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The Power of your Data Achieved - Next Gen Modernization

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Fueled by ever-changing customer behaviors and an increasing number of industry disruptions, the modern enterprise requires analytics to stay ahead of the game. Today’s data warehouse needs continuous enhancements to address new requirements for advanced analytics, real-time streaming data, Big Data, and unstructured data. The focus should be on developing a forward-looking, future-proof view and holistically addressing the combination of forces that are impacting the existing operational model.

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The Power of your Data Achieved - Next Gen Modernization

  1. 1. 1Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved The Power of your Data Achieved – Next Gen Modernization October 2016
  2. 2. 2Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Karim Damji, Saama Karim is VP, Product Management, and joined Saama from Plantronics, where he led software strategy and product management, focusing on driving developer platforms, strategic partner integrations and contextually enabled UC solutions. Prior to joining Plantronics, Karim served in leadership positions spanning business development, product management, sales and network engineering at Cisco, Vocera Communications, MobileIron and DiVitas. Karim spent 7 years at Cisco building global VoIP and WAN networks, eventually transitioning to product architecture positions. At Vocera Communications, Karim was the founding product manager responsible for driving product concept to market-leading solutions. Eric Thorsen, Hortonworks Eric Thorsen is VP, Industry Solutions at Hortonworks, with a specialty in Retail and Consumer Products. Eric holds over 25 years of technology expertise. Prior to joining Hortonworks, Eric was a VP with SAP, managing strategic customers in Retail and CP industries. Focusing on business value and impact of technology on business imperatives, Eric has counseled grocers, e-commerce, durables and hardline manufacturers, as well as fashion and specialty retailers. Eric’s focus on open source big data provides strategic direction for revenue and margin gain, greater consumer loyalty, and cost-takeout opportunities. Today’s Speakers
  3. 3. 3Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Agenda Modern Data Trends What a Modern Data Platform Looks Like Case Studies Key Takeaways
  4. 4. 4Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved 4ZB DATA 44ZB DATA TOMORROW INTERNET OF ANYTHING
  5. 5. 5Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Polling #1 – Maturity Curve How mature is your organization regarding Hadoop – open source data management? 1. Aware – Big data is discussed but not reflected in the business strategy. There is general awareness of the benefits of Big Data. 2. Exploring – The enterprise recognizes the potential for data to be used to generate business insights. 3. Optimizing – The enterprise business strategy encourages the use of insights from data within business processes. 4. Transforming – Data drives continuous business model innovation and competitive advantage.
  6. 6. 6Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Data Drives the Connected Car Insurance Premiums Warranties Recalls Pricing Models Design Innovation Autonomous Driving Connected City Infotainment Sensors Scheduled Maintenance Predictive Maintenance Route Optimization INSURANCE COMPANIES GOVERNMENT AGENCIES INFOTAINMENT PROVIDERS SOFTWARE COMPANIES AUTO MAKERS
  7. 7. 7Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved 7Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Modern Data Applications deliver the value of actionable intelligence only possible with both data in motion and data at rest. The Connected Car is an example of a Modern Data Application DATA IN MOTION DATA AT REST
  8. 8. 8Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved 8Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Modern Data Applications require architectures that connect the cloud with the data center. CONNECTING THE CLOUD WITH THE DATA CENTER Modern Data Applications and the Data Architecture
  9. 9. 9Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Survey #2 - Cloud How does your organization approach cloud? 1. All on-site and intend to stay that way 2. Primarily on-premise, but have some cloud applications 3. Hybrid mode between cloud and on-premise but trending towards cloud 4. “Cloud-first” strategy
  10. 10. 10Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved 10Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Hortonworks solutions come with enterprise-ready security, governance, and operations, to deliver Actionable Intelligence with confidence Ready for Any Enterprise
