The Evolution of Laboratory Data Systems: Replacing Paper, Streamlining Process Execution, and Delivering Product and Process Insight

1. The Evolution of Laboratory Data Systems Replacing Paper, Streamlining Process Execution, and Delivering Product and Process Insight Jarrod Medeiros 22 October 2014 © 2014 ID Business Solutions. All Rights Reserved

4. A Common Situation Heavy dependency on paper Extensive manual activity (Copy/Paste) Data and IP spread out within and across Notebooks Data and Knowledge silos, limited Collaboration Documentation quality is variable Experimental failures are often not recorded For every 100 scientists, RD organizations spend more than 19,980 hours per year managing paper-based processes For every month you rely on paper-based processes, you are spending more than 1,665 hours in non-productive time* * Based on 100 scientists, working 7h/day for 46 weeks/year

5. ©2014 IDBS A Common Situation 5 hours per week spent looking for data to prepare reports Almost 20% of the time, the data needed cannot be found Experiments often have ttoo be rerun because the data is not accessible or cannot be retrieved Results of a survey of development groups in 104 organizations

7. ©2014 IDBS I probably don’t need to tell you the benefits of electronic systems But just to name a few… Reduced entry time Reduced transcription = reduced errors Direct access to other electronic systems Searchability, searchability, searchability!

9. ©2014 IDBS Typical Scenarios •With files stored on a common drive or SharePoint Paper + Excel • Still using excel for anything that doesn’t fit into LIMS Paper + LIMS • Document repository with limited search capabilities Basic ELN • And CDS ELN + LIMS and SDMS and so on…

11. ©2014 IDBS What system is right, or systems? What are your overall goals and how do the requirements align with them? Have you put together the business case? By mapping functionality to requirements to goals you can ensure that the system meets the needs of the organization

15. ©2014 IDBS System Requirements Common pitfalls Too few business experts • What one user needs may not suit another user • Better option is to gather requirements from as many users as possible and focus on the core requirements All requirements are must have • Categorizing and prioritizing requirements allows you to control scope and budget, focusing on high value areas

19. ©2014 IDBS The data management journey Enterprise analysis insight Cross collaboration Structured data process Simple structured data Capturing unstructured data Simple data modeling Low to higher risk costs / ROI minutes to years

21. ©2014 IDBS The data management journey Unstructured Data •Reduced data entry time •Improved legibility •Easier to find data Structured Data •Reduced transcription •Flag deviations •Numerical and contextual search Process Execution •System integration •Eliminate errors •Enterprise insight Typical time saving: 1.5-2 hours per scientist Per week Typical time saving: 2-4 hours per scientist Per week Typical time saving: 5-7 hours per scientist per week

23. ©2014 IDBS VISION – lab knowledge management Clear, defined vision Large scale project Paper removal – time saving IP capture at source and easy data access Reduction of repeated experiments Duration: less than 6 months

24. ©2014 IDBS VISION – evolved over time Flexible, ambitious vision Large scale project Increased data quality Reduced collation time International process harmonization IP standardization Streamlined reporting Wanted to do more! Duration: 6 years

25. ©2014 IDBS VISION – full process integration alignment Two stage, ambitious vision Large scale project across many groups Process harmonization Real time process reporting Less errors time looking for them Holistic view of development process Foundations of QBD Duration: 1 year