Unleash the Value of Big Data through Predictive Analytics with SAP HANA

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1 September 10-13, 2012 Orlando, Florida Unleash the Value of Big Data through Predictive Analytics with SAP HANA Philip Mugglestone SAP

2 Learning Points New market forces are changing the landscape and offering new opportunities Predictive analysis can be for everyone in the business SAP HANA is the cornerstone of SAP s vision for predictive analytics

3 Agenda The Predictive Analytics Landscape Predictive Analytics with SAP HANA Customers Benefit from Predictive Analytics with SAP HANA Predictive Applications for SAP HANA Demo 2012 SAP AG. All rights reserved. 3

5 What if... You could identify hidden revenue opportunities within your customer base through predictive analytics?... You could retain your high-value customers/employees/vendors/partners with the right retention offers?... Your call center agents could delight customers with the best next-step recommendations?... You could increase cross-sell and up-sell effectiveness through cross-channel coordination?... You could build long-term customer/employee/vendor/partner relationships with intelligent interactions? 2012 SAP AG. All rights reserved. 5

6 Extend Your Analytics Capabilities Sense & Respond Predict & Act COMPETIVE ADVANTAGE Raw Data Cleaned Data Standard Reports Generic Predictive Analytics Ad Hoc Reports & OLAP What happened? Predictive Modeling Optimization Why did it happen? What will happen? What is the best that could happen? ANALYTICS MATURITY The key is unlocking data to move decision making from sense & respond to predict & act 2012 SAP AG. All rights reserved. 6

7 What is Predictive Analysis? Predictive analysis encompasses a range of analytic techniques the exploration and analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns and rules. Gordon Linoff and Michael Berry Authors of Data Mining Techniques the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. Gartner Group 2012 SAP AG. All rights reserved. 7

8 Predictive Analytics Examples Forecasting How do historical sales, costs, key performance metrics, and so on, translate to future performance? How do predicted results compare with goals? What anomalies might exist and conversely what groupings or clusters might exist for specific analysis? Anomalies Challenges Key Influencers What are the main influencers of customer satisfaction, customer churn, employee turnover, and so on, that impact success? What are the correlations in the data? What are the cross-sell and up-sell opportunities? Relationships Trends What are the trends: historical / emerging, sudden step changes, unusual numeric values that impact the business? 2012 SAP AG. All rights reserved. 8

9 Changing Landscapes and New Opportunities Data mining and predictive have been around for decades. But new market forces are changing the landscape and offering new opportunities Increased business interest Now that BI users know what happened, they are asking why and what s likely to happen next Explosive demand from sales, marketing, and call center analyses fraud, and government intelligence/security agencies Increased data value (e.g., Big Data) Exploding data volume Expanding data varieties Increasing technology performance Parallel processing, faster CPUs, and inmemory technologies reduce time and cost of data processing 2012 SAP AG. All rights reserved. 9

10 Big data matters Transformational business value from data Drive Better Profit Margins Velocity Instant Messages Mobile CRM Data Customer Business Value Demand Things Sales Order Operational Efficiencies New Strategies and Business Models Transactions Volume Planning Opportunities Inventory Variety 2012 SAP AG. All rights reserved. 10

11 SAP Predictive Analytics Vision Database to Decision Predictive Analytics In-database predictive and R integration Intuitive predictive modeling Advanced data visualization and easy to use exploration For Everyone in the Business Seamlessly embedded in business user applications Extended into BI clients and reports Insight into events instantly delivered to dashboards, alerts, and mobile devices Harnessing Powerful Big Data Analytics Real-time on massive amounts of structured and unstructured data Complex questions answered in-memory, lightning fast Deep Hadoop integration with built-in text analysis 2012 SAP AG. All rights reserved. 11

12 Predictive Analysis - User Personas Number of users User personas 500 / 5,000 Embedding Predictive Analysis Industry applications LOB applications BI client tools Application End User Information Consumer Interactive Consumer 50 / 100 Large number SAP PA visualization SAP PA wizard All personas Business Analyst / Interactive Consumer 5 / 20 SAP PA designer SAP HANA PAL & R IQ Business Analyst Bi-directional Professional Data Analyst Predictive Analysis process designer / author 2012 SAP AG. All rights reserved. 12

