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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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[email protected] 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