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DSPA Insights Conference 2019
1. From AI strategy dream to the execution reality
Bruno Coelho
Analytics & AI Director @ Tessella – Altran Group
Insights Conference
19th September 2019
5. Then the seizures came. His tiny arms and
legs would stiffen for a few seconds as many
as 30 times per hour.
The story of Maverick Coltrin’s medical mystery
… just 6 days old when he stopped eating…
Doctors tried multiple tests and medications,
but nothing revealed what was wrong.
6. The story of Maverick Coltrin’s medical mystery
“Do you think he’s going to survive?”
7. The story of Maverick Coltrin’s medical mystery
“We are doing everything we can”
8. … newborn babies with unexplained
diseases, many are dying or suffering
permanent brain or organ damage.
The story of Maverick Coltrin’s medical mystery
If we can diagnose them rapidly, we can
change those outcomes …
12. The Data Science(ist) graveyard:
Fixed Project duration
Hard Commitment on results
Undetermined background
knowledge
Pre-defined team
The Nightmare
The existing data “is not
enough”
13. The Data Science(ist) emptiness:
Project Too long
No new knowledge required
Repetitive tasks
Problem not challenging
enough
18. 3 Years 3 Weeks
To generate predictive
infectious disease maps
Up to $1 million
losses incurred
per failed batch
$200 M
In saved capital
expenditure in 24
months
Dynamic Pricing
Automation of Pricing
calculation and workflow
From a A.I. Black box
to Transparent AI
2 months
66% cancelations
reduction
1,5M€ penalties saved
It there a secret ? Is there a recipe?
19. The first Use Case together: No
Project duration: Uncertain
The common denominator
Initial Commitment on results: No
Breakthrough within original team: No
Knowledge in the same Geo: Not likely
Customer involvement
YES
Customer Data Science
Knowledge
Enough
Tech or purpose play: Purpose
20. Start with a hypothesis or a purpose
Move to agile delivery methods Vs fixed scope & time
Work on several use cases at the same time, each is unique
Think flexible. Including the team members
Hire a minimum yet flexible capacity
Build
“enough”
internal
knowledge
Be open to external help, look for long lasting partners
Commit on approaches outcomes, not on results
21. This is what a Scalable Data Science team should look like
This is what a Scalable, Agile & High Performance Data Science team
SHOULD LOOK LIKE
22. This is what a Scalable Data Science team should look like
This is what a Scalable, Agile & High Performance Data Science team
SHOULD LOOK LIKE
24. “Genome sequencing was expedited by
bead-based genome library preparation
directly from blood samples and
sequencing of paired 100-nt reads in
15.5 hours.
Clinical natural language processing
(CNLP) automatically extracted
children's deep phenomes from
electronic health records with 80%
precision and 93% recall”
Can Data Science help?
25. Can Data Science help?
Did the Maverick’s
Doctors knew about it?
26. The story of Maverick Coltrin’s medical mystery
If we can diagnose them rapidly, we
can change those outcomes …
… 39 hours later …
Diagnostic arrived: a form of epilepsy
treatable with Vitamin B6
27. “If we had to wait four to six
weeks with the standard genetic
testing, Maverick would be dead.”
29. Thank you
Bruno Coelho
Analytics & AI Director
Tessella – Altran Group
bruno.coelho@altran.com
Insights Conference
19th September 2019
Editor's Notes
We are going to talk about a pawn promoted to king.
Who is that pawn?
That pawn is YOU!
You, the data science leader of today and of tomorrow
You have the power the transform your organization. The power to impact people’s lives.
We want you to have tremendous success.
We have been working with many of you.
We have are embedded in Data science for more than 30 years (even before Data Science & Analytics was a sexy area)
What has been the common denominator of our customers’ success through the years?
This is what brings us here today
To talk about your path to success
What is your biggest enemy to a sustainable success ?
The right balance between: Time, Money & Expectations
Value for money, Time to market, TCO, ROI
This is very important, of course!
In other words: a strategic plan without real-life constraints
A typical new data science leader.
Trial, a first team.
Picks up the low hanging fruit huge success
Money is saved to increase the team and a strategic plan is requested.
At that time the clock starts ticking…
What we have seen over and over again.
Data will seem not be enough for your data science team.
The team will seem not big enough, no matter the team size.
There will be “screw ups”, and sometimes the promises we make will not be fulfilled.
The insights do not seem to be clear or powerful enough for your internal clients.
The insights will seem to arrive always a bit too late for your internal clients.
Some promising ideas will not see the light of day…
A spiral of disbelief must be avoided at all costs!
And It can!
We know it is possible to have a sustainable success.
Here is how to a balance between time, money and manage expectations
This story makes me shiver …. I personally relate to this story.
This year. my kid, was 4 years old at the time.
He had symptoms of fever for 2 weeks in row.
His little body reacted by shivering and roll up his eyes, to the quick raise of temperature.
The doctors did not know the cause, so he was hospitalized.
Some day later he stopped walking, with pains in his little legs.
The Doctors were doing their best to find the drug by collecting several blood samples per day.
The expectations were managed day-by-day with the hope that he new drug would work and break the fever.
The doctors and nurses gave their best.
What was the cause, we are yet to find out….
My public thank you to Dra. Mª João Brito and all professionals at Unidade de Doenças contagiosas do Hospital D. Estefania.
Not the first use case
There was no certainty about project duration
There was no commitment with results
The Core Data Science knowledge needed to be successful was not in the initial team.
The Core Data Science knowledge was not in the same geography
The customer real-time involvement was key
Not the first use case
There was no certainty about project duration
There was no commitment with results
The Core Data Science knowledge needed to be successful was not in the initial team.
The Core Data Science knowledge was not in the same geography
The customer real-time involvement was key
Not the first use case
There was no certainty about project duration
There was no commitment with results
The Core Data Science knowledge needed to be successful was not in the initial team.
The Core Data Science knowledge was not in the same geography
The customer real-time involvement was key
What can data Science do?
What can data Science do?
Coming back to maverick
Fortunatelly the hospital was aware of the trial by Doctor Stephen Kingsmore.
Fortunatelly the doctors has access to the knowledge
Fortunatelly, Maverick’s illness was quickly diagnose and treated. Vitamin B6.
Coming back to Maverick
Fortunatelly the hospital was aware of the trial by Doctor Stephen Kingsmore.
Fortunatelly the doctors has access to the knowledge
Fortunatelly, Maverick’s illness was quickly diagnose and treated. Vitamin B6.
My son ?
I suspect that my son’s Doctor was a data scientist.
After experimenting several drugs and several tests.
My son’s Doctor recommened the correct one.
The fever broke down in just 1 day.
My public thank you to Dra. Mª João Brito and all professionals at Unidade de Doenças infecto-contagiosas do Hospital D. Estefania.