Data lifecycle mgt across the enterprise
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The document discusses the challenges and solutions associated with data lifecycle management in the pharmaceutical industry, emphasizing the need for better data utilization to reduce R&D costs and improve productivity. It advocates for the use of smart data, which enhances data integration across different systems while maintaining existing technologies, and outlines various use cases including lab instruments, clinical trials, and regulatory compliance. Furthermore, it highlights the importance of creating a semantic framework to unify data and improve analytics capabilities across the enterprise.
Data lifecycle mgt across the enterprise
- 1. V.2.2 Eric Little, PhD Chief Data Officer OSTHUS [email protected] Data Lifecycle Management Across The Enterprise
- 2. Slide 2 Pharma invests in R&D and has to make $ back over subsequent years Most R&D will fail, so risk is high Law of Diminishing Returns R&D productivity is declining Harder treatments have greater costs, potentially lower returns Drugs with minimal improvements (not as many blockbusters + generics) The Pharma Industry Is At A Tipping Point From: Kelvin Stott - https://endpts.com/pharmas-broken-business-model- an-industry-on-the-brink-of-terminal-decline/
- 3. Slide 3 Reduce R&D costs through better use of data Many experiments are re-run because scientists cannot find existing data Costs of system integration is much higher than data integration Standardization upstream can significantly impact costs downstream Once data is available – automate as much as possible Connect your internal data to other external data sources Many items exist in open source that can be modified easier than built from scratch How To Help Remedy the Situation Use the data you have before you generate more! Start with reoccurring tasks – workflows, models, query patterns, analytics, etc., then build out! Don’t reinvent the wheel! Build data communities!
- 4. Slide 4 THE MOVE FROM BIG DATA TO BIG ANALYSIS STATISTICAL SEMANTICS MACHINE LEARNING REASONING
- 5. Slide 5 Moving to Smart Data Smart data can be added to existing systems Does not require replacement of existing tech Smart data provides a separation of: Model Layer Data Layer Link to the model layer Leave data in place Smart data links information from the models to instance-level data Smart Data uses metadata in order to capture context about data
- 6. Slide 6 Semantic Spectrum of Knowledge Organization Systems • Deborah L. McGuinness. "Ontologies Come of Age". In Dieter Fensel, Jim Hendler, Henry Lieberman, and Wolfgang Wahlster, editors. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2003. • Michael Uschold and Michael Gruninger “Ontologies and semantics for seamless connectivity” SIGMOD Rec. 33, 4 (December 2004), 58-64. DOI=http://dx.doi.org/10.1145/1041410.1041420 • Leo Obrst “The Ontology Spectrum”. Book section in of Roberto Poli, Michael Healy, Achilles Kameas “Theory and Applications of Ontology: Computer Applications”. Springer Netherlands, 17 Sep 2010. • Leo Obrst and Mills Davis "Semantic Wave 2008 Report: Industry Roadmap to Web 3.0 & Multibillion Dollar Market Opportunities”. 2008. Sources
- 7. Slide 7 Advantages of Using This Tech Use cases where customers report distinct improvement: Better defined terms • Differentiates between Entities and Labels – more specific data dictionary Better taxonomic structure • Hierarchies can be accurately captured – not buried in incorrect tables Query Federation • Can easily use multiple data sources (integration) Query Faceting • Query results can be easily refined (and shared) Better use of metadata • Provides context for users • Raw data is more valuable over time Makes data actionable across an enterprise • Moves from local data (on people’s machines, in their heads) to explicit sharable resources • Adding SMART DATA to BIG DATA provides the means to access and use the data • Requires combining logical data with statistical data in order to find patterns of interest inside of large data sets
- 8. Slide 8 A Semantic Framework can connect the entire enterprise using a common semantics The Semantic Hub should only focus on metadata (not instance level data) Benefits: Common Terms, Models, Queries, Rules and Results (End-to-End) Integrating Data Across the Enterprise Lab Instruments Clinical Trials Regulatory AffairsProduction eArchiving
- 9. Slide 9 Lab Instrument Use Case – Allotrope Framework HPLC – UV Mobile Phase Selection
- 10. Slide 10 Ontology for HPLC Example (Allotrope) resultdevice material process
- 11. Slide 11 Clinical Trials Use Case – Astra Zeneca & MedImmune
- 12. Slide 12 Connecting The Dots Across AstraZeneca & MedImmune For Clinical Trials
- 13. Slide 13 FAIR Principles Bring Together Clinical Trials Data Across Phases
- 14. Slide 14 Domain Knowledge Is Captured In Models
- 15. Slide 15 Production Use Case – Manufacturing Data Integration
- 16. Slide 16 Often times R&D and manufacturing cannot easily share data Competing systems can evolve which cause incompatibilities Manufacturing data is often lower less complex than R&D data, but significantly higher in throughput QA/QC plays a major role Far more interpretation in R&D Manufacturing needs results fast • Alarms • Trends Manufacturing data is less retrospective Manufacturing Data Vs. R&D Data
- 17. Slide 17 Regulatory Use Case – Unstructured Data Integration
- 18. Slide 18 Regulatory compliance requires accessing and mining unstructured data Linking unstructured data to other data provides significant advantages Text to DB links unstructured and structured data Text to Public Data Sources leverages open source research Regulatory Compliance Regulatory Documentation
- 19. Slide 19 E-Archiving: Managing Data Over Long Lifecycles
- 20. Slide 20 Data is made available for easier search and indexing (even after long periods of time) Archiving is no longer a “vault” concept but is integrated within the Data Mgt. Lifecycle E-Archiving Using the Allotrope Data Framework
- 21. Slide 21 Big Analysis Requires Hybrid Architectures Semantic DBs Unstructured Docs Structured Data Cloud DBs (NoSQL)Analytics Dashboards & Reports Integration Layer
- 22. Slide 22 Data Science (machine learning, text analytics, clustering etc.) FAIR Data Is Now Accessible For Advanced Analytics Linked Open Data & Open APIs Semantic Graph DB (Knowledge Graph) Operational DBs … Unstructured Documents Analytics Tools simulations statistics reasoning Visualization dashboards exploration search … Semi-structured Data Instrument Data Lightweight Semantic Integration Layer (semantic RMDM, APIs, semantic indexing, data annotation, catalogues, meta data and linking) Reporting regulatory internal external
- 23. Slide 23 CONNECTING DATA, PEOPLE AND ORGANIZATIONS Contact Information: Email: [email protected] Web: www.osthus.com www.biganalysis.com Twitter: OntoEric