BDW17 London - Abed Ajraou - First Utility - Putting Data Science in your Business

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Data Science is now well established in our businesses, and everyone considers data as a key asset and critical for our competitiveness. However, Data Science is not easy to manage, very often projects failed and the investment made is not seeing as profitable. The aim of this talk is to share the knowledge in different areas: * avoid classical mistakes in Data Science * use the right Big Data technology * apply the right methodology * make the Data Science team more efficient

Technology

Putting Data Science in
Your Business:
a First Utility Feedback
Abed Ajraou – Director of Data & Insights
& Lead Data Scientist

First Utility – Putting customers in control; saving them money
Cheaper tariffs Great service More knowledge

Driving the Success of DS Solutions : Skills, Roles and
Responsibilities

Data - the new power
Internal Data
Allow us to deliver a
better service for our
customers
Allow us to optimise the
business and give the
better price to our
customers
Allow us to give more
knowledge to our
customers

Industry
Data
Individual
Transaction-Level Data Internal Data
 Better Agility
 Data Lake and Data Warehousing in
the same platform
 Unable Data Discovery
 Collect more data
 Analyse the data with high
performance
 Next Gen of Data Visualisation on top
of Hadoop

Start with a business problem
Not considering the business outcome, it’s actually
the first reason of project failure!

Explore the data
● Exploratory Analysis by Visualizing the data

The creativity part and lot of trial /
error process.
Feature engineering
Andrew Fogg win the competition by
categorising the colours of cars.

● ML is often used in DS
● Currently, the buzz/trend ML is xgboost which gives most of the
time better result than the traditional Random Forest & Neural
Networks.
● Reason of the success? More Accurate, more efficient, easy to
use, customized and distributed.
● Need less spending time in Feature engineering but still need
some creativity.
Models to predict

Evaluation - validations
● Overfitting/Underfitting
is the biggest fear of a
Data Scientist.
● Cross validation is one
way to protect the
model to not overfit

Feedback loop
● ML algorithm is a life system …
like any life specimen, it needs cares !!!
● Learning by his mistakes, it’s the only way
to progress and to fit a real AI model.

Bad Methodology
Main reasons:
• No clear business case
• Try to create the best accurate model in the first place
• No agility
• No code version control

An iterative delivery is key
Sprint
1
Sprint
2
Main take away:
• Agility is required
• Weekly delivered is highly recommended to avoid
falling to the “tunnel effect”

Gain in Efficiency
● In the old age of BI world, we gain in efficiency by using ETL tool
rather than scripting codes.
However, ML is often associate with R/Python/Scala coding.

Dataiku Flow => enable
AML
My favorite app
The Collaborative Data Science Platform: Dataiku

Data Science is a range of skills !
It’s quite rare to get them in a single person
Source: Dsradar.com

Thank you
for your attention
Any Questions?
Keep contact:
@AAjraou

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