1. Scientific Information as a Business Asset
Driving Productivity at Merck Research Labs Through Novel
Approaches to Scientific Information Management
Speaker: John Koch
Merck & Co.
2. 2
Overview
• Information Management Challenges Currently Facing R&D Organizations
• The Value of Better Information Management
• Merck’s Scientific Information Architecture and Search (SIAS) Group
• Approaches for Improving Information Management
3. 3
R&D decisions rely on high quality information to steer programs
and the pipeline
145 Knowledge Assets
“Target validation plan”
250 Business Groups
“Early Development team”
1849 People
“John Smith”
1144 Information Types
“Clinical Trial Name”
110 Organization Units
“Analytical Chemistry”
492 Sources
“Electronic Lab Notebook”
66 Business Processes
“Integrative assessment of liver
toxicity”
86 Decisions/ Gateways
“Determine Patient
Stratification Biomarkers”
472 Activities
“Refine model”
125 Roles
“Statistician”
R&D Information LandscapeR&D decisions rely on high quality
information to steer programs and the
pipeline
Over time BioPharma has created and
stored tremendous amounts of data,
information and knowledge; there are
100,000’s of information elements
Companies must make effective,
efficient use of the vast quantity of
information it houses, creates, and has
access to externally to make sound
decisions
The volume and sophistication of internal information and that available through external
sources continues to grow at a rapid and accelerating rate
Therefore, the ability to readily find, access, and use information is absolutely critical
6. 6
5
Today Next 2-3 Years Beyond
Culture of Single Use
“Find & Access”
DecisionMaking
Quality
Vocabulary
Management
Embedded
Stewardship
Information
Flows Modeled
Effective
Search
Integrated
Information
Architecture
IM Challenges
Characterized
Fragmented
tools,
processes
Systematic
categorization
of data
Information
ManagementMaturity
As knowledge workers understand and embrace improved information management
practices, better decision making can be enabled by better access to information
Organization-Wide Information Re-Use
? Better Information Management Better Decision Making: Better
analysis, more transparency and collaboration, better workflow
management, faster decisions
DecisionQualityAdoption,Maturity
Improving R&D Decision Making
7. 7
5
Engaging the business: Focus Area Key Questions
User Interface Engine Content Creators
Creators
ContentEngineQuery Results
Interface
What information is required to make those decisions? Who needs that information? How do they use that
information used to make those decisions?2
What are the critical business processes? What major decisions are associated with those processes?1
How is that information created? Who creates it? Where is that information stored?3
How is that information accessed (searched for, found, displayed)?4
What challenges are associated with accessing and using that information?5
How can access to and use of that information be improved? What value will those improvements deliver to
the business?
6
Users
Morville & Callendar. 2010 Search Patterns
8. 8
Information Management CapabilitiesArchitectureSearchAccess
IM Capabilities Description
Search tools that enable users to locate scientific information across various sources,
both structured and unstructured, in various formats and across functional groups
Capability for users to identify colleagues with specific skills, expertise, or tacit
knowledge through a search tool and / or standardized profiles or tagging
System of access policies that prudently permits access to information and has clear
procedures for granting or restricting access
Shared practices for creating, storing, sharing, and maintaining explicit and tacit
information
Organization of critical data sources to make them more conducive to search,
retrieval, analysis and re-use through techniques including tagging and indexing
Well-maintained record of critical information and data sources across the
organization, including how the information is used or linked to other sources
Improving Information Management requires specific capabilities to enhance information
search, access, and architecture
1
2
3
4
5
6
Expertise Location
Access
Data Stewardship
Data Structuring
Key Data Assets
Scientific Search
9. 9
ILLUSTRATIVE
Leaders in Search & Information Management:
Indexing of complex
hierarchical relationships
from relational database
tables
Multi-faceted, interactive
filtering of search results
based on document
metadata
Implementing solutions for
searching non-text
information (e.g., enterprise
video search)
Advanced search analytics
Integration with social media
Highly scalable / extensible
Service-Oriented
Architecture
Seamless information flow
between departments / sites
Includes a data services and
exchange layer
Reusable and configurable
code modules
Closed-loop data flow via
integrated data sources
across the product life
cycle
Consistent, personalized,
real-time access for internal
and external users
Enterprise-wide technology
to capture, create, and
share knowledge / best
practices
Data stewardship standards
and processes that ensure
consistency of data
quality, storage, and
exchange
BioPharma and other industry players have demonstrated innovative, peer-leading Search,
Access, and Architecture capabilities
Capability Maturity Stages
Basic
Developing
Functional
Advanced
World-class
1
2
3
4
5
Open
Access
Data
Stewardship
Data
Structuring
Key Data
Assets
Scientific
Search
Expertise
Location
ArchitectureSearch Access
10. 10
Basic
Developing
Functional
Advanced
World-class
Data access permissions that
reflect a balance between security
and accessibility
A culture of collaboration enables
information access across divisions
Designated roles and
responsibilities to champion data
stewardship
Employees know what information
to store and where to store it
Well defined best practices,
search processes, and rules
Employees understand the search
content and participate in helping
steward data
Query experts help conducting
complex searches
Intuitive tools and applications
ensure all information is searchable
Well established processes for
categorizing, structuring and
storing information
Clearly defined data assets in key
business areas
Well-defined links between key
data assets to enable
interoperability between different
information types
What does “good” Search look like for R&D?
