Making Cloud R&D Electronic Laboratory Environments a Reality

Transcription

1 Making Cloud R&D Electronic Laboratory Environments a Reality Next Generation Scientific & Laboratory Informatics Platforms John F. Conway Bio IT World Expo April 6, 2016

2 Scientific and laboratory informatics are our DNA. The world s leading, vendor-independent scientific informatics consultancy Science & Laboratory Data & Technology Over 20 years experience Plan Design Build Deploy Support Hundreds of projects each year, spanning research through manufacturing End-to-End Services Extensive Pharma, Biotech, CPG, CxO experience Platform and vendor independent 2

3 Today s on premise laboratory environments can impede R&D breakthroughs an unsustainable situation. Inability to Data Silos & Dark Data Share data, information, knowledge and ideas Inflexible Systems Poor Analytics Collaborate Human intuition is often ignored. Innovation and creativity are stymied. Collaboration & Externalization Constraints Longer time to value and missed opportunity Knowledge Sharing & Innovation Obstacles IT Costs & Overall Risk 3

4 New science, technology and approaches are magnifying the challenges. Diagnosis Treatment Prevent Diagnose Cure Cure Treatmen t Prevention Treat 4

5 Operating models must change new technologies and solutions are needed, and imminent. Collaboration Platforms Integration Services Advanced Analytics Robotics & Automation Cloud Technologies Internet of Things

6 Security / Identity The key to collaboration: An identity-managed, business rules-enabled, scientific data platform. Industry.com Presentation Layer (intuitive apps, dashboards, etc.) Government.gov Data Analytics Business Rules Academia.edu Aggregation Layer / Data Lake/Warehouse Transactional Systems Mobile Apps & The Internet of Things Data Provisioning (Governance, MDM, Stewardship) Hospitals/ Foundations.org 6

7 A future-state scientific data management platform is necessary to enable the vision. Adaptability & Agility Intuitive app toolkit to innovate and develop new scientific tools directly on the platform Streamlined User Experience Intuitive and seamless interaction across a range of tools that allows users to logically group views based on scientific tasks Cloud-based platform Facilitated Scientific Externalization Rapid and secure access to data and features for real-time scientific collaboration Shareable Infrastructure Optimized total cost of IT ownership by harvesting commonalities across Research templates and data models Actionable Analytics Powerful analytics to drive new insights for scientific and operational decision making Comprehensive data provisioning Globally accessible and searchable chemistry, biological and operational data, with transparent historical context and lineage 2016 Accenture All Rights Reserved. 7

8 A fully integrated ecosystem that promotes inter- and intra-company collaboration. Government -.gov Best Practices Industry -.com Proprietary data Public data Ontology Cloud Optimized Workflows Academia -.edu Multi tenant systems Single tenant systems Hospitals/Foundations -.org Integrated Analytics Shared data Integration Bus 8

9 Drive innovation, foster creativity and capture intuition. Externalization and collaboration Data provisioning Quicker deployment, rapid time-to-value Systems & Processes Culture & Governance 9

10 Our industry knowledge and relationships enable us to deliver excellence. 10

11 LabAnswer s approach: from on premise, to hybrid, to cloud. 12

12 Migration to a cloud electronic laboratory environment requires a holistic approach. Understand appetites and capacities Strategic/phased approaches Matching solution capabilities with business function Processes Be opportunistic Process and workflow harmonization Data governance, stewardship and MDM strategy (data provisioning) Culture Data OCM and cultural change Data is a top asset create, or be part of, a data foundation now Technology 13

13 The Scientific Data Platform of the Future It s not just about moving one application into the cloud. No Choose applications Build your own Functional area? Yes No Shared Cost model? Choose functional area Yes Industry Coalition RLSC, Allotrope, Pistoia, etc. Formulate core offering & roadmap Customize development plan Capabilities Mapping & Prioritization Define ontology & governance plan Define integration strategy Customize development plan Identify & harmonize applications Define ontology & governance plan Define integration strategy Customize rollout plan Harmonize applications Ontology governance Integration plan Support non-cloud applications Support non-cloud applications Support non-cloud applications. All rights reserved. Confidential and proprietary information of LabAnswer. 14

