Cisco data analytics in ioe_rajiv niles_2015 nov
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The document discusses the evolution and significance of the Internet of Everything (IoE) and the Internet of Things (IoT), predicting the connection of 50 billion devices by 2020. It highlights the growth of commercial and industrial IoT markets, expected to surpass consumer markets in the long term, and outlines the challenges related to data processing and integration between IT and operational technology (OT). Additionally, it emphasizes the importance of edge computing in managing large volumes of data generated by IoT devices.
Cisco data analytics in ioe_rajiv niles_2015 nov
- 1. © 2013 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1© 2013 Cisco and/or its affiliates. All rights reserved. Cisco Confidential 1 Rajiv Niles Director, Digital Transformation & IOE Acceleration Nov 2015
- 2. 1995 2000 2013 2020 “Fixed” Computing Mobility / BYOD All over IP Internet of EverythingInternet of Things 200M 10B 50B People Process Things Data Third Wave of Internet
- 3. Instrumented Industrial Machine Big Data Analytics Physical and Human Networks Secure, Cloud-based Network THINGS PEOPLE PROCESS DATA
- 4. Source: Cisco Consulting Services BecauseDataisMassive,Messy, and Everywhere 50BDevices Connected by 2020
- 5. It’sHuge, but How Huge? • IoT Hype Has Surpassed Big Data • Press Has Increased 10X in One Year • This Is Not a Market • This Is a Collection of Loosely Related Markets • CAGR of IoT 2013-2020 Is Projected to Be 35% per Year
- 6. IsThistheIoTMarket? • Commercial / Industrial Will Pass Consumer Around 2018 • Until 2020, Consumer IoT Will Be Greater • Long Term, Commercial / Industrial Will Be 2X Consumer In # of Devices • Until 2020, Consumer IoT Will Be Greater • Long term, Commercial / Industrial Will Be 2X Consumer In $
- 7. Christian Horner Team Principal, Infiniti Red Bull Racing “The network will play a crucial part in how we develop the car; gathering data, learning from it and adapting will ultimately determine our season.”
- 8. Edge Devices Data ComputingTiers OptionalFog Nodesfor BetterPerformance Edge Devices Data Center and Applications IoT Data System Fog Node Fog Node Data Center and Applications 3 or More Tiered System2 Tiered System
- 9. IoTWorldForum ReferenceModel Levels Application (Reporting, Analytics, Control) Data Abstraction (Aggregation & Access) Data Accumulation (Storage) Edge Computing (Data Element Analysis & Transformation) Connectivity (Communication & Processing Units) Physical Devices & Controllers (The “Things” in IoT) Collaboration & Processes (Involving People & Business Processes) 1 2 3 4 5 6 7 Sensors, Devices, Machines, Intelligent Edge Nodes of all types Center Edge
- 10. Internetof Things DesignIssues Levels 1 2 3 4 5 6 7 Sensors, Devices, Machines, Intelligent Edge Nodes of all types Center Edge IT OT Query Based Event Based Data at Rest Data in Motion Non-real Time Real Time
- 11. BridgingITand OT: IntroducingIoT“Edgeware” Devices Edge Edge Computing Device Control • Configure (from the device provider) • Status (from the device provider) Device Interactions • Discovery • Addressing • Protocol conversion Middleware • Listeners (Zigbee), brokers (MQTT) • Event grouping / batch interactions Data • Normalize (standardize codes for the app) • Filter (against pre-set criteria from the app) • Expand (decode/expand cryptic codes) • Aggregate (generate statistics) • Notify/alert (to the app) Combine the functions above • Schedule (when to comm with the device) • BPM (when multiple steps are needed) Security • Roles • Privileges An individual edge software function may serve many applications Edge software can be sourced completely separately from the vertical application
- 12. Key Points: • IT – OT • Decoupling Issue: Devices may generate data faster than apps can ingest it Devices Apps BridgingITand OT: HandlingtheVolumeof Data Levels 1 2 3 4 5 6 7 Sensors, Devices, Machines, Intelligent Edge Nodes of all types Center Edge
- 13. IoEAnalyticsIntroducesNew Complexities Key Issues: • The velocity and volume of data may be huge • In some cases, most of the data is unimportant Traditional Capture Data Store Data Analyze Data Edge Computing Becomes Crucial IoT Store Data Analyze Data Capture Data from Devices Reduce Data at the Edge
- 15. Typical DataFlowsacrossCDVand CDP BIAnalytics Self-Service ESBs and Apps Cisco Data Virtualization Big Data / IoT Sources Cloud Data Sources Traditional Data Sources Cisco Data Preparation End-user Data Sets 1.IT-curated enterprise data identified and imported using Business Directory and CIS 2.Business analysts bring their personal data sets 3.Additional Big Data and Cloud sources imported 4.Business analysts iteratively explore, clean and enrich data 5.Prepared data used within analytics to drive business results 6.CDP answer sets promoted to IT / shared via CIS and Business Directory 1 1 1 2 3 4 5 6
- 16. Requirementsfor anIoEPlatform Reusable Modules Framework For Edge Processing & Data Transport Options Fast Data, Deep Data, Streaming Data Flexible For Integration with IT Easy GUI Tools Completely Open System Multiple Vendors