Data Analytics in Digital Transformation
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Pivotal helps large companies undergo digital transformation by implementing agile development methodologies and using data analytics. Pivotal works with one-third of Fortune 100 companies to build cloud native applications on their platform using microservices and data science. Case studies show how Pivotal has helped companies gain insights from data to improve customer experience through predictive models for network issues, call routing, and predictive maintenance.
![Data manifests as new app features
“We’ve found that when a
host selects a price that’s
within 5% of their tip,
they’re nearly 4 times
more likely to get booked”
“The importance of
accuracy and efficiency
[…], will continue to rise
as we expand and
improve products like
uberPOOL and beyond.”
“Over 75% of what
people watch come from
our recommendations”](https://image.slidesharecdn.com/pivotal-telstrasymposiumdeck-171109010159/85/Data-Analytics-in-Digital-Transformation-5-320.jpg)
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Data Analytics in Digital Transformation
- 1. © Copyright 2017 Pivotal Software, Inc. All rights Reserved. Version 1.0 Mukund Babbar Sr. Platform Architect Pivotal Inc. Data Analytics in Digital Transformation
- 2. IMPLEMENTING new methodologies to influence the software development culture of Silicon Valley’s most influential Internet companies DISCOVERING ACCELERATING an agile, rapid iteration, test- driven approach to software development the digital transformation of the world’s largest companies with a modern software development methodology, advanced analytics and modern cloud platform the world’s largest companies into cloud native software companies including one-third of the Fortune 100 TRANSFORMING Pivotal 2000s1990s 2013 Now Founded
- 3. Anatomy of a Modern Digital Business BUSINESS DRIVERS • New systems of engagement • New business models • Internet of Things “Events, people or things happening now and affect the outcome” ENABLED BY The Private / Public Cloud “Infinite, inexpensive compute storage” CAPABILITIES NEEDED Compelling, Unique User Experience/Model Agile Product Development Culture Data Analytics Platform Existing Systems
- 4. Millions of “trip” events each day globally 400+ billion viewing- related events per day Five billion training data points for Price Tip feature Disrupters Use a LOT of Data
- 5. Data manifests as new app features “We’ve found that when a host selects a price that’s within 5% of their tip, they’re nearly 4 times more likely to get booked” “The importance of accuracy and efficiency […], will continue to rise as we expand and improve products like uberPOOL and beyond.” “Over 75% of what people watch come from our recommendations”
- 6. © 2017 Pivotal Software, Inc. All rights reserved. Open Source Innovation Agile Methodologies Cultural Transformation Massively Parallel Processing Data Science and Machine Learning Loosely-coupled Data Microservices Cloud Native Continuous Delivery So How are they accomplishing this?
- 7. “Companies need to learn how to influence people or things in the act of doing something and affect the outcome” PAUL MARITZ EXECUTIVE CHAIRMAN, PIVOTAL
- 8. Analytical Workflows in Smart Apps Be Predictive REQUIRES: Machine Learning at Scale • Demand Forecasting • Preventive Maintenance • Customs Delays REQUIRES: Large Scale Optimization • Routing • Maintenance Scheduling • Aircraft Load Plans Optimize Decisions REQUIRES: Simple interface (applications) for analysts to interact with the optimization algorithms • Querying Solution • Disruption Management • User Preference Capture Enable What-if Scenarios
- 9. Becoming a Data-Driven Company Is Hard Legacy data infrastructure can’t scale or cope Affecting outcomes requires precision and speed Companies in the top third of their industry in the use of data-driven decision making are, on average, 5% more productive and 6% more profitable than their competitors 1 1http://hbr.org/2012/10/big-data-the-management-revolution/ar/2 Turning insight into impact is mysterious
- 10. Analytics within Digital Transformation Cannot Be Accomplished In A Silo
- 11. Large Enterprises look like this... ● Silo’d and aging database systems ● Spaghetti data pipelines ● Expensive, proprietary data management systems ● Lack of structured platforms for continuous software delivery ● Monolithic application architectures ● Batch-oriented data integration ● Limited operationalization of analytics ● Proprietary systems
