SAP Big Data Markus Tempel SAP Big Data and Cloud Analytics Services
Is that Big Data? 2015 SAP AG or an SAP affiliate company. All rights reserved. 2
What if you could turn new signals from Big Data into business value? :-) Brand Sentiment 360 O Customer View Network Optimization Insider Threats Product Recommendation Personalized Care Predictive Maintenance Risk Mitigation, Real-time Propensity to Churn Real-time Demand/ Supply Forecast Asset Tracking Fraud Detection 2015 SAP AG or an SAP affiliate company. All rights reserved. 3
Primary Big Data Scenarios Machine Data Insight Omnichannel Data Insight M2M Sensors Unstructured Multi-media Real-time Mobile IoT Social Processes Assets Providing insight from machines, assets, and devices for better real-time decisions, predictions, and operational performance Enterprise Data Voluminous Data Geospatial Providing insight from high volume and high variety data for real-time analytics & actionable intelligence
SAP makes Big Data actionable Big Data Platform Big Data Analytics & Apps Big Data Science Real Time Real Value Real Results
HAVING DATA ISN T VALUABLE - USING IT IS! +70% of IT projects use more than 1 platform* * Operationalizing the Buzz: Big Data, An Enterprise Management Associations (EMA) Research Report 2015 SAP AG or an SAP affiliate company. All rights reserved. 6
Simplify IT With Flexible Platform for Big Data SAP HANA platform + Analytics + Applications and Hadoop / NoSQL Mobile applications and BI High Performance Applications Reporting & Dashboards Adhoc & OLAP Analytics Data Exploration & Visualization Predictive Analysis Application Development Environment STREAM PROCESSING ANALYTICS ENGINE TEXT ENGINE PREDICTIVE ENGINE GRAPH ENGINE SPATIAL PROCESSING In-Memory Column Storage Series Data Storage Dynamic Tiering Hadoop / NoSQL Data Lake Data model & data High performance analytics Store time-series data Aged data in Disk MapReduce / YARN HDFS HIVE Smart Data Streaming Smart Data Access 1010100 1010110 1001110 Smart Data Integration Smart Data Quality Stream Processing Virtual Tables User Defined Functions Transformations & Cleansing ERP Store & OLTP Geo Logs Text Social Machine forward Sensor 2015 SAP AG or an SAP affiliate company. All rights reserved. 7
SAP HANA Smart Data Access Virtual Table Capabilities Real-time, virtualized data access to external sources SAP Sources: HANA, ASE, IQ, MaxDB, ESP, SQLA Databases: Teradadata, Microsoft SQLServer, Oracle, IBM DB2, IBM Netezza Hadoop: Hive ODBC Driver to Cloudera, Hortonworks, MapR NoSQL: SPARK Benefits Optimized performance Compliments existing enterprise investments Lower development costs by using data directly from its source system 2015 SAP AG or an SAP affiliate company. All rights reserved. 8
SAP Predictive Analytics 3 Key Messages Easy Productive Big Data Data Warehouse Web & Social Media Time to Market Total Campaigns Next Big Thing? 2015 SAP AG or an SAP affiliate company. All rights reserved. 9
SAP Predictive Analytics Analysts disruptor in the Predictive Analytics Market Gartner focus on automating key modelling and analytical tasks is a blessing Forrester Market at $3+ billion in 2016, growing at 10% 17% CAGR customers liked SAP InfiniteInsight s model automation capabilities. In addition, SAP HANA customers reported that they have experienced the high speed that the system promises. Hurwitz low touch approach to Predictive will boom in popularity Forrester customers build Predictive models 3x faster Aberdeen Group 2015 SAP AG or an SAP affiliate company. All rights reserved. 10
Getting Value from Big Data 6 Steps to success Implement your use case Discover your data Conduct an initial evaluation Design your architecture Make Big Data a business Capability Define your use cases 2015 SAP AG or an SAP affiliate company. All rights reserved. 11
Thank you Markus Tempel Global Lead Big Data Analytics Practice markus.tempel@sap.com +4916090822005
Coming Next - 11:30 Theater 2 Walter Müllner Dr. Ingo Peter Demonstration der Data Mining Werkzeuge SAP Predictive Analytics und SAP HANA PAL Ein Wettstreit zwischen einem Vertreter aus einem Fachbereich und einem Data Scientist zur Lösung einer Aufgabenstellung mit unterschiedlichen Vorgehensmodellen und Tools