14 Predictive Analytics with SAP HANA Transforming the Future with Insight Today Unleash the value of Big Data through the power of SAP HANA Employ in-database predictive algorithms Access 3,500+ open-source algorithms via R integration for SAP HANA Intuitively design and visualize complex predictive models SAP Predictive Analysis software Bring predictive insight to everyone in the business Embed within business applications Extend into BI and reports Insight into events instantly delivered to dashboards, alerts, and mobile devices 2012 SAP AG. All rights reserved. 14

15 SAP HANA In-Memory Predictive Analytics Combine the depth and power of in-memory analytics within SAP HANA with the breadth of R to support a variety of advanced analytic and predictive scenarios Predictive Analysis Library (PAL) Native predictive algorithms In-database processing for powerful and fast results Quicker implementations Support for K-Means, K-Nearest Neighbor, C4.5 Decision Tree, Multiple Linear Regression, Apriori, ABC Classification, Weighted Score Tables Y X Z R Integration for SAP HANA Enables the use of the R open source environment (> 3,500 packages) in the context of the HANA in-memory database R integration enabled via high performing parallelized connection R script is embedded within SAP HANA SQL Script 2012 SAP AG. All rights reserved. 15

16 SAP HANA In-Memory Predictive Analytics Predictive Analysis Library (PAL) Additional native predictive analysis functions provide for deeper insights and faster analytic implementations as well as results SAP HANA SPS3 Clustering K-Means Classification K-Nearest Neighbor C4.5 Decision Tree Multiple Linear Regression Association Apriori Classification ABC Classification Weighted Score Tables SAP HANA SPS4 Association Lite Apriori (A -> B) Classification Exponential / Geometric / Logistic / Natural Logarithmic Regression CHAID Analysis Time Series Single / Double / Triple exponential smoothing Preprocessing Outlier Detection (Inter-Quartile Range) The Predictive Analysis Library (PAL) is part of the Business Function Library It resides in the Calculation Engine and consists of functions executing at the database layer and is written in C SAP AG. All rights reserved. 16

17 R Integration for SAP HANA What is R? R is a software environment for statistical computing and graphics Open Source statistical programming language Over 3,500 add-on packages; ability to write your own functions Widely used for a variety of statistical methods More algorithms and packages than SAS + SPSS + Statistica Who s 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 do they use it? Free, comprehensive, and many learn it at college/university Offers rich library of statistical and graphical packages 2012 SAP AG. All rights reserved. 17

19 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 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 2012 SAP AG. All rights reserved. 19

20 Predictive Analytics Process in SAP HANA 1. Visualize the model for better understanding 2. Store the model and results in SAP HANA Step1 Data Loading 1. Understand the business and figure out the problems 2. Load the SAP or non-sap data into SAP HANA Step 4 Visualization SAP HANA Step 2 Data Pre-Processing 1. Train Predictive Model (clustering / classification / association / time series etc.) 2. Predict based on suitable model Step 3 Data Mining 1. Data selection 2. Data cleansing 3. Data transformation 2012 SAP AG. All rights reserved. 20

22 Mitsui Knowledge Industry Healthcare Speed Research & Improve Patient Support 408,000x faster than traditional diskbased systems in a technical PoC 216x faster by reducing genome analysis from several days to only 20 minutes making real-time cancer/drug screening possible Business Challenges Reduce delays and minimize the costs associated with new drug discovery by optimizing the process for genome analysis Improve and speed decision making for hospitals which conduct cancer detection based on DNA sequence matching Technical Implementation Leveraged the combination of SAP HANA, R, and Hadoop to store, pre-process, compute, and analyze huge amounts of data Provide access to breadth of predictive analytics libraries Benefits For pharmaceutical companies, provide required new drugs on time and aid identification of driver mutation for new drug targets Able to provide a one stop service including genomic data analysis of cancer patients to support personalized patient therapeutics Our solution is to incorporate SAP HANA along with Hadoop and R to create a single real-time big data platform. With this we have found a way to shorten the genome analysis time from several days down to only 20 minutes. Yukihisa Kato, CTO and Director of MITSUI KNOWLEDGE INDUSTRY 2012 SAP AG. All rights reserved. 22