- 12. Stream + Batch Processing Programming + Operating Model Cloud-Native Platform Microservices FrameworkPlatform Runtime Cheap HW MPP OSS Microservices and Polyglot Persistence IMDG K/V Store Relational DB Data Science & Machine Learning Cloud Infrastructure Modern Cloud Native Data Architecture
- 13. Modern Cloud Native Data Architecture - Technology NeedsOSS Fast Ingest • Pipelines to consume streaming and batch data from various endpoints • Ability to do real time scoring Speed/Serving Layer • In-memory data grids for real-time and aggregated data sets • ms latency access Analytics Platform • Commodity hardware • Ability to support scale out compute • Parallel execution of SQL, R, Python, and other languages • Ability to run machine learning at scale Platform for Scalable Web/Mobile Apps • Infrastructure Automation • Container Orchestration • 12 factor apps • Data Services that scale • Develop, run and manage Web/mobile applications without the complexity of building and maintaining the infrastructure
- 14. Recipe for Smart Apps Scale-out analytic database Model as API Cloud Native Application Platform Data Sources 0 5 Recipe for Smart Apps
- 16. A Real-World Example App Development Data analytics Cloud-native App platform Data Science & Model building APP Data Microservice
- 17. A Real-World Example App Development Data analytics Cloud-native App platform Data Science & Model building Data Microservice APP Must support scale-out query processing Must deliver as an API Must embrace agile development, focus on outcomes Must support microservices, agile dev, and connect to big data analytics
- 18. Reference Architecture Data Sources ms-Latency Data API Analytical Platform Update with findings Client application Machine learning model Evaluate Train Real-time data streams
- 19. Can you... Ask any of question of your data? Modify data pipelines and add processing steps frequently? Consume a wide range of data sources and protocols? Release new features in minutes, multiple times a day? Support a microservices architecture? Update algorithms and models daily?
- 20. Case Studies
- 21. MOTIVE Maximize Value from Industrial Assets by Creating a Strong Software Foundation for the Industrial Internet BENEFITS ● Intelligent Machines ● Real-time safety ● Transmitting Valuable data ● Optimizing Operations ● Empowering Technicians ● Efficient Maintenance ● Huge Savings Industrial Internet
- 22. I’m not seeing any alarms… Why are our customers having poor service? Network Outage CHALLENGES ● Poor network quality and frequent outages ● Little visibility on what’s happening on the network ● Takes long to detect root cause of network failure BENEFITS ● Real-time data insights on cell phone network quality KPIs ● Data Science models for improving the network quality by predicting network failures
- 23. You’re the third person I’ve been handed off to! Can’t anyone help me? Smart IVR ● Real time ingestion of customer data from multiple source systems ● Intent Prediction Model deployed and updated every couple of hours BENEFITS ● Better routing of calls based on past engagements and customer intent ● Reduced calls to care agents ● Better customer experience Australian Telco
- 24. You’ve charged me extra. I need to know details or give me refund Bill Shock ● Real time ingestion of network probes data 6 Billion events a day. ● Hourly aggregate of customer data usage pattern based on application and location BENEFITS ● Visibility of customer data usage pattern to care agents ● Huge reduction in credits given to customers ● Better customer experience Australian Telco
- 25. Predictive Car Maintenance MOTIVE ● Manage business more efficiently ● Ability to detect failure at the earliest stage ● Handle sensitive data from sensors and other applications ● Get car and IT development teams in sync BENEFITS ● Data Science models to more accurately predict part failure ● Optimize part repair and replacement schedule ● Better customer experience
- 26. “We chose pivotal because we believe it provides a 360- degree view of the process. From a data science and data technology perspective, it means delivering best-in-class data technologies and enabling them on their platform.”
- 27. BECOMING A DATA DRIVEN COMPANY… IS NOT Just about deploying Hadoop OR How many Data Scientists you have
- 28. IT’S ALL ABOUT HOW YOU OPERATIONALIZE YOUR INSIGHTS
- 29. Transforming How The World Builds Software © Copyright 2017 Pivotal Software, Inc. All rights Reserved.