Platform Strategy and
Data-driven development
in Pharmaceutical Industry
Lukas Ott - Domain Architect R&D
ITU - Guest Lecture - 8 april 2022

Agenda
1. Pharmaceutical Research & Development 101
2. From Enterprise Architecture to platform strategy execution
3. Discussion on use cases development for data analytics

Pharmaceutical Development in a Nutshell
Pharmaceuticals
Small
Molecule
(Chemical)
Large
Molecule
(Biologics)

The core of R&D in Drug Development
Target
Identification
Drug
Discovery

From discovery to approved product

From Enterprise Architecture to
Technical Architecture
Source: https://www.leanix.net/en/wiki/ea/enterprise-architect-vs-solution-architect-vs-technical-architect-whats-the-difference#form

IT portfolio is a balance between operative and
strategic development

IT Portfolio Management

Architecture review is a key milestone of the
analysis phase of projects

Key architectural principles
1. Business
2. Application
3. Information
4. Technology
5. Security & Compliance / Data Privacy

Core functionalities of a system in an enterprise
Data
Documents
Workflow
Business Process
Application /
Platform

Global Platform Strategy as Foundation
Adopt before Adapt
“A platform strategy is the mobilization of a networked business
platform to expand into and operate in a given domain. A business
platform, in turn, is a nexus of rules and infrastructure that facilitate
interactions among network users.”
In R&D Domain that means that the goal is to build foundational
platform that are supporting the business processes end-to-end.

R&D Domain - Background
● Use the right datasets - data quality is key!
● Regulatory Authorities
● Content Accumulation
● Leverage Available Content
● Know who does what

Use cases in Pharma
Simplify Compliance
● Detect relevant information in
regulatory texts
● Push information to the right
expert
Optimize Clinical Trials
● Make evidence-based decisions
based on all clinical trial data
available
Stay on Top of Research Trends
● Provides access to the latest
scientific information
Accelerate Time to Market
● Speeds up new drug application
submission
● Reduces costs for new drug
development
Connect to External Experts
● Connect to relevant experts
within external research
organizations
● Identify the most promising
collaboration partners
Connect Experts & Expertise
Internally
● Reveals networks of experts

Natural Language Processing (NLP) /
Machine Learning (ML) Use Cases
1. Business case
a. Create data stories
b. Main concepts
2. Build data pipelines
a. Prerequisites
b. Indexing data
c. Create service & label data

Questions & Open Discussion

Sources
https://de.slideshare.net/LukasOtt/
https://www.leanix.net/en/wiki/ea/enterprise-architect-vs-solution-architect-vs-technical-
architect-whats-the-difference#form
https://towardsdatascience.com/data-driven-isnt-dead-3230378e4c7a

From discovery to approved product