysis Predictive Events Spatial Rules Planning Calculators Database Services Application Function Libraries & Data Models Predictive Analysis Libraries Business Function Libraries Data Models & Stored Procedures Unified Administration Life-cycle Management Security Integration Services Data Virtualization Replication ETL/ELT Mobile Synch Streaming Deployment: On-Premise Hybrid On-Demand SAP HANA platform converges Database, Data Processing and Application Platform capabilities & provides Libraries for predictive, planning, text, spatial, and business analytics so businesses can operate in real-time SAP AG. All rights reserved. Public 10

11 Breakthrough Data & Application Processing Enabling real-time computing design patterns across entire software architecture SIMPLIFIED OLTP + OLAP in Columnar database CONVERGED End-to-end Data Processing OPTIMIZED Application Processing SAP HANA (Main Memory) Operational Analytics Predictive Machine Learning Prescriptive Sentiment Intelligence SAP HANA (Main Memory) Libraries SAP HANA (Main Memory) In-Memory Database layer Application Layer Sensors Transactions Spatial/GIS Database & data processing engines Application Server Image Text Integration Services Development, Deployment and Administration 2013 SAP AG. All rights reserved. 11

12 Agenda The Existing Data Management Conundrum SAP HANA Real-Time Analytics & Application Innovations SAP HANA handles Big Data SAP HANA Transformation al Impact at Customers 2013 SAP AG. All rights reserved. 12

13 Renovate existing systems while enabling future breakthroughs SAP BusinessSuite SAP BusinessOne StartUps & ISV Apps 40+ HANA Apps, Accelerators & RDS Operational Datamarts Enterprise Data Warehouse & BW on HANA REAL-TIME APPLICATIONS REAL-TIME ANALYTICS Consumer Engagement Sense & Respond Planning & Optimization Operational Analytics Big Data Warehousing Predictive, Spatial & Text Analytics SAP SAP HANA PLATFORM Platform Extended Application Services Development Processing Engine Database Services Application Function Libraries & Data Models Administration Integration Services Deployment: On-Premise Hybrid On-Demand 2013 SAP AG. All rights reserved. 13

14 Consumer Engagement Applications Game-changing innovation with new applications and business models Generate real-time consumer insights Deliver personalized, engaging experiences Real-time Engagement for consumers SAP HANA PLATFORM Real-time insights for enterprise Create more responsive business with precision targeting HTML-5 support Mobile integration Consumer-grade usability OLTP + OLAP real-time Processing Sentiment Intelligence Predictive analytics 2013 SAP AG. All rights reserved. 14

15 Sense & Respond Applications for Internet of Things Faster execution by adjusting to signals in the business environment Detect and analyze data trends by aggregating sensor data Connected Cars Smart Equipment Smart Logistics Benefit from more energy efficient logistics, transforming retail distribution Increase quality of life through intelligent buildings, robots, cars and cities Smart Automobile Smart House Smart Vending Smart Cities Event stream processing (ESP) No data preparation RFID Integration Embedded data processing Machine Learning Spatial Processing 2013 SAP AG. All rights reserved. 15

16 Planning and Optimization Applications Faster execution to adjust to changes in the business Sales and Operations Planning Enable iterative period end closing with continuous posting into accounts Cash forecasting management Optimize procurement, manufacturing, transportation without limits Business Planning and Consolidation In-memory stored procedures Integrated Planning and Calculation engines Predictive Analytics and Business Functions Recent data, preferably real-time Supporting complex questions on interactive data Built-in Spatial Processing 2013 SAP AG. All rights reserved. 16

17 Operational ad-hoc analytics and monitoring Real-time insight into the in-the-moment business situations Customer Service Finance and Operations Account Administration Accelerate business decisions Provides vision across entire business process Empower front-line employees Provide POS data analysis as interactive session with drill-down information SAP HANA (DRAM) Implement real-time fraud detection, risk management and monitoring Pricing Accounting Channel Inventory Customers Forecasting Planning Products Suppliers OLTP + OLAP Processing SAP HANA Live SAP smart data access data virtualization Real-time data integration (Replication, Streaming) Unified data modeling Single-query access to data 2013 SAP AG. All rights reserved. 17

18 Agenda The Existing Data Management Conundrum SAP HANA Real-Time Analytics & Application Innovations SAP HANA handles Big Data SAP HANA Transformation al Impact at Customers 2013 SAP AG. All rights reserved. 18

19 The Reality of the Internet of Everything 1 billion Facebook users Data doubles every 18 months 5 billion in emerging middle class 4 billion YouTube views per day 15 billion web-enabled devices 2013 SAP AG. All rights reserved. 19

