Le Big Data et
Transcript of Le Big Data et
Le Big Data
et
l’Internet des Objets
CRM_IVADO
Juin 2015
recognitions
mnubo –at a glance
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pronounced
/nu:bo/
about us
: machines
: from the latin word ‘nūbēs’, i.e.
cloud in esperanto
• founded in April 2012
• HQ in Montreal, Canada
• 50 people
• Profitable
TOP 25 INNOVATIVE
COMPANIES
TOP 20 UP & COMING
COMPANIES
• IoT Data Analytics as a Service
• experts in Fast-Data, machine learning,
cloud operations
• addressing consumer tech, home
automation, industrial & auto verticals
our purpose
analyze the world’s IoT data to make it useful“”
we believe:
the Internet of Things has the potential to contribute
positively to the world’s greatest challenges – food &
water, health, environment and productivity.
internet of everything –and anything!
20-50 billion
100 things
$2-14 Trillion
objects will be connected by 2020 (not PCs, smartphones & tablets)
are coming online
every second
Estimated Global Economic Value
of the IoT in 2020 (Gartner, Cisco)
Generated
annually by
2020
15zettabytes
(1021 Bytes)
the IoT, so what? –we have larger problems to address!
4Healthcare Productivity
Food & water shortage Energy & environment
‘Big Data’ –new approaches needed
‘Traditional’ Big Data Fast Data (incl IoT Analytics)
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VOLUME
UNSTRUCTURED DATA
VELOCITY
IN-STREAM PROCESSING
example IoT Analytics –use cases
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• Personalized user
analytics
• Product usage &
improvement
• User profiling &
clustering
• User behavior
pattern matching
• Feature usage
predictions
• Gamification
• Object Lifecycle
Analytics
• Bluetooth pairing
analytics
Wearables &
consumer tech
• Home/building
profiling
• Self-learned lighting
• Upsell
recommendations
• Preventive
maintenance
• Presence and
occupancy
detection
• Energy demand
prediction
• False alarm & faulty
sensor detection
Home & building
automation
• Real-time batch
production quality
scores
• Anomaly detection
• Fault prediction &
preventive
maintenance
• Livestock weight
optimization
• Smart-farming
• Battery-life
predictions
• Algorithms scoring
• Data interpolations
Industrial &
agriculture
• Real-time driving
scores
• Fuel demand
predictions
• Rail defect
detection
• Cargo route
optimization
• Shared resource
optimization
(car/bike sharing)
• Fleet risk scoring
• Real-time driver
feedback
Automotive &
tracking
IoT analytics –smarter agriculture
• 82% of the world’s almonds are produced in California, a $5.8B business in 2013
• Producing one almond takes 1 gallon of water
• The orchards are drinking up 8% of the state's entire water supply – more than Los Angeles & San Francisco combined
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• Connected Weather Stations & Soil Tension Meters
• Real-time data sent to cloud and enriched with weather forecast
• Algos & Predictions: EvapoTranspiration (ET), Irrigation Quality Index (IQI)
• Reduce Water needs
• Increase Yield per acre
IoT analytics –electronics manufacturing
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Challenges
• Shrinking Operating Margins
• Exponential cost of downtime
• Shorter Product Lifecycles
IoT analytics –smart homes
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+ home & commercial analytics
+ Energy Demand/Response
+ learning-based automation
+ usage and operational analysis
+ fault-reduction and prevention
+ Reduce Customer Churn by 15%
+ Lower cost of False Alarms and
Faulty Sensors40% Cost Savings | Anomaly Detection
+ Focused R&D spend
+ Boost Revenues with smarter, data-
driven apps
IoT analytics –wearables & wellness
• Use Case• Wearable Tech maker wanting to
move from Hardware-centric to data-centric
• Device is accessory, just a data collection point
• Consumer signs up to fun, goal-based, 12-wks programs
• mnubo tools• Data enrichment, Predictive analytics,
Profiling & Clustering
• Benefits• Greater user engagement &
stickiness
• Higher CLV
• Recurring Revenue scheme
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technology stack –we use
Data Processing Messaging Data Science
Software DevOps Database
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maths & data tech –we encounter
data mining:• mnubo “ODA"
• clustering (k-Means, …)
• dimensionality reductions (PCA, SVD)
• statistical analysis
classification with machine learning algorithms:• mnubo “RTA"
• machine learning algorithms (random forest, linear models, …)
• detect patterns (HMM)
time series analysis:• scoring models
• autoregressive models (ARIMA...)
• machine learning regression (linear regression, least squares)
• detect outliers and anomaliesReal-Time
On-Demand
Real-Time
working at mnubo – we’re hiring!
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mnubo is always looking for talented people to join
the fun!
Currently:
• IoT Data Scientists
• Scala/Spark Big Data
Developers
• Data Visualization Engineers
• Test Automation Engineers
• Digital Marketing Manager
Find out more about our opportunities: mnubo.com/careers
Thank you!
www.mnubo.com