osthus-allotrope presents "laboratory informatics strategy" at smartlab 2015
TRANSCRIPT
Building your laboratory
informatics strategy
The benefit of reference architectures &
data standardization
Wolfgang Colsman, OSTHUS
Dana Vanderwall, Bristol-Myers Squibb
Slide 2
Abstract
Building your laboratory informatics strategy:
The benefit of reference architectures & data standardization
Modern laboratory processes have to deal with a multitude of
data sources originating from different instruments, systems,
sites and external resources. As a consequence data analytics is
severely limited by incomplete or inconsistent metadata and
different data formats. This complexity leads to inefficient
processes and high costs due to insufficient data integration and
accessibility.
Showing different use cases, we will present a successful data
and systems integration approach using reference architectures
and data standardization, resulting in increased cost efficiency
and improved decision making.
Slide 3
Why?
What should a Reference Architecture look like?
Which Standards to use?
Topics
Slide 4
Why?
Topics
Slide 5
Instrument Instrument
Why:
Example 1
LIMS
Data Mart
Instrument JMP
CRO
CSV Paper
Scripts
Data Cleansing and
Controlled Vocabularies
build into code
Method Transfer,
Manual Transcription
Data Incomplete,
Formatting, …
Manual Transcription,
Missing Context
Slide 6
Why:
Example 2
LIMS
Request Management
Data Analytics Compound
Logistics
ELN
LIMS Monolith, not best in class
Slide 7
Best of breed did not work in the past because of the lack of
standardization, but do we agree:
1. Everybody should do what he can do best?
2. Anybody should be able to talk to everybody?
Why:
Thesis
Slide 8
Topics
What should a
Reference Architecture
look like?
Slide 9
What should a Reference Architecture look like:
Lab Integration Requirements
ELN
CRO
CMO MDM
IMS
LES
HR
CDS
Data
Archive
Data Mining
Data Mart
SDMS
DMS
LIMS Instrument
Data
Warehouse
Registration
Controlled
Vocabularies
Data
Analytics
Predictive
Modeling
ERP
MES
Departments
Sites
Slide 10
Data Acquisition
Data Analytics Data Management
Master Data Lab Workflow
Collaboration
What should a Reference Architecture look like:
Lab Integration Requirements
ELN MDM CDS
DMS
Instrument
CRO Data
Warehouse
Data Mart
Data Mining
CMO
LIMS
LES
SDMS
Data
Archive
HR
IMS
Controlled
Vocabularies
Data
Analytics
Predictive
Modeling
Manufacturing
ERP
MES
LIMS
Departments
Sites
Registration
Slide 11
Data Acquisition:
CDS / Instrument
Data Analytics:
Data Warehouse / Data Mart / Data Mining / Data Analytics / Predictive Modeling
Data Management:
DMS / SDMS / Data Archive
Ma
ste
r D
ata
:
MD
M /
HR
/ I
MS
/ R
egis
tration /
Contr
. V
ocab
. Lab Workflow & Manufacturing:
ELN / LIMS / LES / MES / ERP
What should a Reference Architecture look like:
Lab Integration Requirements
Collaboration:
CRO / CMO
Slide 12
What should a Reference Architecture look like:
Two Worlds of Workflow
Lab Workflow
Experiment Report
Data Analytics
Data Knowledge
Where is my Data?
Slide 13
Data Analytics
Data Knowledge
Where is my Data?
What should a Reference Architecture look like:
Pain Points
Lab Workflow
Experiment Report
Document Preparation
• Finding data
•Copy/paste
•Transcribe/convert
•Combine multiple sources
Data Management & Archiving
• Searching/Finding data
•Data format conversion
•Data migration
•Maintenance and/or unavailability of legacy systems
Errors
•Manual text entry or transcription
•Manual calculations
•Wrong or missing metadata
•Need to reprocess data
Data Exchange
•Disparate data file formats
•Manual transcriptions
•Added cost & complexity to CROs, CMOs, partnerships
Regulatory Compliance
• Instrument & software validation
• SOPs
• System documentation
• Supporting questions/investigations (CAPA)
Extracting Knowledge & Value from Data
• Speed to answer/decision
•Data silos
•Constrained innovation
• Limited data mining & analytics
Slide 14
What should a Reference Architecture look like:
Root Cause
Lab Workflow
Experiment Report
Data Analytics
Data Knowledge
• Patchwork of software, helper applications, persistent gaps
• Lack of standard data file formats
• Lack of standard software interfaces (APIs)
• Lack of standard for metadata (the who, what, where, when, why, how)
Slide 15
What should a Reference Architecture look like:
Solution
Lab Workflow
Experiment Report
Data Analytics
Data Knowledge
• Open Document Standards
• Reusable Software Components and APIs
• Metadata Repository
Slide 16
Lab Workflow
Data Analytics
Reference Architecture & Data Standards
Data Management
Dashboards Metadata Browser Data Viewer
Plan
Analysis
Prepare
Samples
Submit
Samples
Acquire
Data
Process
Data
Store
Data
Analyze
Data
Reports
Results
Taxonomies Methods Instruments Samples Experiments Results Data
Allotrope Data Format Allotrope Metadata
Taxonomies
Allotrope Class
Libraries and APIs
Forecasting
& Capacity Planning
Request Management
& Tracking
Collaboration
& Distribution
Slide 17
APN webinars and information exchange with specific APN
members:
12 March 2015 APN Partner Led Committee Workshop, New
Orleans, LA
13 March 2015 APN General Meeting & Workshop, New
Orleans, LA
24 April 2015 Cross Industry Workshop, Cambridge, MA
15 Sept 2015 APN Workshop, Chicago, IL
16 Sept 2015 Cross Industry Workshop, Chicago, IL
Next Steps
©2015 Allotrope Foundation
Allotrope Foundation
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• Subject Matter Experts
• Project Funding
Pharmaceutical & Biotechnology
Companies
• Project Management
• Legal & Logistical Support
Secretariat
• Software Development
• Technical Leadership
Professional Software Firm
• Requirements & Specifications
• Contributions, PoC Applications
Partner Network
AbbVie Amgen Baxter Bayer Biogen Idec
Boehringer Ingelheim Bristol-Myers Squibb Eisai Eli Lilly Genentech/Roche
GlaxoSmithKline Merck Pfizer
ACD/Labs
Biovia
BSSN
IDBS
Mettler Toledo
Sartorius
Thermo Scientific
Waters
©2015 Allotrope Foundation
web: www.allotrope.org mail: [email protected]
web: www.osthus.com mail: [email protected]
Sign up to:
Allotrope Partner Network APN: partners.allotrope.org
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