20 Unlocking the value of Dark Data Use Analytics Today 10% 75% Need Analytics by 2020 Missing new insights Not utilizing all the information out there = IT is not agile enough and the business wants to get involved Ability to manage and consume all data is getting harder Not leveraging the power of collective insight 2013 SAP AG. All rights reserved. 20

21 How analytics need to evolve to deliver collective insights End-to-end User Engagement Raw Data Cleaned Data Standard Reports Ad Hoc Reports & OLAP Self Service BI Generic Predictive Agile Analysis Visualizati on What happened? Optimization Predictive Modeling Why did it happen? What is the best that could happen? What will happen? Maturity of Analytics Capabilities Collective Insight Easy adoption Fast implementation Business focused Enable storytelling 2013 SAP AG. All rights reserved. 21

22 Sense and respond are no longer enough Severe analytics skills shortage Challenging to detect meaningful signals in big data Difficult to apply predictive algorithms to anticipate business trends 50-60% shortfall for experienced data analysts Dun & Bradstreet and McKinsey Global Institute analysis 68% of organizations that used predictive realized a competitive advantage 2013 SAP AG. All rights reserved. 22

23 Predictive Analytics & Machine Learning Transforming the Future with Insight Today Provide Business Analysts with sophisticated algorithms to take the next step in understanding their business and modeling outcomes. Perform statistical analysis on your data to understand trends and detect outliers in your business. Build models and apply to scenarios to forecast potential future outcomes Combine, manipulate and enrich data to apply it to your business scenarios. Self-service visualizations and analytics to tell your story OLTP + OLAP Processing SAP HANA Live SAP smart data access data virtualization Real-time data integration (Replication, Streaming) Unified data modeling Single-query access to data 2013 SAP AG. All rights reserved. 23

24 Database & Data Processing Services Simplified & Optimized SAP HANA PLATFORM SAP HANA PLATFORM Application Services Fully ACID compliant, Inmemory, columnar, massively parallel processing database platform Deployment Services Event Processing OLTP + OLAP MPP Processing Engine Database Services SIMD CPU Cache Aware Planning Calculation Predictive Text Mining X86 Commodity HW Shared Nothing Administration Services In-memory stored procedures and Data virtualization with smart data access Integrated data processing for end to end analytic processing Scan 3.2 billion billion integer/sec/core Rules Search Predictive Machine Learning SpacialGIS Business Fuction Libraries 12.5 million aggregates/sec /core Integration Services Ingest 1.5 million records/sec/node Deployment Service OnDemand Hybrid OnPremise 2013 SAP AG. All rights reserved. 24

25 Top Application Platform Services Migrate Existing or Build New Application to Process Any Data Browser / Mobile Custom App Web App Server SQL MDX ODBO JSON Easily migrate your applications. Deploy App without Re-Write ADBC ODBC JDBC XML/A Process any Data within same query with ONE SQLScript Structured data Click stream Social network Customer Data Point of Sale RFID Machine Data Connected Vehicles Text Data Geospatial Data Smart Meter Mobile R-scripts SAP HANA APP Services (Web Server) SQL Script Unstructured Server JS Lib Application Function Library Build new web applications with any open source HTML5 / JS libraries, server-side Java script 2013 SAP AG. All rights reserved. 25

26 Top Comprehensive Data Provisioning Real-time high volume data integration from any source Any Source Data Movement Over Networks Transform and Persist Data SAP Business Suite Trigger-Based SAP LT Replication Server ODBC Non-SAP Data Sources Log-Based SAP Sybase Replication Server SAP HANA Cloud Deployments ETL, Batch SAP Data Services Virtual Tables Complex Event Data Source Event Streams SAP Sybase Event Stream Processor Network Devices Wired / Wireless Data Synchronization SAP Sybase SQL Anywhere Data Sources (SP6: HANA, IQ, ASE, Hadoop, Teradata) Data Virtualization SAP HANA Smart Data Access 2013 SAP AG. All rights reserved. 26

27 Mission-critical infrastructure Ensuring most demanding service-levels Campus cluster Geo clusters Metro cluster Single Server 2 CPU 128GB to 8 CPU 1TB (Special layout for Suite on HANA for up to 4 TB per host) Single SAP HANA deployments for data marts or accelerators Support for high availability and disaster recovery Scale Out Cluster 2 to n servers per cluster Each server is either 4 CPU/512GB or 8 CPU/1TB Largest certified configuration: 56 servers Largest tested configuration: 100+ servers Support for high availability and disaster recovery 2013 SAP AG. All rights reserved. 27

