In this presentation, you will learn how to transform a Big Data initiative into a realized, measurable ROI: • Understand the complex mix of business expectation, hype, reality, and new information source opportunities in the Big Data space • Use the Business Case process to help to you identify what you can achieve and what is not yet ready • Build communities of interest around prototypes and plan for success for your company’s advantage • Learn how to industrialize your Big Data innovations to achieve measurable, sustainable benefits

1. Transforming Big Data
into Big Value
Sep 18, 2013
Speaker
Thomas Kelly
Practice Director
Enterprise Information Management
Cognizant Technology Solutions, Inc.
©2013, Cognizant

2. Big Data – Yesterday, Now and Tomorrow
Data in Research has grown considerably in the past few years …
Biological
Laboratory
Social Media
25-50M
400per day
mn
tweets
162,632
terms in eLab
Notebook pdfs for
typical large pharma
2 | ©2013, Cognizant
Healthcare
1
tweets on asthma in
just the last 10 months
Doubled
Doctors and hospitals’ use
of health IT since 20122

3. Many of the Opportunities are not new but some are…
• Traversing from Data to Knowledge continuum is not a new challenge in Life Sciences…
• Dealing with complex, dynamic, large and rapidly-growing data sets is not new either…
 Genome sequence
 Number of Base pairs
 Omics data
 High Throughput Screening
 Next Generation Sequencing
Volume
Velocity
Variety
Complexity
 Chemical structures
 Gene expression data
 Microarray data
 Realtime data from social
networks
 External data from EHR
 Pipeline analysis
 Computational Modeling
 Statistics
Our primary focus has, however, been on managing and analyzing data individually…
3 | ©2013, Cognizant

4. Also New is a Set of Tools to Tackle the Challenge…
Open source Distributed Processing Frameworks
Big Insights &
Streams
Big Data
Appliance
HANA
Big Data Analytical Applications
Packaged Big data platforms
Data Visualization,
memory Analytics
Statistical
&
In-
MPP Data appliance Platforms
Big data Integration
Translational Research specific tools…
4 | ©2013, Cognizant
TRANSLATIONAL RESEARCH CENTER

5. … including Semantic Technology to enrich Big Data with
Insights and Expertise
5 | ©2013, Cognizant

6. Example: Type 2 Diabetes Research using Semantic Technology
Mayo Clinic used Semantic Web technologies to develop a framework for high throughput
phenotyping using EHRs to analyze multifactorial phenotypes
1
4
Diseasome
Mapped Clinical Database
to Ontology Model
DBPedia
ChemBL
Find Genes or Biomarkers associated
with T2D, as Published in the Literature
2
5
RxNorm
DailyMed
Clinical DB
Find All FDA-approved T2D Drugs;
Find All Patients Administered these Drugs
Diseasome
RxNorm
ChemBL
DrugBank
Selected Genes have Strong Correlation to T2D. Find All Patients
Administered Drugs that Target those Genes.
3
RxNorm
SIDER
Find Which of these Patients are having a
Side Effect of Prandin
Clinical DB
Diseasome
RxNorm
ChemBL
DrugBank
Clinical DB
Find All Patients that are on Sulfonylureas, Metformin, Metglitinides,
and Thiazolinediones, or combinations of them
Reprinted with permission from Jyotishman Pathak, Ph.D., Mayo Clinic
6 | ©2013, Cognizant
Clinical DB
6

7. Is the Juice worth the Squeeze?
Cost Containment
ACOs
 Cost Reduction through better Trial Design & Execution
 Cost Avoidance through Better Patient and Study Selection,
Retention & Adherence
Payers
Improved Patient Outcomes
 Personalized Medicine is more attainable and affordable
 New insights into Disease & Mode of Action
Data
Marketers
Improving Regulatory Compliance
Providers
 Reduced effort for compound screening and competitor
intelligence
 Improved Trust through Data Traceability
Device
Manufacturers
7 | ©2013, Cognizant
Faster Time to Analysis Results
 Reduced the time required to conduct gene-environmental
interactions analysis by 99 percent, from over 25 hours to
under 12 minutes3
Regulators

8. Overcoming Barriers and
Getting Started – People,
Data, Technology & Delivery

9. Driving Big Value
Create Communities of Interest
Select Area of Focus
Define Value Objectives
Plan and Execute
Measure and Publish Results
9 | ©2013, Cognizant

