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MarketingSmart Cloud
Building real-time predictive apps from small data vs big data: two use cases
PAPIs Connect
Athmane HAMEL & Cédric HERVET
May 21st, 2015
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Our Group
2
i
SPECIALIST LEADER CROSS ACTIVITY
SECTORS
EXPERTS
DIGITAL TO
15 M€
INNOVATIVEDATA
EUROPEAN
QUALITY
SaaS Marketing: 50%
Data intelligence: 50%110 Paris, Bordeaux,
London
ISO 9001 since
2001
30 digital experts CIR35 statisticians
15 of CAC 40
companies
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• Introduction: general concepts
• 1st use case with small data: target sales
• 2nd use case with big data: failure detection
Summary
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Two steps
Machine Learning Apps - general concepts
Learning• Use historical data to build a predictive model
• A predicted variable & a set of prediction variables
Prediction• Use the prediction model to make predictions
• Back-end App Or Front-end App
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Real-time Predictive Analytics, does that mean:
Make predictions using a model?
• This is the most common use
Build the predictive model in real-time?
• Model building involves rigorous experimentation, sufficient historical data. It is time consuming process
Update the model in real-time?
• Refresh a model implies other issues: use fresh data, replace existing data and keep the same learning dataset size, use more and
more larger dataset?
Even technologies make it possible, is there a benefit in doing so?
• The model does not match with just one data point
• Yes, we can do! but it’s necessary to define railing rules based on domain expertise (or experts)
Real-time: training or predictions?
Training data
Test data Hypothesis
Machine Learning Algorithm
Performance
Feedback
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• Learn from little is possible• Create new data to enrich the data collection
• Choosing the good model is critical
• Well-defined objectives
• What about Big data • Large volumes and types, large objectives set, large models
library
• Machine learning Algorithms = Oil for knowledge discovery
• Increasing “Open Data” sources
• Additional steps for data integration
• Data extraction: using unstructured datasets, we have to create structured data
• Aggregation step: allowing the creation of new quantitative data
• Evolving objectives in respect with own data knowledge
With which data?
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Request the documentation
http://www.np6.co.uk/contact-request/
To read more
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Bordeaux
104 Bis Quai des Chartrons
33300 Bordeaux
T. +33 557 92 41 21
Paris
7, rue du Pasteur Wagner
75011 PARIS
T. +33 175 43 76 10
London
23 Hanover Square
London W1S 1JB
T. +44 203 714 8915
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