GSK are a science-led global healthcare company with a special purpose: to help people do more, feel better, live longer. We have three global businesses that discover, develop and manufacture innovative pharmaceutical medicines, vaccines and consumer healthcare products. In this talk i will share our experience in the Pharmaceutical business delivering commercial analytics going from hackathon to MVP. From the initial ideas and business discussions through delivery of a hackathon as an accelerator, on to building an MVP. Using the Azure cloud platform and Databricks to rapidly ingest data and prototype. I will touch on the challenges, opportunities and learning points of the process we went through to deliver commercial analytics at scale in Pharma.

1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics

2. Peter Webb GlaxoSmithKline
Commercial Analytics at
Scale in Pharma: From
Hackathon to MVP with
Azure Databricks
#UnifiedDataAnalytics #SparkAISummit

3. Introduction
• GSK are a science-led global healthcare company. Our
purpose is to help people do more, feel better, live
longer
• 3 global businesses
– Pharmaceutical medicines, vaccines and consumer
healthcare products.
• Our goal is to be one of the world’s most innovative,
best performing and trusted healthcare companies
3#UnifiedDataAnalytics #SparkAISummit

4. Initial Concept
4
Cx board
conversation
Hackathon idea
Senior leadership
support from both
business & tech
Business
challenge
development

5. Hackathon prep
5
Business
challenges - Clear
aims
Local business and
tech teams engaged
Data prep - File per
business challenge
Demonstrate art of
possible

6. Hackathon prep
• Mixed teams
– What – Business owners
– How – Data scientists
– Why – FLP (graduates) trained in Product concept
• Strategic business partners
6

7. Hackathon Tech
7
Azure Data
Lake
Data
Sources
Azure
Databricks
PowerBIAzure Blob
File per
challenge

8. Hackathon Event
• Mixed teams
• Read backs at lunch and end of
day
• No Powerpoint
• Senior leadership support &
attendance
• Follow up agreed in advance
• Enthusiasm
• More data……..
• Prep teams more in advance
• Art of possible –
– Base working solution
– Business teams
– Thunder Shiviah Databricks for
his use of a Facebook Algorithm
8
Out of 4 business challenges 2 accepted
On the day Learning points

9. Road to MVP
• Focus on thread e2e
• Business involvement with agreed aims
• Three 2 week Sprints
• Data ingestion – limited sets
• …..yet still code with future in mind
9

10. MVP Tech
10
Azure Data
Lake
Data
Sources
Azure
Databricks
PowerBIAzure Data
Factory
Raw
Foundation
Enriched

11. Scaling
• Building on single e2e thread
• Data ingestion – increase date range, volume,
sets
• Automation of pipeline
• Model complexity
• Outcomes focused backlog
11

12. Summary
• Business engagement
• Buy in at all levels
• Ruthless focus on single e2e thread
• Code discipline
• Art of possible
12

13. DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
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