23 Cisco Systems, Inc. High-Tech Deliver Predictive Insight in Near Real-Time 5 seconds Seasonality analysis with any selected filters including country, segments and sales levels 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 2 seconds Cluster analysis across multiple quarters for a combination of sales level and quarter to determine sales achievement level 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 Technical Challenges Unable to transform the 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 2012 SAP AG. All rights reserved. 23

24 Bigpoint Gaming Industry - Predictive Game Player Behavior Analysis 5,000 events per second loaded onto SAP HANA (not possible before) 10-30% increase in revenue per year Interactive data analysis leading to improved design thinking and game planning Business Challenges Increase conversion rates from free paying player Increase the average revenue per paying player Decrease churn keep paying players playing longer Technical Challenges Leverage real-time data processing in SAP HANA and classification algorithms with R integration for SAP HANA to deliver personalized context-relevant offers to players Analyze vast amounts of historical and transactional data to forecast player behavior patterns Benefits Real-time insights Per player profitability analysis and increased understanding of player behavior Increase data volume and processing capabilities to communicate personalized messages to players At Bigpoint in the Battlestar Galactica online game, we have more than 5,000 events in the game per second which we have to load in SAP HANA environment and to work on it to create an individualized game environment to create offers for them. In this co-innovation project with SAP HANA, using Real Time Offer Management Bigpoint, we hope to increase revenue by 10-30%. Claus Wagner, Senior Vice President SAP Technology, Bigpoint 2012 SAP AG. All rights reserved. 24

26 SAP HANA Predictive Ecosystem SAP 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 2012 SAP AG. All rights reserved. 26

27 SAP HANA Hadoop Support in SAP Data Services 4.1 Extend the Big Data Ecosystem to SAP HANA Files SAP HANA, Sybase IQ, other Target Systems Databases SAP Data Services SAP Data Services Web & Others SAP HANA can integrate with Hadoop via SAP Data Services 4.1 Ability to load and read large volumes of data into and out of SAP HANA from/to Hadoop via SAP Data Services 4.1 Reading from and loading into Hadoop Hive Support (table-like access to Hadoop data) Direct HSFS file support through PIG scripts 2012 SAP AG. All rights reserved. 27

29 Smart Meter Analytics Identify energy consumption pattern that can be used for Customer Segmentation Smart meter data volume is huge Using SAP HANA + PAL K- Means algorithm Clustering of smart meter data applying the k-means clustering to >20 million x 96-dimensional vectors High performance clustering computation Challenge Solution Results 2012 SAP AG. All rights reserved. 29

30 Customer Analytics Customer Revenue Performance Management What: Combination of financial and sales data to get visibility of profitability and margins of a customer, a customer deal or group Margin visibility decomposition of margin to highlight areas of improvements Comprehensive customer revenue analysis Call for action take focus customers and define initiatives to improve sales success Simulation and Business Advice Perform what-if scenarios to see the impact of pricing and discount changes Simulations in real time to provide advice on cross/up sell and product bundling Predictions on customer behaviour and profitability for more effective segmentation and targeting 2012 SAP AG. All rights reserved. 30