28 Predictive Analysis Library (PAL) 2013 SAP AG. All rights reserved. 28

29 Predictive Ecosystem SAP BusinessObjects Predictive Analysis SAP and Custom Applications Business Intelligence Clients SAP HANA Platform SAP HANA Studio Predictive Analysis Library (PAL) R Integration for SAP HANA R Data Pre-Processing and Loading SAP Data Services, Information Composer, SLT, DXC, Hadoop 2013 SAP AG. All rights reserved. 29

30 R integration R is a software environment for statistical computing and graphics Open Source, programming language plus a run-time environment Over 3,500 add-on packages; ability to write your own functions Widely used for a variety of statistical methods: linear and non-linear models, statistical tests, time series analyses, classification and clustering, predictive, etc. More algorithms and packages than SAS + SPSS + Statistica Who is using it? Growing number of data analysts in industry, government, consulting, and academia Cross-industry use: high-tech, retail, manufacturing, CPG, financial services, banking, telecom, etc. Why are they using it? Free, comprehensive, and many learn it at college/university Offers rich library of statistical and graphical packages 2013 SAP AG. All rights reserved. 30

31 Introduction to R Adoption by Market 2013 SAP AG. All rights reserved. 31

32 R Integration for SAP HANA Functionality Overview R integration for SAP HANA enables the use of the R open source environment in the context of the HANA in-memory database Establishes a communication channel between HANA and R for fast data exchange Improved data exchange mechanism supports transfer of intermediate database tables directly into vector oriented data structures of R. Performance advantage over standard tuple-based SQL interfaces with no need for data duplication on the R server. Embedded scenario: Supported by providing the R-operator as a custom operator in the calcmodel of the calculation engine. This allows the application user to embed R script within SQL script and submit entire query to the HANA database. As the plan execution reaches R-node, a separate R runtime is invoked using Rserve and input tables of R node passed to R process using improved data transfer mechanism SAP AG. All rights reserved. 32

33 R Integration for SAP HANA Embedded Scenario Embedding R scripts within the SAP HANA database execution Enhancements are made to the SAP HANA database to allow R code (RLANG) to be processed as part of the overall query execution plan This scenario is suitable when the modeling and consumption environment sits on HANA and the R environment is used for specific statistical functions Send data and R script Run the R scripts Get back the result from R to SAP HANA 2013 SAP AG. All rights reserved. 33

34 Agenda The Existing Data Management Conundrum SAP HANA Real-Time Analytics & Application Innovations SAP HANA handles Big Data SAP HANA Transformation al Impact at Customers 2013 SAP AG. All rights reserved. 34

35 Values previously unattainable Cancer cell genomic analysis at Mitsui Knowledge Industry Streamlining the process of providing personalized cancer drug recommendation 2013 SAP AG. All rights reserved. 35

36 Mitsui Knowledge Industry Healthcare industry Cancer cell genomic analysis 408,000x faster than traditional diskbased systems in technical PoC 216x faster DNA analysis results - from 2-3 days to 20 minutes Product: Real-time Big data (R+Hadoop+HANA) Business Challenges Improve decision making for hospitals which conduct cancer detection based on DNA sequence matching Reduce delays and minimize the costs associated with new drug discovery by optimizing the process for genome analysis Technical Challenges Existing R and Hadoop architecture is too limited to support big data Data deluge slows down genome interpretation Benefits Reduce the time it takes to detect variant DNA and support personalized patient therapeutics SAP HANA provides in-memory predictive acceleration & correlated analysis For pharmaceutical companies, provide required new drugs on time and identify driver mutation for new drug targets quickly Genomic DNA analysis in real-time will transform how we enable comprehensive patient care to fight against cancer. SAP HANA will be the mission critical and reliable data platform to make real-time cancer analytics into a reality. Separately, our internal technical comparison demonstrated that SAP HANA outperforms a traditional disk-based system by factor of 408,000 when performing other types of data analysis. Yukihisa Kato, Director & Executive Officer, CTO, Research and Development Center, MITSUI KNOWLEDGE INDUSTRY CO.,LTD SAP AG. All rights reserved. 36

37 Innovation previously unfeasible T-Mobile USA, Inc. Measure effectiveness of marketing campaigns real-time Enable targeted micro-offers in real time for optimum retention Real-time decisions to maximize revenue by channel Refine sales offers for increased adoption and profitability Quickly fine tune campaigns to reduce churn and increase profitability 2013 SAP AG. All rights reserved. 37

