Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)


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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.

Published in: Business, Technology
  • Great presentation! I especially like how you brought in the genome sequencing cost drop to visualize the information flood challenge.
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Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)

  1. 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. 2. Who is Infosys? Over150,000EmployeesFrom89Nationalities Operationsin77CitiesAcross32Countries
  3. 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. 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. 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. 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. 7. Big Data Will Enable Smarter Healthcare 7
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 21. BigDataEdge “Intestines” 21
  22. 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
  23. 23. © 2013 Infosys Limited, Bangalore, India. All Rights Reserved. Infosys believes the information in this document is accurate as of its publication date; such information is subject to change without notice. Infosys acknowledges the proprietaryrights of other companies to the trademarks, product names and such other intellectual propertyrights mentioned in this document. Except as expresslypermitted, neither this documentation nor anypart of it may be reproduced, stored in a retrieval system, or transmitted in anyformor byany means, electronic, mechanical, printing, photocopying, recordingor otherwise,without the prior permission of Infosys Limited and/or any named intellectualpropertyrights holdersunderthis document. Thank You