10. Interest
Build an Environment for Success
Executive
Leadership
Business
Stakeholders
Integrate new and existing data
to rapidly stimulate new
insights about customers,
products, and markets
Champions
Advisors
Business Experts
Technology Experts
Benefits Owners
Information
Technology
Extend the footprint of existing
technology assets; reduce the
overall cost of operations;
eliminate high cost, low value
10 | ©2013, Cognizant
infrastructure
Create opportunities for
products and services that
transform the organization’s
role and position in the
marketplace
Partners
Create value that
cannot be achieved
alone

11. Focus
Big Data Opportunities in Pharma R&D
Drug Discovery
Clinical
Development
Drug Safety
Regulatory
Genomic Technologies
Disease & Mechanism of Action
R&D Business
Development
Predictive Sciences
Translational Medicine
 New Market
Identification
 CompetitorCompound
Profiling
Regulatory
Monitoring
Imaging
Drug Repositioning
Investigator Selection
& Profiling
Patient Selection
Safety Reporting
from Social
Media
Healthcare Data Mining
11 | ©2013, Cognizant

12. Focus
Big Data Focus in Pharma R&D
Innovation Enablers
(Improved Patient Outcome)
High
 Predictive Sciences
 Translational Medicine
Business Value
 Genomic Technologies
Operational Excellence
(Cost containment)
 Drug Repositioning
 Investigator Performance & Patient
Selection
 Mine Healthcare Data
R&D Process Context
(Compliance)
 Safety Reporting from Social Media sources
 Regulatory Monitoring
 Compound Profiling
Low
12 | ©2013, Cognizant
Maturity
High

13. Define Your Value Objectives
Objectives
Establish clear success criteria and SMART metrics (Specific, Measurable,
Attainable, Realistic, and Traceable) to prove ROI
Revenue enhancement (increase revenues by $5M in the first y months)
Cost reduction (source, commitment)
Operational efficiency (reduce analytics cycle time by 90%)
Increase market share
Scale
Globalize a local activity, capability, or product
Collaboration between business and IT
Prioritizing benefits realization
13 | ©2013, Cognizant

14. Execute
Big Data Strategy
Business
Strategy
• Establish Governance
Model
• New Business Models &
Organisational Impact
Data
Strategy
• Include Data
Access, Integration,
Quality and
Curation &
Analytics
• Identify Service
Provision across
Data, Analytics &
Technology
14 | ©2013, Cognizant
Technology
Strategy
• Include R&D
platforms, Big Data
strategy, Analytics
& Visualization
Delivery
Model
• Robust servicesbased delivery
model
• Include
Experimentation
approach using
Lab-on-hire

15. Business Strategy
Data Strategy
Technology Strategy
Delivery Model
Create a Data-Driven Focus
Identify Patient
Population
Warning Letters
Disease of Interest
Inspection
Sentiment
Geography
Rare Diseases
Performance
Metrics
Patent
Clinical Trials
Social Media
Publication
Unmet
Need
Current
Collaboration
Key Opinion
Leader
Journal
Research Focus
Conferences
Peer Reviews
Unmet Need
Expert? (based
on confidence)
Investigators
Geography
Academia/Pharma/
Biotech?
Working
with
competitors
?
Emerging Countries
Therapeutic
Areas
Collaboration
Identify Patient
Population
Research Focus
BRICS
Clinical Trials
15 | ©2013, Cognizant
KOLs working on DPP IV inhibitors, based in emerging markets with positive performance
metrics and publications in journals, conferences and social media
China

16. Business Strategy
Data Strategy
Technology Strategy
Delivery Model
Health Data Integration using Semantic Technology
Intelligent Health Data Integration Technology Stack
Health Data Exchange Technology Stack
on Semantic Technology
CDISC
Expert Knowledge
PRM
Entity Resolution
CDASH
Patient
Behavior
Data
ODM
SDTM
ADaM
SHARE
SEND
Patient
Privacy
Data Virtualization
Nutrition
Data
Linked Data
Lifestyle Data
Data Federation
CDA
CCD
RIM
16 | ©2013, Cognizant
Epidemiology
Data
CCOW
HL7
QRDA
GELLO
ICSR
SPL
Provenance