  11. 11. 11Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Merck’s Journey Improving Life Sciences Manufacturing Yields Presents a Complex Data Discovery Challenge Vaccine manufacturing requires precise control of complex fermentation processes Two batches of a vaccine, produced using an identical manufacturing process, can exhibit significant yield variances Batches that fail quality standards can cost $1 million each Merck analyzed one vaccine: 10 years of manufacturing data stored across 16 systems
  12. 12. 12Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Merck’s Journey Scientific Search Sensor Data Storage Vaccine Yield Optimization Innovate Renovate The Journey to the Golden Batch Combined 10 years data amounted to 1 billion records 5.5 million batch comparisons 1st year yield boost of 40K more doses  $10M profit impact McKinsey: 50% yield improvement Epidemiology D ATA D I S C OV E RY A C T I V E A R C H I V E D A T A D I S C O V E R Y D A T A D I S C O V E R Y The Golden Batch
  13. 13. 13Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Cardinal Health’s Journey Data Ingest Constrained Analysis of the Medical Supply Chain at Fuse by Cardinal Health Cardinal Health supplies equipment and medicines to 85% of US hospitals and clinics Limited visibility into the entire supply chain prevented suppliers from understanding how their drugs were prescribed Acute pharmacists couldn’t see all the product options that they could prescribe for various conditions
  14. 14. 14Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Cardinal Health’s Journey Drug Supply Chain Analytics Sensor Data Ingest Prescription Archive Pandemic Response Outcome-based Medicine Clinical Decision Support Public Data Ingest Drug Cost Optimization Single Patient Record Cardinal Health Launched a New Line of Business Fuse by Cardinal Health aims to make healthcare safer and more cost-effective Team enriches supply chain data with public sources – bringing suppliers, providers and patients closer together Data processing speeds doubled Fuse shows suppliers how their drugs are used Innovate Renovate Balanced Medical Supply Chain PREDICTIVE ANALYTICS P R E D I C T I V E A N A L Y T I C S E T L O N B O A R D D A T A D I S C O V E R Y D A T A E N R I C H M E N T D A T A E N R I C H M E N T A C T I V E A R C H I V E P R E D I C T I V E A N A L Y T I C S S I N G L E V I E W
  15. 15. 15Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Mercy’s Journey Mercy Medical System Sought a Data Lake for a Single View of its Patients – “One Patient, One Record” Existing platform impeded goal of enriching Epic data for 1 million patients over 35 Hospitals and 500 clinics Moving Epic EMR data to Clarity EDW took 24 hours and was “never going to enable real-time analytics”. Now that takes 3-5 minutes with HDP Improved billing processes resulted in $1M additional annual revenue from newly documented secondary diagnoses and care
  16. 16. 16Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved PREDICTIVE ANALYTICS Mercy’s Journey Billing Vital Signs Single Patient Record Lab Notes Privacy Database Medical Decision Support Device Data Ingest Preventive Care Epic Enrichment OPEX Efficiency Epic EMR Replication Better Health Through Data Searches of free-text lab notes, speed researcher insight from “never” to “seconds” Ingest of ICU vital signs increased by 900X, letting clinicians respond more quickly Mercy is building real-time tools to support surgical decisions and preventive care Innovate Renovate Better Health D A T A D I S C O V E R Y S I N G L E V I E W D A T A D I S C O V E R Y S I N G L E V I E W A C T I V E A R C H I V E A C T I V E A R C H I V E A C T I V E A R C H I V E D A T A E N R I C H M E N T E T L O N B O A R D P R E D I C T I V E A N A L Y T I C S
  17. 17. 17Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved 17Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Payment Tracking Due Diligence Social Mapping Product Design M & ACall Analysis Machine Data Defect Detecting Factory Yields Customer Support Basket Analysis Segments Customer Retention Sentiment Analysis Optimize Inventories Supply Chain Cross- Sell Vendor Scorecards Ad Placement Cyber Security Disaster Mitigation Investment Planning Ad Placement Risk Modeling Proactive Repair Inventory Predictions Next Product Recs OPEX Reduction Historical Records Mainframe Offloads Device Data Ingest Rapid Reporting Digital Protection Data as a Service Fraud Prevention Public Data Capture INNOVATE RENOVATE E X P LOR E OP T I M I Z E T R A N S F OR M ACTIVE ARCHIVE ETL ONBOARD DATA ENRICHMENT DATA DISCOVERY SINGLE VIEW PREDICTIVE ANALYTICS M&A Storage Blending M&A Ingest Integration
