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The evolving data science center for the intelligent future

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Forward-Looking Statement This presentation includes “forward-looking statements” within the meaning of the safe harbor provisions of the United States Private Securities Litigation Reform Act of 1995. These statements are based upon the current beliefs and expectations of Merck’s management and are subject to significant risks and uncertainties. There can be no guarantees with respect to pipeline products that the products will receive the necessary regulatory approvals or that they will prove to be commercially successful. If underlying assumptions prove inaccurate or risks or uncertainties materialize, actual results may differ materially from those set forth in the forward-looking statements. Risks and uncertainties include but are not limited to, general industry conditions and competition; general economic factors, including interest rate and currency exchange rate fluctuations; the impact of pharmaceutical industry regulation and health care legislation in the United States and internationally; global trends toward health care cost containment; technological advances, new products and patents attained by competitors; challenges inherent in new product development, including obtaining regulatory approval; Merck’s ability to accurately predict future market conditions; manufacturing difficulties or delays; financial instability of international economies and sovereign risk; dependence on the effectiveness of Merck’s patents and other protections for innovative products; and the exposure to litigation, including patent litigation, and/or regulatory actions. Merck undertakes no obligation to publicly update any forward-looking statement, whether as a result of new information, future events or otherwise. Additional factors that could cause results to differ materially from those described in the forward-looking statements can be found in Merck’s 2016 Annual Report on Form 10-K and the company’s other filings with the Securities and Exchange Commission (SEC) available at the SEC’s Internet site (www.sec.gov).

Major Therapeutic Areas • Cardiovascular • Diabetes & Obesity • Infectious Disease • Neurosciences • Oncology • Respiratory & Immunology • Vaccine-preventable diseases • Women’s Health & Endocrine

PRESCRIPTION PHARMACEUTICALS & VACCINES

• Livestock • Poultry • Companion Animal • Aquaculture

ANIMAL HEALTH

A Quick Look at Merck *MSD outside USA

$39.8B in sales* 68,000 employees*

* Merck Press Release as of Dec 31st 2016

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2012 Start-up Phase Build credibility fast

2013 Growth Phase Create Exponential Business Value; Large volume of project delivered

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Data Science 1.0 – “Quick Wins” from Small Analytics Team

Maximizing ROI: Start Up Phase

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2 Big-4 consultants

Partner Consultants Executive Director “sell” Analytics to marketing & finance top leadership

Senior Evangelist Analyze public data showcase capabilities

Select high impact, ease to implement projects

Use speed to awe

Quick Wins

2 Analytics professionals Start Small

SAS, Spotfire, SQL, R, Excel Use Available Tools

Created succinct dashboards answering key business questions

Deploy via IPads

Emphasize Visualizations

Business Goal: Build Credibility Fast

2012 Start-up Phase Build credibility fast

2013 Growth Phase Add Exponential Business Value; Large volume of project delivered

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Data Science 1.0 – “Quick Wins” from Small Analytics Team

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Maximizing ROI: Growth Phase (1)

Focus Functions 80%

“Marketing” Analytics 30%

Focused Impact Accelerate Pipeline Higher Order Analytics

Analytics Deliverables

Analytics Leader’s Time

• Projects came from Finance, Marketing and Strategy departments

• Projects replicable across region • Track realization of value • Break protracted projects into phases • Deploy project governance structure Predictive Models

• Embed predictive models into dashboards (regression, optimization, simulation)

• Focus on quantifying business value vs. sophistication of algorithms

25% Analytical Projects

Business Goal: Take Great Steps

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Maximizing ROI: Growth Phase (2)

Fresh Graduates

• Roll-off consultants • Tag-on enterprise graduate program

to hire 3 fresh graduates (strong quantitative skills, curious, driven)

50%

Augment Resources • Hire 1 intern, 1 contractor • Create Analytics Professional

Rotation Program • Build-Transfer to Local markets

33%

Training & Fun • Low Budget training – free seminars • Tap-on Virtual Analytics Network • Analytics meet-ups on weekends • Multiple of company events, team

outings, celebrate success

20% Full Time Headcount

Total Resources

Analysts’ Time

Expand Team Flexible Resources Upgrade Skills & Keep Happy

Business Goal: Recruit, Train, Motivate

2014 Maturity Phase Expansion with cost containment

2015 Re-invention Phase

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Discover New Frontiers Refresh Team with New Talents Devise Future-Ready Engagement Mode

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Maximizing ROI: Maturity Phase

Outsource Sustainment Work Non financial projects Create SOP, transfer knowledge Develop sustainment governance (Check-list, teach-backs, escalation matrix)

Project Prioritization Say “no” to reporting requests

Project prioritized by AP President Stop Work guidance

Resist Sub-team Cultures Junior Analyst -> project leaders

Emphasize Courage & Candor

Aspire towards Analytics Mastery Kaggle/Dextra Competition Contribute to technical forums

