While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack. Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.

1. Big Data Trends—A Basis for
Personalized Medicine
Dr. Hellmuth Broda, Principal Technology Architect
eMedikation: Verordnung, Support Prozesse & Logistik
5. Juni, 2013, Inselspital Bern

2. Who is Infosys?
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3. Infosys helps clients look into
the future for opportunities
Innovation is
obviously crucial
but there is an
innovation gap
that we can
help to bridge
through
co-creation
They know that the technology
that built their success will
not be relevant tomorrow
Our research shows 78% of banks
know innovation is vital, but only
37% have innovation strategies
Using every Infosys capability
and IP, we help our clients exploit
opportunities for growth
By pursuing innovation alongside
clients it is easier to help unlock
the potential of their technology
3
Photo by Infoscion Shailendra Tawade
Looks to the future for opportunities

4. We Are in the Midst of an Information Tsunami
• Mankind is generating more data in two days than we did from the dawn of man
until 2003 (Eric Schmidt, Google)
• Information is doubling every 18 months
4
But this report did not include
genome sequencing:
Source: IDC White Paper: The Diverse and Exploding Digital Universe
Source: National Human Genome Research Institute

5. What is “Big Data”
• The global information explosion does not exclude the
medical community
• In the past we used “Data Warehousing” to make data available
for new quests
• But Data Warehouses are limited to one compute center and (often) one server
• Data Warehouses need structured tabular information
• But most of the information is free text
• Big Data approaches allow to tap the gold nuggets hidden in the haystacks of free text
information
• Some believe medicine can become more of a science, rather than a practice, by
relying more on technology
• By combining data from many current and future devices and from different records,
healthcare providers can have greater insight into medicine
• This could generate thousands of data points about a person’s health and lead to
faster and cheaper correct diagnoses
5

6. Promises of Big Data
• More and more data can be gathered to identify patterns and
physiological interactions
• Machine learning software can point to abnormalities and predict health issues
• Smart phones and “iAnythings” will empower the patient to monitor his health
• Big Data analysis can help to move from corrective to preventive medicine
• Doctors will increasingly rely on technology and Big Data for triage, diagnosis and
decision-making
• Consequently, doctors will perform better, health care costs can decrease, and
patient care can improve
• Pharmaceutical companies when screening large clouds of Big Data can find and
corroborate seemingly weak correlations and target medicines to subgroups of
patients with similar genetic backgrounds
6

7. Big Data Will Enable Smarter Healthcare
7

8. Challenges Faced by Managing Big Data
• Explosion in Volume of Data Available for Research
– Expansion into genomics, proteomics and metabolomics research has
resulted in an explosion of data across the research organizations
– Advancements into Biomarkers and Imaging biomarkers have added to the existing data
volume and need for high throughput computing
– Availability of data from the public domains and collaborators for research have added to the
current data management issues
• Existence of Data Silos
– A large amount of this data resides in data silos across the global sites.
– In addition, translation and personalized medicine based drug development requires an
integration with clinical and real-world patient and physician data which are spread across
disparate silos across the globe
• Infrastructure challenges to meet the high throughput computing needs
– With the increase in computing and collaboration needs, IT infrastructure strategy needs to
adapt to keep up
8

9. Leveraging Value from Big Data
• Integrate data siloes across the research organization to allow
scientists easy access to the wealth of information available
– Also the ability to capture and ingest data from multiple structured and unstructured data
sources
• Develop and place flexible and broad high throughput analytical layers on the
integrated data sets to mine the information
– Continue to allow the researchers the flexibility of choosing a broad array of analytical tools that
fit there research needs
– Advanced near real time analytics over Big Data combining structured and unstructured data
– Ability to process data on cloud architecture and cloud platforms
• Develop scalable and flexible infrastructure to support the increasing computing
needs.
– Bioinformatics is increasingly needing Tera-Scale computing in the areas of drug discovery and
drug repurposing
9