38 T-Mobile USA, Inc. Telecom Measure effectiveness of Marketing Campaigns $10-25 savings per win-back subscriber - potentially billions of additional revenue per year 56x faster insights performing churn analysis Product: Agile Datamart Business Challenges Absence of rapid insights lead to inefficiencies and potential revenue loss Proliferation of offers/micro-offers makes offer performance management increasingly strategic High acquisition costs and intense competition in a saturated market Technical Challenges Big data cannot be analyzed efficiently and accurately 2 Billion+ records for 21M customers analyzed in 3 hours (Vs. 1 week earlier) Benefit Enable targeted micro-offers in real time Real-time business decisions to maximize revenue by channel Refine sales offers leading to increased adoption rate and profitability from their customers SAP HANA is enterprise ready and can handle information from multiple sources at T-Mobile for targeted offers to more than 21 million customers via channels such as retail stores, customer care centers and eventually SMS. Since marketing campaigns are frequent and precise to a segment, it requires rock solid performance in addition to speed of analysis with SAP HANA to provide offers on the fly for improved customer adoption, profitability and retention. Jeff Wiggin, VP Entreprise Information Technology, T-Mobile USA, Inc. Based on the rapid analytics that we re performing on SAP HANA, we are now able to quickly fine tune our current and future campaigns to improve the customer adoption rate, reduce churn and increase profitability. Alison Bessho, Director, Enterprise Systems Business Solutions, T-Mobile USA 2013 SAP AG. All rights reserved. 38

39 Simplicity previously unachievable ebay Early Signal Detection System powered by Predictive Analytics Automated signal detection system increasing accuracy of forecasts

40 Simplicity previously unachievable Charité Berlin Analyze terabytes of data in seconds Improve cancer treatment with new patient therapies 1,000x faster tumor data analysis (in seconds) Real-time analysis of 300M patient entries across departments and geographies Reduced time in staff shift changes Personalized healthcare for cancer patients 2013 SAP AG. All rights reserved. 40

41 Charité Berlin Healthcare industry Personalized healthcare for cancer patients 1,000x faster tumor data analyzed in seconds instead of hours Doctors and researchers can access all data through an ipad app while visiting patients in the hospital Product: Agile Datamart Business Challenges Improve cancer treatment with new patient therapies Reduce costs due to slow reporting Better negotiations with health insurance companies Technical Challenges Big, unstructured data: more than 500k data points or 2 TB per patient; more than 30% increase in recent years Full transparency of financial, clinical and research data 2-10 seconds for report execution Benefit Real-time analysis to 300M patient entries across various departments and geographies Faster, more flexible reporting helps reduce time in staff shift changes, saving dollars Today we analyze terabytes of data in a matter of seconds. This is of great advantage to both the executive management as well as to the doctors and patients. Martin Peuker, Vice CIO, Charité University Hospital Berlin 2013 SAP AG. All rights reserved. 41

42 Value previously unattainable Automotive Resources International (ARI) Improving customer service Increased performance of contact centers Maintained high service levels and reduced cost-to-serve customers 5% reduction in total overhead expense Make accurate predictions and provide foundation for new products 2013 SAP AG. All rights reserved. 42

43 Automotive Resources International (ARI) Automotive Industry Help ARI help its customers 5% cost reduction in total overhead expense Product: Agile Datamart Business Challenges Maintain high service levels while reducing cost-to-serve customers Provide differentiating value to customers with complex fleet services 3 seconds to run year-end activity report that couldn t be delivered before infinite improvement Technical Challenges Ocean of data produced with volume doubling every 14 months Complexity of fleets with 14k data points and infinite combinations Amount of data and speed required made it impossible to effectively analyze data at reasonable cost 3 weeks implementation Benefit Increased performance of contact centers (speed of answer, time to respond, first call resolution) Provided new self-service access for customers to directly get the information needed at significantly reduced cost for ARI We are taking data from a myriad of sources, from parts dealers, firms called upfitters that enhance vehicles, repair shops, gas stations, and so on. SAP HANA helps us combine all of that into a form that supports decisions, allows for exploration and diagnosis, helps make predictions, and provides the foundation for new products. Steve Haindl, Senior VP and CIO, ARI 2013 SAP AG. All rights reserved. 43