Business Goal: Expansion with Cost Containment

2014 Maturity Phase Expansion with cost containment

2015/6

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Re-invention Phase Discover New Frontiers Refresh Team with New Talents Devise Future-Ready Engagement Mode

2015/6 - Transformation Phase

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2015/6 - Transformation Phase

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Prioritize & Go Deep on key Divisions , Key Departments

Pick Large Markets to Pilot

Maximize Business Impact

Hire talented “data people”

People & Culture Re-package Legacy work

Engagement Model

Re-invent Business Model

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Transformation Phase: People & Culture

JOE CHEN

Team Leaders

Comfortable speaking algorithms, data

architecture, business language

SARA KUSIZ

Data Scientist – Machine Learners

Passion Coding (R/Python/SAS), feature

engineering, Deep Learning, AI

JOHN DO

Data Scientist – Statistical Modelers

Econometrics, Time-Series, All Regressions, ANOVA

MARTIN KING

UX/Visualization

Design Thinking, Psychology Graphic

Designers, Tools Agnostic

TOM BRADY

Data Engineers

Big Data Stack Hadoop, MapReduce, Spark, HIVE,

MONGDB, NoSQL, SQL

Global Data Science Leader Sits in Singapore!

Global Team of 50 Data People; SG Team of 5- 20 Data People in 1.5 years (Elaborate)

Evolve Skillset or Hire: Optimizations, Natural Language Processing, Behavioral Sciences

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Prioritize & Go Deep

• Focused Markets – China, Japan, Korea, Australia

• Focused Divisions – R&D, Manufacturing, Sales & Marketing

• Focused Support Functions – Finance, IT, HR

• Direct line-of-sight to business drivers

80%

Educate & Co-Create

• Reject “requirements gathering” mentality;

• Focus on asking business questions and work on first principled needs

• Agile iteration of outputs with stakeholders

• Modularize solutions

60%

Document & Collaborate

• Detailed Documentations • Code Library & Peer Reviews • Tap into Global Network of

Specialists

20%

Team Leads’ Time

Data Scientists’ Time

Prioritize; Get Wins Educate & Co-Create Document & Collaborate

Transformation Phase: Maximize Business Impact

# of Projects

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Over-Hype of Data Science • Sell Analytics-as-a-journey • Di-emphasize descriptive, predictive, prescriptive • Lunch-&-Learns & Workshops

Overload of Legacy Work • Create Data Insights Suites

(Modularized, Weave-a-story) • Reshape conversations away

from reporting requests • If you are good at something

don’t do it for free

Buy-In from IT to get DATA • Acknowledge Dependencies & co-create roadmap to acquire, store, explore various datasets • Ensure information and privacy compliance

Transformation Phase: Right Engagement Model

Centralized Demand Planning • Common Collaborative Tools (Jira, Confluence, Stash) • Primacy by Region, avoid hegemony of ideas • Governance at Global Level, Voice from Local Level

Results - Dec 2016

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Strong

2017

Pipeline

Sustained

High

Adoption

Rates

US$980K 5 D Scientists

In-Housing

Immediate

Cost

Savings

“I’m impressed with the team’s collaborative approach in building the tool – they’ve been customer/client focused, have worked with cross-functional collaboration and with good understanding of the business. They seek to add value while driving for results” “The kind of work your guys are doing are the real assets. Based on my past experience in other companies, analytics tools are very expensive developed by 3rd party consultants. It’s great to have you guys. ”

65 users daily for 1 market

8 Large-Scale Projects

2017 Global Phase Prioritize on “Data Assets” to scale global, act local

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Data Science 3.0 – Maturing a Global Data Science Center

2015/6 Re-invention Phase Discover New Frontiers Refresh Team with New Talents Devise Future-Ready Engagement Mode

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Global Phase: Create Value at Scale

Data Solution Architecture

Future-Ready Support

Beyond Merck

• Re-engage decision makers who have changed roles and portfolios

• Answer progressively harder business questions

Continue journey with Stakeholders

• Open-source scale deployment • Validations & testing • Support model for trouble-shooting • Refresh of models (offshore or in-house)

Support Model

• Close loop data flows from acquisition, storage, transformation, modeling , outputs,

feedback; integrated platform

Build Sustainable Automation

• Collaborate with healthcare eco-system players to acquire, share non-competitive data, joint experiments on data products/services

Collaboration across Eco-System

Change Management

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Roy.goh@merck.com Director

Service Delivery/Management IT Global Innovation Network @ Singapore

Office: 6508 6091 Mobile: 9619 3933

https://sg.linkedin.com/pub/eejin-roy-goh/34/79a/86a

Thank You