17. Business Strategy
Data Strategy
Technology Strategy
Delivery Model
Technology Reference Architecture
Linked
Data
Source Systems
Ontology
Models
Data Acquisition
Channels
Data Virtualization
and Federation
Inferencing and
Embedded Expertise
Data Integration and Quality Hub
Natural Language
Processing
Data Storage and Repository
Databases
ODS/Staging
Files
Standard Interface for
Database [JDBC]
Web Services
Standard Interface for Files
[FTP/SFTP/CP/RCP]
Semantic
Technology
Integration
EDW
Data Marts
CDC Engine
(Optional)
Sqoop
Map Reduce Processing
Routines
Subject Area
Specific
Marts
External
Data /
RWE
e.g.
•
Thomson
Reuters
•
i3 InVision
•
Wolters
Kluwer
•
•
GPRD
…
Standard Interface for
Web Services
[SOAP/WSDL]
Sqoop/ Java
Programs
Data Audit and
Certification
Data Security
Hub
Data Delivery
Hub
Data Control Access
Data Extract Jobs
ODBC Pull
Through
Web Services
Data Governance
Innovation Services
Technology Services
17 | ©2013, Cognizant
Automation Tools
Published
Reports
Adhoc
Reports

18. Business Strategy
Data Strategy
Technology Strategy
Delivery Model
Experimental Evaluation Model
Data Sources
New Opportunity
New Technologies
New Data Sources
New Stakeholders
New Processes
• Review scale up potential
• Generate idea
• Enumerate opportunity
• Technical assessment
• Refine opportunities as
needed
• Review Design Concept
• Go/No Go Decision
• Pilot created
• Users informed
• Production project formed
• Performance optimization
• Additional requirements
• Business process redesign,
if needed
• Training and roll out
18 | ©2013, Cognizant
• Review Design Concept
• Go/No Go Decision
• Pilot created
• Users informed

19. Execute
Leverage Insights and Expertise,
Rapidly and Sustainably
Identify and leverage
existing, relevant data
assets and expertise
Ingest new data
sources (light
integration and
curation)
Reuse Expertise
Analyze
Monitor and measure
use and benefits
achieved; identify next
set of priorities
Realize
Benefits
Extend
Create and extend
data relationships,
leveraging insights
from previous study
cycles
Govern
Elevate study-proven
data, relationships and
expertise to organizationwise definition
19 | ©2013, Cognizant
Refine
Capture insights from
new study cycles,
refining relationships
to support new
analyses

20. Example #1: Epidemiology Analytics and
Patient Cohort Analysis at Global Pharma
MarketScan
I3 Invision
DataMart
Business Need
 De-identified patient data is
provided by third party data
providers
 Datasets can range from 500 GB
to 2-3 TB
 SAS analysis can take more than
10 hours due to the complexity
of the processing.
 Preparation of the control and
analytic datasets can take up to
several days
20 | ©2013, Cognizant
Results
Solution
 Hadoop-based solution
developed to leverage its
parallel processing capabilities
 Pig used for converting the
datasets from multiple
providers into a common format
 Python used for applying the
algorithms for the cohort
analysis
 Analysis results stored in Hive
for querying and analysis using
SAS
 Use of HBase and Solr for fast
search
Benefits
 Understanding of prevalence of
secondary conditions
 Better understanding of disease
market
 Improved trial design
 Real time search of over million
records in 2.5 seconds
 Reduced processing time of
Epidemiology analytics to 20
minutes

21. Example #2: Investigator Performance and
Selection Analysis
Business Need
 Assess performance based on
FDA inspections (10-20,000
unstructured documents)
 Identify and select
investigators and sites across
various geographies having
experience in specific
therapeutic areas
21 | ©2013, Cognizant
Solution
Results
Benefits
 Extracts information from FDA
inspection reports
 Auto-categorization results
based on performance
 Provide summary for users to
review
 Selection of potential
investigators based on
integration with Clinical
Trials.gov and existing
investigator database
 Identified high performing
existing investigators
 Plan additional sites visits
 Quick start new campaigns

22. Example #3: Building a KOL Network
Business Need
 Build a network of high
performing investigators and
partners to improve trial
performance and establish
thought leadership
Solution
Benefits
 Semantic integration of data
from external and internal
sources
 Manual curation and delivered
as actionable insights
 Monitor new trends and
provide alerts and dashboards
 Be on the cutting edge of
science and identify new focus
areas
 Assign a confidence level to
each of the elements being
tracked
 Early to market
 Data mart that will enable
complex analytics and
visualization
22 | ©2013, Cognizant
Results
 Planned new market entry
 Identified partners for rare
diseases in new/existing markets
 Quick start clinical trials with a
master list of investigators
 Tracked and profiled new/existing
partners

