The journey of becoming a data science company is more about the culture and thinking, rather than hiring and up-skilling individuals. In Novartis, while we are hiring data scientists and spending a lot of time in training and learning related to data science, the destination for us is one of cultural change, which is required to make us a data science company. Head of the AI Hub Dublin, Ashwini Mathur will share practical insights into the Novartis journey and how each employee plays a part. He will talk about the value of using the language of data science throughout the organisation and how this takes them one step closer to becoming a data science company.

1. Data Science Company or
Company with Data Scientists
Ashwini Mathur
Novartis Ireland

2. All views are personal and do not reflect the views of Novartis.

3. Table of contents
Intro
1 Background
Building a Data Science Team
Building a Data Science Company
Culture and Digital Transformation
Discussion2 5
4
303
04
05
06
07
08
Ashwini Mathur | 03

4. Novartis and Microsoft join forces to develop drugs using AI
Joint research will take place at the Novartis campus in Switzerland and
the company’s global service centre in Dublin. It will also involve
Microsoft’s research labs in Cambridge under Christopher Bishop, one of
the UK’s foremost machine learning experts.
Ashwini Mathur | 04
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Vas Narasimhan, CEO
Novartis
“We are aiming to transform how we create
innovative medicines, engage with patients
and healthcare providers, and improve
operational efficiency. Ultimately, we are re-
imagining medicine with data science and AI.”
“As an industry we have vast datasets built
from having conducted countless studies in
thousands of diseases and in some cases, like
ours, going back decades. At Novartis this is
our goldmine: our wealth of clean, curated,
longitudinal and interventional ..... integrating
into every aspect of how we approach R&D
uniquely position us to lead the digital
revolution in pharma and reimagine Novartis
as a 'medicines and data science’ company.”

5. Ashwini Mathur| 05
Statistics
"Computers are not biased, but biased people can compute”
• Use of wrong statistical / mathematical model
• Use of software which is not fit for purpose due to not having access to
best tools
• Answering an approximate business problem and thus implementing a
sub-optimal business solution despite solving the associated analytical
problem correctly
• Using data which was collected for a purpose other than the business
problem at hand
• Using too much data or too little data
• Over interpreting the results by showing selective data
• Making the final story either too clear or too obtuse
Storytelling – Contextual, Passionate, Engaging
• Based on a real world problem
• Timely not dated
• Using cutting edge science and latest data
• Using models, data, visuals which are stimulating
• Covering all angles including disadvantages, shortcomings and future work
• Concise and exact, does not utilize adverbs and flowery language
• Able to hook audience with a provocative scenario
• Humorous in presenting explanations and arguments
• Able to ask audience on their interpretations
Data
Science
Algorithms
Data
Wrangling
Math
Statistics
Domain
Expertise
Visualization
Story Telling
Data Science Team – Expertise
Distributed but needs to work like a
Jazz Ensemble

6. Ashwini Mathur | 06
Data Science Company
How do you become a Data Science Company
instead of a company with Data Scientists?
• More about the culture and thinking rather than
hiring and up-skilling individuals.
• Senior leaders, managers and every individual
needs to go through this journey
• Driven by a culture which is “Unbossed”, driving
”curiosity” and making each one of us “inspired”
• Open environment
• An open learning culture
• Innovation mindset
• Support for risk taking
Examples of Data Science Talk
"my data analyses has got a bias due to the way the
data was collected but one thing that I assured was
that we had an appropriate sample size for the
analyses and we had a good understanding of what
a meaningful effect size is"
“can you do this analyses which can give some ideas
on patient population which is not being addressed
today, but make sure that the data that you use is fit
for purpose and your own biases are
acknowledged"
“I worry about the small effect size"
“I worry about the interpretation. While I am
excited about the insights, not being able to
understand the reasons why, makes me
uncomfortable”

7. Ashwini Mathur 07
Digital Transformation
• Digital Lighthouse Projects
• Partnerships
• Biome
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consequent.
Leadership in the data and digital age
• Ability to combine business knowledge and data based insights to take
decisions. This requires leaders to openly share their decision making
journey. If not done, people in the organization, due to data being
available to them, will challenge the decision, specially if it is contrary to
what data is suggesting.
• Ability to take decisions under uncertainty. Data science provides
insights but uncertainty and risks, driven by biases,
completeness/accuracy of data and the correctness of question, are
present. Ability to think statistically helps.
• Ability to avoid over-analyses resulting in decision paralysis. Availability
of data lends itself to unending analyses and this needs to be avoided.
The first two behaviours will help with this.
• Walk the data science talk - Technology to become mainstream requires
everyone to talk the "technical language". Leaders in this data age
should talk the real data science language, beyond the "dumbed" down
versions of it.
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Culture

8. Ashwini Mathur | 08
Discussion
Ability to select objectively “Approximate answer to a
correct question or a correct answer to an
approximate question.”
Visual presentation of problems and solutions, ability
to tell a story that transcends differing knowledge
bases
In-built transparency
Democratization of data and tools including AI/ML
Culture as important as Technology
Data Science Talk as Leadership very important
Partnerships driving Transformtion
Helps manage scientific humility and makes skeptics
of all of us

9. Thank You
Q & A