Sprint's Data Modernization Journey
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In this webinar, we will hear from Mark McKinney, Director – Enterprise Data Analytics at Sprint about the business drivers, key success factors, and challenges faced while undertaking Sprint’s data modernization journey. You will hear how Sprint set about establishing a Hadoop data lake, ingested data from multiple environments, and overcame key skill shortages. You will also hear from Diyotta and Hortonworks about best practices for modernizing your data architecture to support transformational business initiatives. https://hortonworks.com/webinar/sprints-data-modernization-journey/
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Sprint's Data Modernization Journey
- 3. 3 ©2016 Sprint. This information is subject to Sprint policies regarding use and is the property of Sprint and/or its relevant affiliates and may contain restricted, confidential or privileged materials intended for the sole use of the intended recipient. Any review, use, distribution or disclosure is prohibited without authorization. 10.1.2016 Version 1.1 DSB Sprint’s Data Modernization Journey Putting a foundational framework for empowering Analytics November 30, 2017
- 4. 4 ©2016 Sprint. This information is subject to Sprint policies regarding use and is the property of Sprint and/or its relevant affiliates and may contain restricted, confidential or privileged materials intended for the sole use of the intended recipient. Any review, use, distribution or disclosure is prohibited without authorization. 10.1.2016 Version 1.1 DSB About Sprint Sprint is one of the largest telecom services providers in the US with business operations across multiple countries offering its residential and commercial customers a variety of communication and media services. Multiple Countries - US, Mexico, Caribbean Wireless, Internet, Prepaid, Wired Service Offerings - B2B, B2C 60M Subscriber Base
- 5. 5 ©2016 Sprint. This information is subject to Sprint policies regarding use and is the property of Sprint and/or its relevant affiliates and may contain restricted, confidential or privileged materials intended for the sole use of the intended recipient. Any review, use, distribution or disclosure is prohibited without authorization. 10.1.2016 Version 1.1 DSB Our Modernization Demand Drivers Paradigm Shift in Customer Usage – Data now outpaces Voice usage enormously Data Explosion – More data originating from all directions and new types Increased Appetite for Analytics – Cost Savings, Better Customer Experience We were experiencing a paradigm shift in how the data landscape was evolving and our focus was on how we can harness the wealth of data to better serve our customers and improve our business operations.
- 6. 6 ©2016 Sprint. This information is subject to Sprint policies regarding use and is the property of Sprint and/or its relevant affiliates and may contain restricted, confidential or privileged materials intended for the sole use of the intended recipient. Any review, use, distribution or disclosure is prohibited without authorization. 10.1.2016 Version 1.1 DSB Our Modernization Goals Our data modernization goals were focused on business imperatives in order to provide a richer, personalized customer experience, improve business operations and gain better visibility into our Customers. Fraud detection – Expanded datasets and improve data availability Improve visibility to gain better insights and make timely business decisions 360 Degree View of Customer – better ways to serve and reduce churn
- 7. 7 ©2016 Sprint. This information is subject to Sprint policies regarding use and is the property of Sprint and/or its relevant affiliates and may contain restricted, confidential or privileged materials intended for the sole use of the intended recipient. Any review, use, distribution or disclosure is prohibited without authorization. 10.1.2016 Version 1.1 DSB Iterative Approach to Modernization We took an iterative approach towards modernization in order to ensure our journey had meaningful and achievable targets. Build foundational framework with an evolving and scalable architecture Reduce data redundancy by consolidating data points and improve data availability Spend less time on lift & transform and more on Analytics Core Foundation Data Consolidation Empower Analytics
- 8. 8 ©2016 Sprint. This information is subject to Sprint policies regarding use and is the property of Sprint and/or its relevant affiliates and may contain restricted, confidential or privileged materials intended for the sole use of the intended recipient. Any review, use, distribution or disclosure is prohibited without authorization. 10.1.2016 Version 1.1 DSB Tips for Modernization • Analysis and scoping • Big data is not just volume but also more compute and analytics • Scalable hardware which can be improved for more compute when required • Network throughputs or improve bandwidth for large data volumes • Prepare and plan • Technology and Business Teams Adoption • Plan real-time architecture ahead instead of rearchitecting later • Communicate and manage expectations Think big, start small, move fast.
