1. The World Moves Fast, and Data is Driving: Big Data and Analytics @Frost_Sullivan - #GILSV September 2013 | Silicon Valley @Frost_Sullivan - #GILSV September 2013 | Silicon Valley Jeff Cotrupe Global Program Director Big Data & Analytics (BDA) Stratecast | Frost & Sullivan

2. Three Key Takeaways 1.An understanding of the structure (or UN-structure) of Big Data, and where you need to look to be sure you’re capturing it all 2.A blueprint for the bases a Big Data, analytics, and business intelligence (BI) solution must cover to ensure that your organization wrings every drop of value out of the data 3.Real-world use cases showing Big Data in action 2

3. Business Intelligence (BI): “The ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.” - IBM researcher Hans Peter Luhn* 3 *IBM Journal: A Business Intelligence System, October 1958

4. The Growth Partnership Company: Analyzing this Large, Growing Market 4 Global Mkt: $22 BILLION Global Mkt: $22 BILLION

5. COMPONENT: ONLINE ANALYTICS COMPONENT: ONLINE ANALYTICS Big data core: Platforms Applications Systems Services Big data core: Platforms Applications Systems Services COMPONENT: MOBILE COMMERCE MGMT (MCM) COMPONENT: MOBILE COMMERCE MGMT (MCM) COMPONENT: CUSTOMER EXPERIENCE MGMT (CEM) APM-CSA QoE CEA COMPONENT: CUSTOMER EXPERIENCE MGMT (CEM) APM-CSA QoE CEA SOCIAL NETWORK ANALYSIS (c) SOCIAL NETWORK ANALYSIS (c) Customer Loyalty (C,M) Customer Loyalty (C,M) Retail/wifi analytics (o,m)

6. STRUCTURED DATA Big Data? MANY Data

7. UNSTRUCTURED and SEMI-STRUCTURED DATA: Enterprise 39 types 5 categories UNSTRUCTURED and SEMI-STRUCTURED DATA: Enterprise 39 types 5 categories Communications & Messaging Communications & Messaging OTHER/External… Online/Digital

8. Networks/Services OSS/BSS Operators/CSPs: ENTERPRISE categories, +… 14 types 3 categories OTHER/External including Content Providers… OTHER/External including Content Providers…

9. “Just add servers.”  Big Data architectural elements –Hyperscale computing and high-performance cluster computing for ultra-high- speed processing –Reconfigurable, massively parallel architecture –Shared-nothing (memory or disk): processes maximum amount of data –Data auto-sharing or “sharding”: partition data across DBs, maintain copy of served application’s data –Memcached: in-memory key-value store for small chunks of data; e.g., superior Web user experience through faster page-loading –Trillions of calculations per second (TeraOPs) vs existing floating point operations per second (FLOPS)  “Open your eyes” (data-wise) –Traditional: sampling/summaries –Unlocking the value of Big Data value: unfiltered, ALL 9

10. 10 “I’ll take one Hadoop, please. Extra analytics.”  What you get: open source distributed computing framework –Open source implementation of Google’s MapReduce data framework –Good when data too large for single DB; cost- prohibitive to index data updates; many simultaneous users  “DB not included” | Add: NoSQL DBs –To get information, must run MapReduce job…TIME* –NoSQL Wide Colum Stores for distributed data storage  Ultra-high-speed performance executing highly-complex queries over similar data  Read only queried attributes (row DBs: all surrounding data)  More efficient attribute storage enhances data compression –Examples: Apache Hbase and Cassandra; Google BigTable;* Cloudata; Cloudera Invented by open source search advocate Douglas Cutting, and named for one of his son’s childhood toys, Hadoop is operated by the Apache Software Foundation. * Google: MapReduce > BigTable

11.  Challenge: distributed data processing | Solution: Apache Hadoop Distributed File System (HDFS) –Grid computing approach –MapReduce to distribute processing across servers  Challenge: Java programming | Solutions: Apache Pig and Hive –Pig simplifies tasks, accommodates semi-structured data –Hive is a DW(H) system for Hadoop  Challenge: integrating structured data | Solutions: Apache Sqoop and Flume –Sqoop does bulk data transfers between RDBs and Hadoop (HDFS or Hive) –Flume imports streaming (Web) log data into HDFS  Other tasks: deployment and ongoing management | Solutions: Apache… –Deployment and administration: Ambarri and Whirr –Workflow management and data sync: Zookeeper and Oozie 11 “One Hadoop, please” (continued)

12. 1.Knowledge Management & Baselining 2.Master Data Management (MDM) 3.Data Integration-ETL-ELT 4.Storage & DW(H) 5.Enterprise Search 6.Security & Enterprise Rights Management 7.Analytics & Reporting 8.Collaboration 9.Fast Start & Extensibility Tools 12 Big Data Blueprint for Success

13. “We all offer real-time analytics.” Not So Fast… 13

14. Use Cases? Boundless. Here Are a Few  Predicting and identifying security threats; fraud detection  Pricing optimization  Behavorial analytics  Predictive customer support  Device analytics (performance failure/part swapouts)  Mobile branding-advertising-commerce  Customer experience  Customer loyalty programs  Integrated retail business optimization  Integrated online/offline business processes …our Featured Speaker and PANEL 14

15. Using Big Data to Make a Powerful Impact Niloy Sanyal Software Commercial Strategy Leader GE Software 15

16. 16 Ask the Experts! Panel Discussion | Driving Big Data: Real-World Big Data Issues & Answers 16  Southard Jones, Vice President of Product Strategy, Birst  Matti Aksela, Ph.D., Vice President, Analytics & Technology, Comptel  Mike Brown, Chief Technology Officer, comScore  Niloy Sanyal, Software Commercial Strategy Leader, GE Software  Lucia Gradinariu, Ph.D., Chief Marketing Strategist, Huawei  Eugene Kolker, Ph.D., Chief Data Officer, Seattle Children’s Hospital TEXT to +1 760 583 4079