A Bigger Magnifying Glass: Analyzing the Internet of Things
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Transcript of A Bigger Magnifying Glass: Analyzing the Internet of Things
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Grab some coffee and enjoy the pre-show banter
before the top of the
hour! !
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The Briefing Room
A Bigger Magnifying Glass: Analyzing the Internet of Things
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u Reveal the essential characteristics of enterprise
software, good and bad
u Provide a forum for detailed analysis of today’s innovative
technologies
u Give vendors a chance to explain their product to savvy
analysts
u Allow audience members to pose serious questions... and
get answers!
Mission
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Teradata and the Analytics of Things Presenter Name, Date A Bigger Magnifying Glass Analyzing the Internet of Things
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Today’s Speakers
Dan Graham Teradata
Richard Hackathorn Bolder Technology
Eric Kavanagh CEO, The Bloor Group
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Potential of IoT as The Bigger Magnifying Glass
R
• Magnifying Glasses into Your Business – Basic transactional systems, with reporting – Integrating data warehouses – Interactive dimensional analyses – Active DW to support core biz processes – Big data from web and social media – IoT throughout the biz ecosystem
Where we are going…
• Looking at 8 companies using IoT
• IoT apps exploding but unclear goals
• Why should executives care about IoT
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People and process
Operational and edge computing
Doing
Analytics of Things Thinking
Internet of Things
R Its simple: the #AnalyticsofThings is part of the #InternetofThings #IoT
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Public Clouds
Data Center
Analytics Networks
Information Technology
Things Gateway
Operational Technology
The Edge
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“On a three-hour journey, only one out of over two
thousand journeys are delayed by more than five
minutes.”
Gerhard Kress Siemens spokesman
SIEMENS High-speed train between Madrid and Barcelona
R @Siemens makes trains on time in Madrid with @Teradata #IoT
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• Situation – 32,000 miles of track | 3350 trains per day
• Problem – Prevent derailments from overheated wheels
• Solution – Sensors every 20 miles | outliers detection
• Results – 20M daily sensor readings | 1500 issues/day |
new maintenance schedules | cut bearing-related derailments by 75%
Preventing Derailments
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• Situation – Per capita electricity consumption
• Problem – Peak periods utilization surges – Electricity based heating
• Solution – Alternative pricing programs – Incentives for natural gas heating
Utilities
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• Situation – 10-20K sensors per machine
• Problem – Unplanned shutdowns – Consumables cost—felts and belts, parts
• Solution – CBM and benchmarking consumables – Analyze pulp, chemicals, heat, suppliers
• Results – $35M selling ‘our’ consumables
Grinding Pulp and Sensor Data
@Teradata Aster delivers $35M in revenue from #IoT
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• Situation – Semiconductor manufacturing yields
• Problem – Hard to analyze fragmented data – 30K files daily from around the globe
• Solution – Consolidating data in data lake
• Results – Prepare data for IDW – Find root causes: quality up, costs down
Increased Product Quality
@ThinkBigA builds data lakes for #IoT
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• Situation – Oil shifts around as it is pumped out – Find the oil, again, and again
• Problem – Geolocation accuracy | legacy sensors
• Solution – Teradata consultants – Deep math and analytics
• Results – High quality surveys | less downtime
Deep Sea Oil Reservoirs
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• Situation – Predicting machine repairs
• Problem – No business access to sensor data
• Solution – Connect QueryGrid to data lake
• Results – Improved maintenance - Labor, parts, schedules in sync
– Bridge between cultures
Heavy Assets
https://commons.wikimedia.org/wiki/File:Titan,_once_the_largest_mining_truck_in_the_world.jpg
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13 DTG
• The vision – Smart City 2050 – Resources (energy, mobility, buildings) – Quality of life (social, health, environment) – Innovation (education, economy, research)
• Solution – New 42 acre suburb – Multi-vendor multi-year deployments
• Results and next steps – Low voltage grid optimizer – Balancing to weather is next
Smart Cities
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Data Warehouse
Project benchmarks
Operational grid planning
Grid load forecasts
Strategic grid planning
Low voltage grid optimizer
Grid management
Grid alerts
Smart meters
Energy monitoring and control
Building flexibility
Predictive maintenance
Consumption optimization
Citizen interaction
Smart City: Where OT Meets IT
DTG
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• IoT will incrementally expand
• May be limited by tech assimilation
• Numerous strategic impacts
• Drives revenue & business strategy
• Value depends on analytics
Why Should Executives Care About the Internet of Things?
R
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Why Executives Should Care
• Value expansion from initial use cases – Ask questions & imagine opportunities – Incremental not ‘big-bang’ projects – Need to evolve people & culture
• Technology assimilation – Lead cultural change -- ask tough questions – Bust thru traditional tech boundaries
R @hackathorn says #IoT projects breed like rabbits and that’s good. Cute bunnies photo
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Why Executives Should Care
Strategic
Tactical
Operational
Policy
Procedure
Actions
R
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Why Executives Should Care
– Holistic view of the business
– Cross-functional business impacts
IoTSensors
IoTNetworks
IoTDataCura2on
DataWarehouse
Analy2cs
IoTBusinessValue
R
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Questions and Answers
For More Information Hyperlink
Business Value from the Analytics-of-Things
http://www.teradata.com/Resources/White-Papers/Business-Value-from-the-Analytics-of-Things
Obsession with Quality at Western Digital Corporation
http://www.teradata.com/Resources/Case-Studies/Western-Digital-Obsession-with-Quality-at-Western-Digital-Corporation
How the Internet of Things Changes Big Data Analytics
http://data-informed.com/how-the-internet-of-things-changes-big-data-analytics/
Richard Hackathorn http://bolder.com/
Teradata and the Internet of Things http://www.teradata.com/solutions-and-industries/IoT
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THANK YOU for your
ATTENTION!
Some images provided courtesy of Wikimedia Commons