  18. 18. 18Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Practical Approach to Modernization We’ve looked at the reasons for modernization Following slides cover a real customer success story and our approach to modernization in a real life scenario Including what the customers tell us about the benefits they achieved
  19. 19. 19Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Source Systems Case Study – A Large Insurance Company RDBMS DATA Files Enterprise Data Warehouse Data Mart Data Mart Data Mart Business Requirements 10-12% Rationalized and Transformed Aggregated Need Additional Attributes 1 High Data Fidelity for Modeling 2 Analyzing Unstructured Data 3 Unknown Unknowns Landing Zone
  20. 20. 20Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Survey #3 – Business Agility Are you able serve your business’ ever evolving analytics need? 1. Very Well – Architecture is very flexible and able to serve most fast changing business needs 2. Well Enough – Able to serve 50 to 60% of the changing business needs 3. Not so well – Able to serve 10 to 20% of the changing business needs 4. Not at all – Business requirements driven, new needs require a full change management process
  21. 21. 21Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Saama’s Modern Analytics Framework Connectors for multiple sources: • Structured • Unstructured • Real Time geospatial • Syndicated • Social Media Elastic Search and Indexing Fast (Millisecond) Search Highly Scalable for massive amounts of data Distributed and Scalable Processing Consuming and Storing Large Data Sets Access to Raw Granular Data Self-Service for Modeling and Data Science Fluid Analytics Data Extraction Layer Business Intelligence/Analytics Layer Data Aggregation Layer Layer Raw Transactional Data Layer – Lowest Grain Data Integration Data Storage COBOL / VSAM / DB2, PIG, SQOOP, STORM, MAP - REDUCE DATAACQUISITION Profit Stats Loss Stats Scorecards Quote Conversions Ratios Customer 360 Product 360 Others FILE SYSTEM Sales Legacy Data Marketing Booking Quotes Customer Service IVR Web Logs D&B Syndicated Structured Data Unstructured Data External Data Reference Data Master Data Hierarchies Notes Images IVR Web & Mobile System Logs D&B Social Others PUSH AND/OR PULL PUSH AND/OR PULL Billing Backlog Syndicated PUSH AND/OR PULL Schema on Read Future Proofing Analytics Highly flexible for exploring unknown- unknowns DATAWAREHOUSE/DATALAKE
  22. 22. 22Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved What our Customers Told Us Fraud Analytics We are now focusing on the right claims to investigate and catch more fraudulent claims based on the predictive scoring model, the uptick is already +0.5% on actual fraud caught Agency Dashboard Real-time search of the customer/ prospect has hugely helped us in serving and upselling, while substantially increasing customer satisfaction and engagement Decommission The platform has enabled us to accelerate the decommissioning to legacy systems while adhering to regulatory requirements, saving us 10s of millions Subrogation We are already see an uptick in subrogation claims by 5%, saving us close to $10 million, thanks to machine learning algorithms and analytics Special Investigation Unit Insurance Agent CIO Head of Claims Machine Learning and Modeling Elastic Search Low Cost Hot/Warm Storage Raw Streaming Data
  23. 23. 23Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Key Takeaways Modernizing Analytics is more of a need than a want Modern Analytics are based on the Hadoop Ecosystem To deliver Modern Analytics, Saama’s Framework builds on top of the Hadoop ecosystem and adds • Schema on Read - Data Models • Elastic Search • Data Flows • Algorithms • Aggregation • Analytics Saama can deliver a complete Modernization of Analytics using Saama’s Framework and Hortonworks HDP
  24. 24. 24Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved About Saama 5000+ Engagements 900+ Employees 50+ Global 250 3000+ Algorithms 1 Purpose Accelerating Business Outcomes using Data Driven Insights
  25. 25. 25Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved About Hortonworks Leader in Connected Data Platforms Publicly traded on NASDAQ: HDP Hortonworks DataFlow for data in motion Hortonworks Data Platform for data at rest Powering new modern data applications Partnering for Customer Success Leader in open-source community, focused on innovation to meet enterprise needs Unrivaled support subscriptions Founded in 2011 Original 24 Architects, Developers, Operators of Hadoop from Yahoo! 950+ E M P L O Y E E S 1500+ E C O S Y S T E M PA R T N E R S
  26. 26. 26Copyright © 2016, Saama Technologies and Hortonworks Inc. All Rights Reserved Q&A Or questions to crystal.black@saama.com

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