The document discusses the transition from paper-based laboratory data systems to electronic solutions, highlighting inefficiencies and the significant amount of time R&D organizations waste managing paper processes. It outlines the benefits of electronic systems, including improved data accessibility, reduced errors, and enhanced collaboration, while also emphasizing the importance of aligning system requirements with organizational goals. Finally, the document provides steps for implementing data management systems effectively, along with considerations for successful deployment.

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

2. ©2014 IDBS
How do you manage the most important
product in your lab your data

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Are you still using paper?
Maybe SharePoint or a basic ELN?

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

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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

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Where do you want to take your RD?

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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!

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But this only works if you have a system that
meets your needs and is used by the
scientists

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…

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SSSSTTTTOOOOPPPP!!!!
Where are you going to get the most value most quickly?

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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

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ELN vs LIMS vs LES vs MES vs SDMS…

13. ©2014 IDBS Common drivers will affect your project
Link data to data
Link data to people
Link people to people
1111000011110000 0000111100001111

14. ©2014 IDBS Steps to take
Requirements
Project buy in
System/vendor selection
Project approval
Implementation planning
Install
Configuration
Training
Go live
System maintenance and support

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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

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Application Integration
Start simple

17. ©2014 IDBS Other things to consider
Deployment Methodology
• Carrot vs Stick
Logistical Support
• Getting all the labs ready for digital
• System administration strategy

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The more data goes in, the more it comes out
Not quite…

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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

20. ©2014 IDBS The data management journey
Process execution
Structured data
Unstructured data

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

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Case Studies
Examples of customer deployments

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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

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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

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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

26. ©2014 IDBS Measured Time Savings

27. ©2014 IDBS Before you start your journey…
Define your vision
Understand your drivers
Focus on the ROI
Create a stepwise plan
Start simple!

28. ©2014 IDBS
Questions?
For more information, visit IDBS
at Booth 104 (far left aisle) or
idbs.com