ONTOFORCE Talk at PharmaTec London 2019 on the Data Surrealism in times of FAIR data
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Talk given by Hans Constandt at the PahrmaTec event in London Sept 24 2019 where Hans gives an update on the value and ROI of FAIRified data in BioPharma using ONTOFORCE's Linked Data Platform DISQOVER.
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ONTOFORCE Talk at PharmaTec London 2019 on the Data Surrealism in times of FAIR data
- 1. Data Surrealism In Times Of FAIR Data A Pragmatic Approach To Generate More Value With Data Hans Constandt hans@ontoforce.com www.ontoforce.com +32 (0)477 566 102 +1 (415) 996 8988 TWITTER: hconstandt LINKEDIN: hansconstandt
- 2. hconstandt
- 3. How it All Started Source - IDC hconstandt Minimizing Big Data Complexity
- 4. The Data Tsunami Source - IDC 165 ZETTABYTES BY 2025 hconstandt
- 5. This is what data looks like THE PROBLEM hconstandt
- 6. PATIENT-CENTRIC PRECISION MEDICINE CREATES LARGE VOLUMES OF DATA Health Data 300 million books 2020 Medical Data Doubles every 73d Monitoring 1K Readings per Sec Wearables 44ZB by 2020 hconstandt
- 7. Providing a Complete, Single View of Trusted Data Helping Patients hconstandt
- 8. Big Data 4 V’s DATA VARIETY & DATA VERACITY VOLUME VELOCIT Y VARIE T Y VERACIT Y Data at rest Data in motion Data in many forms Data In doubt hconstandt
- 9. Building a Linked Data Fabric PATENTED hconstandt
- 10. hconstandt
- 11. hconstandt
- 12. hconstandt
- 13. hconstandthconstandtconfidential and not to be shared without consent
- 14. If I had an hour to solve a problem and my life depended on it, I would use the first 55 minutes determing the proper question to ask ALBERT EINSTEIN hconstandt
- 15. hconstandt A Self-Service and Collaborative Data Platform for Business Users Saved € 120M in 13 min
- 16. Management of Corporate Data Assets INSUFFICIENT MANAGEMENT OF INTANGIBLE RESOURCES Source https://www.gartner.com/smarterwithgartner/understanding-the-chief-data-officer-role/ Components of S&P 500 market value hconstandt
- 18. DATA SWAMP Data Lake Typical Realisation hconstandt
- 19. You don’t need to be a genius to do Data analytics. Data integration is the key hconstandt
- 20. the most successful project that uses deep neural networks in there company or group, 10% or less improvement was found over more traditional methods?’ At the Bio-IT World conference 2019 53% of the company’s indicated that… AI REALITY CHECK hconstandt
- 21. Poor sales ... IBM Watson Health hconstandt
- 23. 2018: enabling AI/ML functionality automating the fabric. Round A: € 4,3M hconstandt AI
- 24. Big Data Fabric 2.0 Reference Architecture hconstandt Source: Forrester
- 25. Solve scalability issues associated with massive linking & inferencing during Data Ingestion User friendliness of ETL process creation Transparency / communicability / auditability of ETL Data Quality Control / validation of data & process Auditability Data Quality Challenges User friendliness Scalability hconstandt
- 26. Streamlining Data through Pipelines for Faster Analysis hconstandt
- 32. Show me the Money hconstandt
- 33. FAIR DATA Findable Accessible Interoperable Reusable hconstandt
- 34. FAIR enough to bring value hconstandt
- 35. ROI of $3.1M 100 USERS SITE LICENSE Saving $134M ENTERPRISE LICENSE Saving $807M hconstandt
- 36. hconstandt ROI & Calculate Value
- 37. Solomon Nwaka & Robert G. Ridley. Nature Reviews Drug Discovery 2003 Time squeezing in Discovery Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM IT = Identifiied Target PT = Prioritized Target VT = Validated Target LM = Lead Molecule CM = Candidate Molecule hconstandt
- 38. Solomon Nwaka & Robert G. Ridley. Nature Reviews Drug Discovery 2003 Time squeezing in Discovery Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM IT = Identifiied Target PT = Prioritized Target VT = Validated Target LM = Lead Molecule CM = Candidate Molecule hconstandt
- 39. $3.1M hconstandt ROI & Calculate Value
- 40. Solomon Nwaka & Robert G. Ridley. Nature Reviews Drug Discovery 2003 Time squeezing in Discovery Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM IT = Identifiied Target PT = Prioritized Target VT = Validated Target LM = Lead Molecule CM = Candidate Molecule hconstandt