14 Some case studies 15

15 Case Study #1: Creating an ELE.from on premise, to hybrid, to cloud Mapping Step 1: Define organizational objectives Compound Selection Decision Mapping Step 2: Map as-is state from DMPK and identify gaps Mapping Step 3: Analyze issues, gaps and propose solutions Group Meeting Determines Compound Priority From Biology Start Scientist Requests Compound Material Scientist Receives Compound Test Request Scientists Performs Formulation Experiments Scientist Adds Study to Study Tracker DCMG Receives Request Focus Scientist Creates a Study Folder on P Drive Compound Receipt from DCMG Scientist Updates Study Workbook with Formulation Scientist Creates a Study Workbook From Excel Template Scientist Receives Compound Creates Label List Animal D Activi Scientist Orders Animals 16

16 Mapping Step 4: Build a R&D IT roadmap Capabilities Platform / System Maintenance New Implementation Legend Explore/POC Upgrade Multiple Data Governance Inventory Lab Execution XXX XXX Inventory ELN Implementation Entity XXX Chemical Registration Data XXX Knowledge XXX Operations XXX 17

17 Data/Services Governance is critical in building an integrated electronic research environment. Data Architecture Data Quality Data Development Metadata Data Governance Database Operations Document and Content Data Security Data Warehousing and Business Intelligence Reference and Master Data 18

18 Integration Case Study #2: Applying a platform approach to research RLSC Open, standards-based platform integrating core capabilities supporting pharmaceutical research and early development. Research Informatics Software Platform Ecosystem Research Life Sciences Cloud Pharma, Biotech and CROs ELN Co. 1 Analytics Co. 1 LIMS Co. 1 Analytics Applications Data Services Biotech Co. 1 Pharma Co. 1 CRO Co. 1 OMICS Co. 1 LIMS Co. 2 Request. 1 Infrastructure CRO Co. 2 Biotech Co. 2 Entity Registration 1 Assay Mgt. Co. 2 OMICS Co. 2 Biotech Co. 3 CRO Co. 3 Pharma Co. 2 Analytics Co. 2 Pharma Co Accenture All Rights Reserved. 19

19 The RLSC platform will be focused on driving innovation in the early stages of the R&D value chain. Primary RLSC Focus Later Focus Target identification Target validation Lead identification Lead optimization Preclinical ENABLES Make decisions Ideation and design Innovation cycle Analyze results Synthesize and test The vision for RLSC is to drive each step of the innovation cycle, ultimately enabling better science and a more productive research organization. ENABLES RLSC use cases Design experiment s Conduct experimen t Conduct workflow Register entity Predict values & properties Manage assay data Search metadata Define relationship s Partner collaboratio n Define a narrative Manage portfolio Manage inventory Accenture All Rights Reserved.

20 Vision for Research Transformed: Derive deep insights about research targets, drug characteristics and therapeutic outcomes through analysis and visualization of a holistic view comprising all available data The Changing Ecosystem Six imperatives for Future-state Research Externalization Data volume & Machine types Learning Non-traditional players IoT, Mobility, Cloud Simulation & modeling Science and technology breakthrough Precision / Outcomes More 1 Next generation capabilities deployed and integrated rapidly to keep pace with changing science, capitalizing on the platform economy 2 Collaborations mobilized seamlessly, quickly and securely for growing externalized research Infrastructure costs Time and effort to IND Disparity across industry Barriers to new capabilities Manual analysis Collaboratio n ramp up time Less 3 4 Diverse and complex data types captured and managed in growing volumes Insight-driven decision making through advanced analytics and visualization One directional process Paper Repeat experimentation Dark data & data silos 2015 Accenture All Rights Reserved. Long SW implementations Manual data collation Stop 6 5 Agility and operational efficiency drive reduced costs, allowing greater investment in innovation Digitization of research processes, the user experience with workplace of the future, and the emergence of new business models LabAnswer 21

21 Witnessing evolution: Appification and MSA of R&D scientific applications s and earlier 2000s 2010s Pre SOA (monolithic) Tight coupling Traditional SOA Looser coupling Micro-services Decoupled Image from PWC.com 22

22 LabAnswer is teaming with industry thought leaders to create the way forward. Collaborating to build a Research Life Science Cloud Robust set of embedded services and features, aligned to research industry needs Facilitates collaboration and exchange of information within an integrated, security-controlled, extensible platform Shared investment model reduces TCO and risk Are you ready? 23

23 Thank you labanswer.com Realize the promise of your science.