How Businesses use Big Data to Impact the Bottom Line

1. John L Myers Managing Research Director Enterprise Management Associates JMyers@EnterpriseManagement.com @johnlmyers44 How Business uses Big Data to impact the Bottom Line Steve Sarsfield Product Marketing HPE Vertica Steve.Sarsfield@hpe.com @stevesarsfield

2. Featured Speakers Steve Sarsfield, Product Marketing Leader, HPE Vertica Based in Cambridge, MA, Steve is an evangelist and spokesperson for Hewlett Packard Enterprise. He is a frequent speaker and blogger on the subjects of big analytics, information quality and data governance. He is also author of the book "The Data Governance Imperative," published by IT Governance Publishing. John Myers, Managing Research Director, EMA John has over 10 years of experience working in areas related to business analytics in professional services consulting and product development roles. Additionally, John helps organizations solve their business analytics problems, whether they relate to operational platforms – such as customer care or billing – or applied analytical applications – such as revenue assurance or fraud management. Slide 2 © 2017 Enterprise Management Associates, Inc.

3. Agenda • Big data and data lake adoption and implementation • Use cases where end-users around the world are using big data in their organizations • How maturity with big data strategies impact why and how business stakeholders use information from their big data environments • How HPE empowers the use of information from big data environments • Question and Answer Slide 3 © 2017 Enterprise Management Associates, Inc.

4. Data Lakes

6. Data Lake Technical Drivers Slide 6 © 2017 Enterprise Management Associates, Inc. Multi-structured Data Management Capabilities Multiple Data Processing Engines Lack of Scale with Existing Platforms Economics

8. Example: Common Architecture Slide 8 © 2017 Enterprise Management Associates, Inc. Ingest & Transform Analyze Visualize SQL Python R NoSQL Spark ML Java Big Data Warehouse Data Lake Structure & Connect Enlighten Real-time Dashboards Business Intelligence Predictive Analytics Descriptive Analytics Ad-hoc Queries Connectors PMMLs

9. Use Cases

11. Big Data Projects: Top Business Goals Slide 11 © 2017 Enterprise Management Associates, Inc. Market basket Analysis for Cross-sell/Up-Sell Social Brand Analysis for Customer Engagement Risk Analysis for Fraud and Cost Reduction

12. Needing different kinds of analysis is common Slide 12 © 2017 Enterprise Management Associates, Inc. Security Analytics • Are there any attacks happening right now? Weather Application • Tell me the current temperature and pressure Short, fast queries Deeper analytics with bigger data sets Machine learning and predictive – What was the high/low for my zip code? – What was the high/low for my state? – What was the average temperature? – Highest and lowest of all time? – Can we predict conditions tomorrow? – What IP and where are most of my events coming from? – Has traffic spiked compared to historical? – Has any event happened liked this over the last three years – What new events should we be tracking to predict security events?

13. Big Data Maturity

16. Consider depth of analytical capabilities and concurrency Slide 16 © 2017 Enterprise Management Associates, Inc. 99 99 80 59 29 19 40 70 VERTICA ENTERPRISE VERTICA SQL ON HADOOP IMPALA HIVE ON TEZ HIVE ON SPARK TEST FAILURES IN STANDARD QUERIES 1 long 2 medium 5 small Test failures in continuous queries: Selected from TPC-DS queries that execute without error on both systems when executed alone. Single Queries Multiple Queries

17. Leverage data warehouse to access data lake Slide 17 © 2017 Enterprise Management Associates, Inc. Hadoop Data Lake Vertica Big Data Warehous CREATE TABLE customer_visits ( customer_id bigint, visit_num int) PARTITIONED BY (page_view_dt date) STORED AS ORC; Customer information in Hadoop Customer information in Data Warehouse SELECT customers.customer_id FROM orders RIGHT OUTER JOIN customers ON orders.customer_id = customers.customer_id GROUP BY customers.customer_id HAVING COUNT(orders.customer_id) = 0; Vertica Engine ROS  Leveraging Web Logs to gain customer insight  Sensor and IOT data for pre-emptive service  Marketing Programs Tracking  Tracking impact of application updates  Many more uses

18. HPE Vertica Use Cases

19. Rethinking the Data Warehouse Slide 19 © 2017 Enterprise Management Associates, Inc. Spark and in-memory Solutions Vertica and columnar data warehouse Hadoop • Hardware costs • Spark Expertise • Per TB • Data Warehouse Expertise • Per node licensing • Hadoop Expertise Cost Emphasis • Licensing Costs • Per user • Per connected system • Per processor • Per amount of data • Tuning and expertise • Getting it to handle big data • Maintenance • Specialized Hardware • Closed system costs Cost Emphasis Legacy data warehouse

21. Cloud Has A Role • Ingest important data and analyze in real-time • Understand Immediate challenges • Tier off data to lower-cost tier for deeper analysis • Provide daily analytics to meet SLA • Analyze Big Data for description and predictive • Cost effectively support concurrent users w/ full SQL support • Tier off data to lower-cost tier as it ages or SLA changes • Ingest data of unknown value • Provide a data lake for data science • Store data for compliance • Fire up extra analytics for a monthly, quarterly, end- of-year calculation • Handle seasonal variations in data and special projects • Ensure expensive hardware doesn’t sit idle most of the time

23. HPE Vertica Enterprise – Columnar storage and advanced compression – Maximum performance and scalability HPE Vertica 23 All built on the same trusted and proven HPE Vertica Core SQL Engine Core HPE Vertica SQL Engine • Advanced Analytics • Open ANSI SQL Standards ++ • R, Python, Java, Spark. Scala • In-database machine learning HPE Vertica for SQL on Hadoop – Native support for ORC and Parquet – Support for industry-leading distributions – No helper node or single point of failure HPE Vertica In the Cloud – Get up and running quickly in the cloud – Flexible, enterprise-class cloud deployment options Try it on my.vertica.com

24. How to Use Big Data and Data Lakes to impact the Bottom Line! • Big Data and data lakes are growing in adoption. • Data lakes are NOT just for exploration. • Maturity matters in how you implement and what you implement in your data lake Slide 24 © 2017 Enterprise Management Associates, Inc.

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