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Nubix.ioRACHEL TAYLORCEO

What’s holding back Analytics at the Edge?

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Data, Data, Data

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Deluge of Devices & Data41.6 Billion Connected IoT Devices by 2025

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1960 1970 1980 1990 2000 2010 2020

Num

ber o

f Edg

e D

evic

esC

ompu

te D

eman

d at

the

Edg

eEvolution of the Enterprise Architecture

MainframeNo processing power at the edge

CloudProliferation of cloud compute

centralization

Client-ServerPCs processing at the edge

Edge-IoTEverything from servers to shoes

needs to be smart

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Current Enterprise ArchitectureCloud-Native▪ Centralized▪ Container-based▪ Near limitless Compute▪ Powerful Analytics

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Transforming Enterprises - Edge IoT Analytics

Massive Impact▪ OT Flexibility

▪ Drive Business Expansion

▪ Net New Revenue

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Software is eating the world…but only nibbled at the edge

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Value of IoT DataIn Real-Time▪ Analyze the Data▪ Draw Insights▪ Improve Actions & Outcomes

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Centralized & Cloud only AnalyticsCentralized Fails▪ High data volumes▪ Data transport/processing expense▪ Security/Compliance▪ Latency inherent

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Lack of Real-time Insights

5% O&G data analyzed

Takes 30 daysto get production

data on wells

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Oil & Gas: Production Impact

* Fracking specific stats

⅓ of potential oil extracted

1.7T Barrels 5T Barrelsvs

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Companies who invest in analytics get higher growth and ROI

McKinsey* found that companies who invest in analytics are seeing, on average:

▪ Revenue growth 5x better than their peers

▪ Profit growth 8x higher than their peers

▪ Return to shareholders 2x higher than their peers

Edge Analytics & ROI

* McKinsey Report: *https://www.oilandgaseng.com/articles/edge-computing-comes-to-the-oil-and-gas-industry/

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Edge IoT Analytics Challenges

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Top 3 Challenges holding back Analytics at the Edge

▪ Analytics Gap▪ Technology Gap▪ Near-edge

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Analytics Gap

▪ Non-embedded Developers (lack hardware, firmware, C/C++ skills)

▪ Lack of Data Curation/Management Tooling

▪ Lack of Analytics Services on Tiny Platforms

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Technology Gap

▪ Resource Constraints: Compute, Power, Connectivity

▪ Variety/Complexity of IoT Ecosystem

▪ Lack of Standards

▪ Risky Firmware Updates (bricking)

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Most Challenging: Near-edge

▪ 97% listed Real-time Analysis of data/telemetry as most challenging obstacle

▪ Zero latency tolerance, 100% reliability

▪ Example Use Cases ▪ Signal-processing▪ Predictive/Preventative Maintenance▪ Vibration Analysis

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Edge IoT Platforms - Issues

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ABB Ability Platform

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AVnet IoT Connect Platform

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Open IoT Platform - Agnostic Systems

✓ Vital real-time insights at every layer▪ Cloud - ecosystem level insights▪ Gateway - system level insights ▪ Device - equipment level insights

✓ Develop Once - Deploy Anywhere

✓ Easy to Update

✓ Cross-platform/Multi-vendor

✓ Calls for Standardization

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How do we enable Edge IoT Analytics?Tiny Containers & Tiny Services!

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Edge-Native Containers & ServicesEdge-Native Requirements▪ Embedded: compute at the data source▪ Intelligent: analytics on edge devices▪ Risk: continuous deployment

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Nubix: Edge-Native with Cloud-Native Sensibilities

▪ Tiny Containers▪ Tiny Services▪ Orchestration

▪ DevOps▪ Microservices▪ Elastic

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Develop Once, Deploy Anywhere

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What Does a Container Deployment Look Like?

Firmware&

Analytics

Firmware

Analytics

▪ Stable

▪ Unchanging

▪ Update Independently

▪ Low-Risk ▪ Static -

Just Analytics

▪ Risky

Traditional With Nubix

Container

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Container Landscape

Cloud Edge

Docker CEDocker EEKubernetes

Nubix

BalenaZededaPixeom

(microdatacenters)

Fog Edge(IoT Gateways)

Device Edge(Controllers, Sensors, ...)

EmbeddedArm A-ClassArm M-Class

WorkstationIntel Atom

AMD A-SeriesArm A-Class

EnterpriseIntel Xeon

AMD Opteron

Linux-based containers can’t cross the M-class barrier

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Developing with Nubix

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Speaker/Demo Hours:Thursday 12-1pm2nd Floor Convention Center

Rachel Taylor Tom Yatesrachel@nubix.io tom@nubix.io408.835.8597 415.490.7542

rachel@nubix.io