23. Industrializing Your Big Data Project Outcome
Transforming an innovation project into a repeatable, sustainable, and valueproducing participant in your business processes
Build Industrialization Support with Stakeholder Community
Present Achieved Benefits, Manage Expectations, and Update Goals
Analyze
Verify End User Expectations and SME Requirements
Elaborate/Validate the Business Context
Refine Project Goals and Value Objectives
Evaluate Technologies (Performance, Process Automation)
Data Provisioning and Organization, including New and Additional Data Sets
Reuse Opportunities – Extending the Solution to a Larger Audience of Users
Sun-Setting Opportunities – Additional Cost Take-out
Align
Verify Data Set Quality Processes
Catalog and Share Data
Achieve
Build and Verify Repeatable Process(es)
Educate and Support the Users of the New Process(es)
Regularly Measure and Report Achieved Benefits
23 | ©2013, Cognizant

24. Achieving Big Value by
Transforming the
Customer Experience

25. Enhance the Customer Experience
Ingestible chips will help
manage Heart Failure,
Central Nervous System
Conditions, Transplants
≈25% of all heart failure patients
Have You Taken Your Chip Today?
re-admitted within 30-days due to
complications and difficulty
“… digital medicines will help heart failure patients
following challenging care
stay in control, in better communication with their
regimens
clinicians …” 4
Digital medicines will provide care givers and
pharma with more insight into how the patient is
assimilating and responding to their medication
25 | ©2013, Cognizant

26. Take an Active Role in the Customer Experience
Fifty six percent of companies are making digital
engagement of customers a top strategic priority,
and linking this to high projected returns. 5
Smart Toothbrush and
Digital Mirror
26 | ©2013, Cognizant
http://realitypod.com/2011/12/brushing-teeth-and-diagnosing-problem-made-easy/

27. We are at an Inflection Point at which Value is
Created or Destroyed
Source : The Motley Fool
27 | ©2013, Cognizant

28. “Meaning Makers” are Emerging
Meaning Makers combine data and analytics to tell a story, and
then apply that story to business decisions
• Of the 300 firms studied
• 26% are “Meaning-Makers”
• 50% are “Data Explorers”
• 24% are “Data Collectors,” (lagging
significantly)
• “Meaning Makers”
• Significant data integration
• Value attributed to analytics
• Self report they are ahead of industry peers
Image: Joan M Mas; http://www.flickr.com/people/dailypic/.
28 | ©2013, Cognizant

29. Meaning Makers Get Economic Benefit
11.3% ˗ Boost in revenue
10.7% ˗ Reduction in cost
Over the past year…
That’s 9.9% more than Data Collectors.
Cognizant study done with Oxford Economics, 2013
29 | ©2013, Cognizant

30. Analytics drives both Cost Containment and
Revenue Uplift across Industries
30 | ©2013, Cognizant

31. Focus on the Process and How Your Product
Engages the Customer
• Find the most squeaky wheels
within your process anatomy
• Look for processes that shape >20%
of cost or revenue
Think
Build
Count
• Redefine moments of engagement
(internal and customer-facing)
Run
See “Build A Modern Social Enterprise To Win In The 21st Century,”
http://www.cognizant.com/Futureofwork/Documents/Build%20a%20Social%20Enterprise%20for%2
0the%2021st%20Century.pdf.
31 | ©2013, Cognizant
Design
Sell

32. Thank you
©2013, Cognizant

33. References
1: https://blog.twitter.com/2013/celebrating-twitter7
2: http://www.hhs.gov/news/press/2013pres/05/20130522a.html
3: Substantial data analysis improves gene-environmental correlation identification to help develop new treatment for
multiple sclerosis, State University of New York (SUNY) Buffalo
4: http://www.proteus.com/future-products/therapeutic-areas/
5: http://www.mckinsey.com/Insights/Business_Technology/Bullish_on_digital_McKinsey_Global_Survey_results
33 | ©2013, Cognizant

34. Speaker
Thomas (Tom) Kelly
Practice Director, Enterprise Information Management, Cognizant
Thomas is a Practice Leader in Cognizant’s Enterprise
Information Management (EIM) Practice, with over 30
years of experience, focusing on leading Data
Warehousing, Business Intelligence, and Big Data
projects that deliver value to Life Sciences and related
health industries clients.
Thomas.Kelly@cognizant.com
©2013, Cognizant