31 Customer Analytics Predictive Customer Segmentation & Targeting CRPM? SEGMENTATION ALERTS SIMULATE FEED SEARCH PERSONALIZE ADMINISTRATOR OVERVIEW SEGMENT All customers with revenue greater than 55 milions ATTRIBUTES Attribute Sales Group 1 Attribute Country 2 Attribute Quarter 3 Customer Product Group Product Sales Area Attribute Sales Group 4 Attribute Customer 5 Attribute 6 Attribute SearchResults: All customers with revenue greater than 55 milions Customer Class A Customer Product Group (0/6) (0/7) Product Sales Area(1/8) Class PGroup 1 Status PGroup 2 Label PGroup 1 3 Label PGroup 2 4 Label PGroup 3 5 Label PGroup 4 6 Label PGroup 5 7 Group Distribution 1 Chain Group Area 2 Group Area 3 Type Area 4 Group Area 45 Group Area 56 Group Area 67 Customer Class Revenue Margin Purchasing Bu... Country Region Customer N... A $ % $ USA Wash... Customer N... A $ % $ USA Wash... Customer N... A $ % $ USA Wash... Customer N... A $ % $ USA Wash... Customer N... A $ % $ USA Wash... Customer N... A $ % $ USA Wash... Type Distribution (2/5) Chain (2/5) Explore Save Query Type Sales 1 Org Type DChain 2 (0/15) 2 (0/15) Type DChain 3 (1/45) 3 (1/45) Type DChain 2 2 Subtype DChain 2.1 Subtype DChain 3.1 Type DChain 3 3 Subtype DChain Subtype DChain 3.2 Type DChain 4 4 Subtype DChain 2.3 Subtype DChain 3.3 Type DChain5 5 Subtype DChain 2.4 Subtype DChain 3.4 Type DChain6 6 Subtype DChain 2.5 Subtype DChain 3.5 Some Meaningful Label 30 % All Manufacture Customers 20% 10% Subtype DChain 3.2 (4) What: Segmentation on a wider and more granular set of data not requiring SAP CRM Tracking of successful implementation of initiatives out of segmentation All levels of sales can use segmentation features to define own target customers to improve sales Predictive segmentation by identifying common characteristics within a given group Customer N... A $ % $ USA Wash... Customer N... A $ % $ USA Wash... Customer N... A $ % $ USA Wash ,000 M Class A Class B Class C Real-time simulation and segment building More granular targeting leveraging predictive analytics Improved alignment amongst Sales, Marketing and Product Development to focus on target Customers 2012 SAP AG. All rights reserved. 31

32 Performance and Insight Optimization Services Enabling complex use cases with SAP HANA Examples of innovative business solutions advanced by SAP HANA: Tax Compliance and discovery engine for public sector Industry-specific optimization Algorithms and processes Affinity insight and forecasting for retail Industry-specific predictive modeling Proprietary models for retail, banking, utilities, manufacturing, and beyond High performance compliance engine for chemical companies Back end Sophisticated aggregation and cleansing algorithms High volume industry specific data relationship algorithms SAP HANA 2012 SAP AG. All rights reserved. 32

33 Startups Powered by SAP HANA Predictive and Analytics / Security Geospatial & Visual Analytics Predictive and Analytics / Financial Social and Predictive Analytics AlertEnterprise Security Convergence solutions combine Situational Intelligence with Command and Control capabilities for Critical Infrastructure Protection. Next generation geospatial and visual analytics solutions transform massive volumes of real-time disparate data into intuitive visual displays. Space-Time Insight s situational intelligence software helps visualize operations with absolute clarity, analyze the cause of a problem and determine how to prevent another occurrence, and act instantly to address any situation. The Taulia Invoicement Suite includes a Dynamic Discounting module and self-service vendor portal. Together, these tools reduce Accounts Payable support overhead, improve supplier relations and generate significant cash savings for corporations. From campaigns to communities, #NextPrinciples empowers anyone to monitor, manage and act on social media interactions across channels SAP AG. All rights reserved. 33

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36 Best Practices Companies can no longer focus solely on delivering the best product or service. To succeed, they must: Uncover hidden customer / employee / vendor / partner trends and insights Anticipate behavior and take proactive action Empower your team with intelligent next steps to exceed customer expectations Create new offers to increase market share and profitability Develop and execute a customer-centric strategy Target the right offers to the right customers through the best channels at the most opportune time

37 Key Learnings New market forces are changing the landscape and offering new opportunities Predictive analysis can be for everyone in the business SAP HANA is the cornerstone of SAP s vision for predictive analytics

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