- 41. Solomon Nwaka & Robert G. Ridley. Nature Reviews Drug Discovery 2003 Time squeezing in Discovery Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM IT = Identifiied Target PT = Prioritized Target VT = Validated Target LM = Lead Molecule CM = Candidate Molecule hconstandt
- 42. Solomon Nwaka & Robert G. Ridley. Nature Reviews Drug Discovery 2003 Time squeezing in Discovery Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM IT = Identifiied Target PT = Prioritized Target VT = Validated Target LM = Lead Molecule CM = Candidate Molecule hconstandt
- 43. Solomon Nwaka & Robert G. Ridley. Nature Reviews Drug Discovery 2003 Time squeezing in Discovery Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT = Identifiied Target PT = Prioritized Target VT = Validated Target LM = Lead Molecule CM = Candidate Molecule IT PT VT LM CM Target Identification Target Selection Target Validation Lead Identification Lead Optimization Preclinical Development IT PT VT LM CM hconstandt
- 44. hconstandt
- 45. Srini Dagalur. Applied Clinical Trials 2016 Time squeezing in Clinical Trials Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA = Protocol Approved FSI = First Site Initiation LSI = Last Site Initiation FPI = Frist Patient In LPI = Last Patient In LPO = Last Patient Out DBL = Database Lock ANL = Analysis Complete CSR = Clinical Study Report PA FSI LSI FPI LPI LPO DBL ANL CSR Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA FSI LSI FPI LPI LPO DBL ANL CSR hconstandt
- 46. Srini Dagalur. Applied Clinical Trials 2016 Time squeezing in Clinical Trials Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA = Protocol Approved FSI = First Site Initiation LSI = Last Site Initiation FPI = Frist Patient In LPI = Last Patient In LPO = Last Patient Out DBL = Database Lock ANL = Analysis Complete CSR = Clinical Study Report PA FSI LSI FPI LPI LPO DBL ANL CSR Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA FSI LSI FPI LPI LPO DBL ANL CSR hconstandt
- 47. Srini Dagalur. Applied Clinical Trials 2016 Time squeezing in Clinical Trials Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA = Protocol Approved FSI = First Site Initiation LSI = Last Site Initiation FPI = Frist Patient In LPI = Last Patient In LPO = Last Patient Out DBL = Database Lock ANL = Analysis Complete CSR = Clinical Study Report PA FSI LSI FPI LPI LPO DBL ANL CSR Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA FSI LSI FPI LPI LPO DBL ANL CSR hconstandt
- 48. Srini Dagalur. Applied Clinical Trials 2016 Time squeezing in Clinical Trials Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA = Protocol Approved FSI = First Site Initiation LSI = Last Site Initiation FPI = Frist Patient In LPI = Last Patient In LPO = Last Patient Out DBL = Database Lock ANL = Analysis Complete CSR = Clinical Study Report PA FSI LSI FPI LPI LPO DBL ANL CSR Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA FSI LSI FPI LPI LPO DBL ANL CSR hconstandt
- 49. Srini Dagalur. Applied Clinical Trials 2016 Time squeezing in Clinical Trials Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA = Protocol Approved FSI = First Site Initiation LSI = Last Site Initiation FPI = Frist Patient In LPI = Last Patient In LPO = Last Patient Out DBL = Database Lock ANL = Analysis Complete CSR = Clinical Study Report PA FSI LSI FPI LPI LPO DBL ANL CSR Protocol Development Site Selection Site Initiation Patient Recruitment Study Conduct Treatment Time Data Cleaning Analysis Reporting Study Closeout PA FSI LSI FPI LPI LPO DBL ANL CSR hconstandt
- 50. $134M hconstandt ROI & Calculate Value $807M
- 51. hconstandt
- 52. ROI of $3.1M 100 USERS SITE LICENSE Saving $134M ENTERPRISE LICENSE Saving $807M hconstandt
- 53. hconstandt From Sick Care to HealthCare
- 54. The goal of the Web is to serve humanity. We build it now so that those who come to it later will be able to create things that we cannot ourselves imagine TIM BERNERS LEE hconstandt
- 55. Winning Passionnate Hands-On Genius Team Excellence - Customer Driven - Respect Impact – Unconventional - Passionate - Fun hconstandt
- 56. Glad to see Realism coming in & Value & ROI demonstrated by FAIR Data Fabrics hconstandt
- 57. SKYPE: hansconstandt TWITTER: hconstandt LINKEDIN: hansconstandt booth #16 hans@ontoforce.com Hans Constandt Filip Pattyn filip@ontoforce.com Martijn Adriaens martijn@ontoforce.com Thank You! Questions?