Transforming Big Data into Big Value


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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

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Transforming Big Data into Big Value

  1. 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. 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. 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. 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. 5. … including Semantic Technology to enrich Big Data with Insights and Expertise 5 | ©2013, Cognizant
  6. 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. 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. 8. Overcoming Barriers and Getting Started – People, Data, Technology & Delivery
  9. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 and existing investigator database  Identified high performing existing investigators  Plan additional sites visits  Quick start new campaigns
  22. 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. 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. 24. Achieving Big Value by Transforming the Customer Experience
  25. 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. 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
  27. 27. We are at an Inflection Point at which Value is Created or Destroyed Source : The Motley Fool 27 | ©2013, Cognizant
  28. 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; 28 | ©2013, Cognizant
  29. 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. 30. Analytics drives both Cost Containment and Revenue Uplift across Industries 30 | ©2013, Cognizant
  31. 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,” 0the%2021st%20Century.pdf. 31 | ©2013, Cognizant Design Sell
  32. 32. Thank you ©2013, Cognizant
  33. 33. References 1: 2: 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: 5: 33 | ©2013, Cognizant
  34. 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. ©2013, Cognizant