44 Data Access 7 billion records with data from telemetry recordings, services systems, other systems. < 1 second with query of 7 billion records 8 Weeks Implementation Product Solution Asset utilization Schedule variance Fuel(cost) insight Business Challenges Operational systems are comprised of disparate heterogeneous systems. Fleet operators and supervisors only have a reactive and ad hoc ability to investigate and understand only a fraction of potential issues within Aurizon operations. Technical Challenges Data used by one operational unit are mainly used within that unit and not shared by other units that may benefit from it. Benefits Better manage the 650+ locomotives within the Aurizon business Assist with improving locomotive fuel usage which is one of the largest costs to the business Present cases to various below rail providers on network delays to initiate network improvements Enable Aurizon to identify the areas that where cost efficiencies can be achieved Will be able to track the progress of the identified cost-saving measures From our perspective, this is great stuff. It is good to see it. So visual so easy instead of spending days and days churning data to get the results. To be honest we do understand the risk that we face in the current environment without having this capability. -- Louise Collins, SVP Operation 2013 SAP AG. All rights reserved. 44

45 Value previously unattainable Cisco Systems, Inc. Predictive insights into supply chain in near real-time Provide Cisco sales with real-time insights to make better decisions Better respond to customers and deliver tangible business value Drive higher profitability and growth Deliver information just-in-time as the business is changing 2013 SAP AG. All rights reserved. 45

46 Cisco Systems, Inc. High-Tech Industry Predictive Insights into Supply Chain in Near Real-Time 5 seconds seasonality analysis with any selected filters including country, segments and sales levels 2 seconds Cluster analysis across multiple quarters for a combination of sales level and quarter to determine sales achievement level Product: Predictive Analytics (R + SAP HANA) Business Challenges Provide Cisco sales with real-time insights to make better decisions Better data delivery decisions to improve the business for optimization and to drive topline growth Lack of understanding of underlying sales performance drivers and the seasonality of buying patterns Technical Challenges Inability to transform results of statistical analysis into actionable insights Benefits Better respond to customers and deliver tangible business value Reduce time to transform information into insights and improvement in the quality of decision-making on those insights Ultimately drive higher profitability and growth The HANA platform at Cisco has been used to deliver near real-time insights to our execs, and the integration with R will allow us to combine the predictive algorithms in R with this near-real-time data from HANA. The net impact is that we will be able to take the capability which takes weeks and months to put together, and deliver just-in-time as the business is changing. Piyush Bhargava, Distinguished Engineer IT, Cisco Systems 2013 SAP AG. All rights reserved. 46

47 Simplicity previously unachievable NongFu Spring Sales reporting Real-time decisions in line with long-term goals Improved efficiency and reduced costs Real-time shipping fee calculation and sales order analysis Better decision-making with accurate, real-time data 2013 SAP AG. All rights reserved. 47

48 NongFu Spring CPG Industry (Bottled water and beverages) Sales Reporting >2,300x faster to produce freight settlement report Product: Agile Datamart, BW on HANA Business Challenges Poor decision making due to slow speed and lack of accurate data Slower response time to competition, growing business needs 5-10x reduction in BW query response Technical Challenges Big data: takes more than a day to organize huge amounts of point-of-sale (POS) data Lack of coordination between sales, marketing, production, and logistics 33% reduction in database size Benefit Real-time decisions regarding the company s long-term development, improving efficiency and lowering costs Real-time shipping fee calculation and sales order analysis We are so satisfied with the operation and speed of the entire BW on HANA implementation. According to end users' feedback, the speed of their standard BW reports is very fast and has been improved greatly. In addition, the running of the whole production system is stable with fast speed of the data extract, ETL, and incremental updates. CIO NongFu Springs 2013 SAP AG. All rights reserved. 48

49 Value previously unattainable Usha International Ltd. Domestic appliances Increased sales through faster analysis of secondary sales data Real-time decision-making for better inventory management Enter new markets by analyzing secondary sales data and market research reports Increasing supply chain efficiency 2013 SAP AG. All rights reserved. 49

50 Usha International Ltd. Consumer Durables Domestic Appliances 60-80x faster query performance improvement 9:1 data compression factor Product: BW on HANA Business Challenges Due to report-generation time-lag, actual sales and supply-chain performance could not be tracked Poor decision making due to lack of fast analysis Technical Challenges Slow response to the large data analysis query Exponentially increasing data volumes affecting supply chain efficiency 10x faster data load Benefit Sales up-lift enabled by faster analysis of secondary sales data Real-time decision-making for better inventory management Enter new markets by analyzing secondary sales data and market research reports Business users are very thrilled with the results. They are able to run reports faster, and more importantly, productively utilize their time for analysis and better decision making. Our IT department is also very happy as this project has achieved 3 things in one go: achieve a smaller DB footprint for the same size of a BW deployment; faster data loads making more current data available to business; faster reporting and analysis making business users happy. We are now planning to migrate to BPC HANA. Customer is also planning to upgrade to SAP BusinessObjects BI 4.0. Subodh Dubey, Group CIO, USHA 2013 SAP AG. All rights reserved. 50

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