- 13. What Matters for Data Modernization • Time to Value • Data Latency • Data Consumption • Cost Technology • Performance • Scalability • Maintainability • Evolving Technologies Enablement Business
- 18. 18 © Hortonworks Inc. 2011 –2017. All Rights Reserved The New Way of Business Is Fueled By Connected Data • Connected Customers, Vehicles, Devices • Socially crowd- sourced requirements • Digital design and analysis • Digital prototypes and tests (simulations) • Connected Factories, Sensors, Devices • Human-robotic interaction • 3D-printing on demand • Connected Trucks, Inventory • Location, traffic, weather-aware distribution • Real-time inventory visibility • Dynamic rerouting • Connected Customers, Devices • Omni- channel demand sensing • Real-Time Recommendations • Connected Assets • Remote service monitoring & delivery • Predictive maintenance • OTA Updates Development Manufacturing Distribution Marketing/Sales Service
- 19. 19 © Hortonworks Inc. 2011 –2017. All Rights Reserved Modern Data Architecture Telemetry – Connected Devices Time–Series Historian Machine Learning C L O U D Sensors, SCADA, Control Systems Edge Analytics Exceptions that Drive Predictive Maintenance 360 View of Operations, Equipment Failure Analytics, etc. Deep Historical Analysis DATA C E N T E R Stream Analytics Cyber & Threat Data
- 20. 20 © Hortonworks Inc. 2011 –2016. All Rights Reserved Many organizations struggle to understand the difference between Big Data and Big BI Big Business Intelligence vs. Big Data Big BI DATA Big Data § Same analysis as before, just more data § Batch or warehouse-type processing § Informative, but not really actionable § Joining data sets never before joined, asking questions never before asked § Real-time or near real-time, leading to predictive/persuasive/preventive § Ability to extract the value of data and quickly translate into action – ”Data Agility” § Action-oriented, driving “Action at the Speed of Insight”Many apply traditional reporting methods to Big Data & Advanced Analytics resulting in missed opportunities and limited financial return
- 22. 22 © Hortonworks Inc. 2011 – 2017. All Rights Reserved EDW Optimization patterns : 3 Use Cases • Active Archiving • ETL Offloading • Data Enrichment ANALYTICS Data Marts Business Analytics Visualization & Dashboards DATA SYSTEMS Systems of Record RDBMS ERP CRM Other
- 23. 23 © Hortonworks Inc. 2011 – 2017. All Rights Reserved EDW Plus Hortonworks Data Platform (powered by Apache Hadoop) Optimize and Reduce Costs Archive Cold Data away from EDW • Move cold or rarely used data to Hadoop as active archive • Store more of your data longer, cheaper • Greater historical analysis Offload costly ETL process • Free your EDW to perform high-value functions like analytics & reporting, not ETL • Use Hadoop for advanced or massive-scale ETL/ELT ANALYTICSDATA SYSTEMS Data Marts Business Analytics Visualization & Dashboards Systems of Record RDBMS ERP CRM Other ELT ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° N Cold Data, Deeper Archive & New Sources Enterprise Data Warehouse Hot Data Science OLAP on Hadoop
- 24. 24 © Hortonworks Inc. 2011 – 2017. All Rights Reserved EDW Plus Hortonworks Data Platform and Hortonworks DataFlow Land and Enrich more data to respond faster to new business requests Archive Cold Data away from EDW • Move cold or rarely used data to Hadoop as active archive • Store more of your data longer, cheaper • Greater historical analysis Offload costly ETL process • Free your EDW to perform high-value functions like analytics & reporting, not ETL • Use Hadoop for advanced or massive-scale ETL/ELT Land & Enrich more data to create more value-add analytics • Use Hadoop to ingest new data sources, such as web and machine data for new analytical context from unstructured and semi-structured sources • Create an analytical sandbox for advanced data science ANALYTICSDATA SYSTEMS Data Marts Business Analytics Visualization & Dashboards Systems of Record RDBMS ERP CRM Other ELT ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° ° N Cold Data, Deeper Archive & New Sources Enterprise Data Warehouse Hot Data Science OLAP on Hadoop Clickstream Web & Social Geolocation Sensor & Machine Server Logs Unstructured NEW SOURCES Ingest Stream Events
- 26. 26 © Hortonworks Inc. 2011 –2016. All Rights Reserved M A X I M U M C O M M U N I T Y I N N O V A T I O N T H E I N N O V A T I O N A D V A N T A G E P R O P R I E T A R Y H A D O O P T I M E INNOVATION Innovation happens best not in isolation but in collaboration OPEN COMMUNITY Our Founding Belief
- 27. 27 © Hortonworks Inc. 2011 –2016. All Rights Reserved M A X I M U M C O M M U N I T Y I N N O V A T I O N T H E I N N O V A T I O N A D V A N T A G E P R O P R I E T A R Y H A D O O P T I M E INNOVATION Innovation happens best not in isolation but in collaboration OPEN COMMUNITY Our Founding Belief Unmatched Economics Enable cost effective data-center and cloud architectures Minimal Vendor and Integration Risks prevents vendor lock-in and speeds ecosystem adoption of ODPi-compliant core