Le Big Data et

Le Big Data

et

l’Internet des Objets

CRM_IVADO

Juin 2015

recognitions

mnubo –at a glance

2

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)

5

VOLUME

UNSTRUCTURED DATA

VELOCITY

IN-STREAM PROCESSING

example IoT Analytics –use cases

6

• 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

7

• 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

8

Challenges

• Shrinking Operating Margins

• Exponential cost of downtime

• Shorter Product Lifecycles

IoT analytics –smart homes

9

+ 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

11

technology stack –we use

Data Processing Messaging Data Science

Software DevOps Database

12

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!

13

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