Beyond Big Data: Data Science and AI
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This document discusses how data science and AI are fueling new business models driven by data. It summarizes that (1) connected devices, customers, and sensors are generating massive amounts of data across manufacturing, distribution, marketing, sales, and service; (2) technologies like cloud computing, streaming data, IoT, and machine learning are enabling new ways to harness this data; and (3) a modern data architecture is needed to encompass all data sources, enable analytics and machine learning, and power actionable intelligence across edge, cloud, and on-premises environments.
Beyond Big Data: Data Science and AI
- 2. The New Way of Business Is Fueled by Data • Connected customers, vehicles, devices • Socially crowd-sourced requirements • Digital design and analysis • Digital prototypes and tests • 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 MANUFACTURING DISTRIBUTION MARKETING/SALES SERVICEDEVELOPMENT
- 4. INTERNET OF THINGS DATA SCIENCE/ MACHINE LEARNING CLOUD COMPUTING STREAMING DATA ~$380B $210B~$1300B ~$19B Sources: Public Cloud Services Market size, $383B by 2020, Gartner 2017 WW Public Cloud Services market. Big Data & Business Analytics revenues forecast to be $210B by 2020, IDC 2017. IoT Spending forecast to be ~1.31T by 2020, IDC 2017 Worldwide IoT Spending Guide. AI intelligence market size to reach $19,478 million by 2022, growing at a CAGR of 45.4% from 2016 to 2022, Allied Market Research. The Perfect Storm: Tech Trends Fueling Business
- 6. Exception-Based Monitoring 360 View of Operations, Equipment Failure Analytics, etc. Deep Historical Analysis DATA C E NT E R Stream Analytics Cyber Security & Threat Detection Telemetry – Connected Devices Machine Learning C LO UD Sensors, SCADA, Control Systems Edge Analytics Time Series Historian Modern Data Architecture
- 8. Powering the Modern Data Architecture DATA AT RESTDATA IN MOTION ACTIONABLE INTELLIGENCE COMPLETE DATA LIFECYCLE MANAGEMENT RUN CONTAINERIZED APPLICATIONS CONCURRENTLY EDGECLOUD H O L I S T I C M A N A G E M E N T, G O V E R N A N C E A N D S E C U R I T Y ON-PREMISES MULTI-WORKLOADS MULTI-TYPE MULTI-TIER
- 10. 10 T H A N K Y O U
Editor's Notes
- #2: The future of the enterprise is becoming clearer as organizations begin to realize the strategic value of data. Today I want to walk you through the drivers and look at what the high level architecture will look like as enterprises realize that value.
- #3: * Business across all industries are undergoing a digital transformation of massive scale. * Establishing a world where they are connecting everything to everything else. People, devices, vehicles
- #4: We started our journey by making Hadoop ready for the enterprise. Established a data platform for structured data AND the new paradigm data from streams and social platforms open community open ecosystem. Multi tenancy and integrated security and governance Data in motion: manage data through its entire lifecycle from inception to where it lands at rest. With security, governance, lineage across that entire journey. On Prem and cloud Now serving ML, DL and AI
- #5: Over the course of the last 5 years the 4 mega trends have driven and even accelerated the need to transform into a modern data architecture. * These trends are powering and enabling these transformations * Driving with it tremendous rewards for winners and losers in each industry
- #6: Hortonworks open and Connected Platforms enable this transformation and are the core of the modern data architecture. Real time decisions on data in motion and data at rest—to the edge.
- #7: So, what’s really happening? There is a an entire new world being created by combining lots of data with break through tools. Data could be on-premises and in the cloud Data is moving from sensors in real time across our data fabric and giving us precise instrumentation of what happened just before an event as well as after the event. This is true for customers buying on the web as well as products that might fail. We can run our machine learning and deep learning on these vast repositories of data And we can push these models down to the edges to automate decision
- #8: • Data Science is the next key driver to transformational business execution • Companies need a strategic approach to turn data into value and create a competitive advantage We need three things today to succeed: • Lots of data makes the models very accurate • Lots of compute makes the models run fast • Data science as a team sport, new tools enable collaboration and make the models easy to deploy.
- #9: We are not done innovating – the journey will continue We have solved the need for Hadoop to become enterprise ready We saw the need to manage data from inception to rest with our data in motion platform And we saw the need for driving the consumability of these platforms via the cloud Like we did for Hadoop, we are now working on making our the modern Data Architecture enterprise ready and usable