10. Big Data in Life Sciences*
10
Big Data Analytics
Sequencing and gene
expression data
Drug data (pathways,
structure etc.)
Clinical Trial & Hospital
Electronic Record data
In vitro diagnostics
& X-ray results
Patient self-reported and
social media data
Drug repurposing
New biomarkers
New drug targets
Personalized
healthcare
Adverse event
detection
Etc.Telemedicine data
*This and the following nine slides contributed by Edward Currie, AVP Life Sciences, Infosys

11. Big Data in Life Sciences—Example Use Cases
11
Drug
Repurposing
Treatment
Adherence
(Compliance)
Health record-
guided drug
development
Adverse Event
Detection
Next
Generation
Sequencing
Patient Pre-
Profiling
Personalized
Healthcare
Influencer
Profiling

12. Drug Repurposing
12
Compound structures
Transcriptome signatures
Drug/protein interactions
Side-effect profiles
Mechanisms of action
Understand
which new
diseases are
most likely to
be treatable
with a drug
Drug Repurposing
BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

13. Healthcare Record Guided Drug Development
13
+ Healthcare record data
Drug data as for DRP
Understand
outcomes of patients
who have been
treated with drugs of
similar profile to that
of the drug of
interest
BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED
Identify patients for clinical
trials
Predict “real-world” efficacy
& safety
Reiterate drug design to
optimize efficacy & safety

14. Next Generation Sequencing
14
Outcome data
(clinical trial or hospital)
Sequencing data
Understand
which gene
mutations are
associated with
which diseases
BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED
New drug targets
New biomarkers

15. Personalized Healthcare
15
Understand
which patients
respond to
which drugs
Personalized Healthcare
BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED
Diagnostic test (Dx) data
Prescription (Rx) data
Sequencing data
Outcome data
(clinical trial or hospital)

16. Compliance (Treatment Adherence)
16
Social media analytics
Prescription (Rx) data
Understand
why patients
don’t take their
medication
correctly/fully
Treatment Adherence
BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

17. Adverse Event Detection
17
Social media analytics
Prescription (Rx) data
Identify
otherwise
unreported
drug side-
effects/
interactions
Drug safety
BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED

18. Patient Pre-Profiling
18
Identify patients
eligible and willing to
take part in clinical
trials, and who have
elected to be gen-
etically profiled
Clinical Trial Subject
Pre-Profiling
BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED
Social media analytics
Prescription (Rx) data
Personal genomics data

19. Influencer Profiling
19
Identify the key
influencers among
different customer
groups: key opinion
leaders; prescribers;
patients
Influencer Profiling
BIG DATA ANALYSED INSIGHT DELIVERED STRATEGY ENABLED
Social media analytics
Prescription (Rx) data
Medical literature

20. Infosys’ BigData Edge
20
BigData-
Edge
• Agility
Actionable intelligence from
new data in hours
• Speed
Expedite your processing
deployment across all data
types by 10X or more
• Business Value
Monetize your data through
applied insights
Consumption
Full Featured Hub Management
One-Click Cloud Deployment -Seamless Cluster Setup, Configuration
Industry leading Visualization techniques for deep
insights
Integration with wide variety of industry solutions
Industry
specific
Solutions
Enables building solutions though ETL like graphical tool—accelerating time to market while reducing
cost

21. BigDataEdge “Intestines”
21

22. Conclusion
1. Data in our world are growing exponentially
2. There is tons of hidden and useful information in those data
3. Clever methods to mine these data are necessary
4. Big Data analysis methodologies are instrumental in detecting new information
5. Infosys’ BigDataEdge is one of the most mature solutions on the market
6. Big Data will help to detect correlations between people and illnesses
7. Big Data will help in Drug Repurposing
8. Big Data will allow for patient pre-profiling
9. Big Data will help in adverse reaction monitoring and detection
10.Big Data will help progress in Personalized Medicine
22

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