Addressing identified challenges will produce a future state with capable people, processes
and technologies to enable fluid information exchange and better decision making
1
2
3
4
5
Current State
Capability Maturity Stages
Search Access Architecture
Access
Data
Stewardship
Data
Structuring
Key Data
Assets
Scientific
Search
Expertise
Location
ILLUSTRATIVE
11. 11
SIAS has developed a flexible, repeatable business
engagement and problem solving approach
Scope Pilot: Define scope of problem, including
specific business impact and value proposition
Define Requirements: Define use cases;
prioritize and select use case(s) to test in Pilot
Select / Model Use Case(s): Model information
flow for selected use case(s), select pilot platform
Execute Pilot(s): Build test environment; create /
update processes / standards; test use case &
determine if needs are met
Build Business Case / Roadmap: Develop
business case & roadmap for scale-up; validate
with business users and sponsor
Scale Solution: Expand
coverage / capability to new
information types, sources,
users; measure adoption,
performance, value realized
Embed and Maintain:
Assess long-term production
viability; define long-term
roadmap; take viable solutions
to production scope / capability
Monitor / Measure: Continue
to track performance; re-visit
unaddressed business issues
Target and Engage Business Area:
Build relationships in target areas;
gauge IM needs
Identify Pain Points: Document
high level business processes,
identify & map key information types
& sources, characterize pain points
Validate / Prioritize Issues: Define
impact of pain points, detail / prioritize
use cases aligned to business
impacts, develop business case
Solve (Pilot Solution)
Execute
Pilot(s)
Define
Requirements
Scale and Embed
Build Business
Case / Roadmap
Monitor /
Measure
Scope
Pilot
Model Use
Case(s)
Scale
Solution
Embed and
Maintain
Target & Engage
Business Area
Identify Pain
Points
Validate &
Prioritize Issues
Engage and Diagnose
SIAS follows a consistent process for diagnosing and solving specific business area IM issues,
then embedding and transitioning those solutions
1-6 months 6-18 months1-3 months
12. 12
Drive an integrated approach to improve Information Management
& Search
Targeted IM solutions: Deliver improvements in processes, technologies, and
/ or behaviors that improve data quality / availability
Stewardship: A set of shared practices for creating, storing, sharing, and
maintaining information that is conceived, sustained, and improved by business
Information Stewards
Address complex, specific business needs with appropriate processes / capabilities
Deep coverage of information sources
Search: Deploy a search capability to make information more accessible, explorable
and useful for scientists
Addresses broad, high-level search use cases
Provide exploratory and analytic capabilities to drive value high ROI opportunities
Big Data framework that can deliver use cases beyond scalable search
Define, communicate, embed, and monitor good stewardship practices
Create a vital link between business, information, and technology
14. 14
sIFM is a method of documenting and modeling the flow of information through an enterprise (from data
generation to knowledge creation) that allows both targeted analysis (e.g. information flow through a
specific business process for a select organization), as well as holistic analysis (e.g. complex, cross-
organizational information flows, processes, and knowledge transitions) across the information continuum.
PPDM
GHH
MCC
•Regulatory
MRL
MMD
PharmSci
Merck
Traditional Business Analysis
Multiple BA resources working to develop
project/area-specific analysis artifacts using
a variety of methods and representations (not
connected; shared and stored in isolation)
Multiple BA resources working to represent
information flows in a common way, so that related
information entities are connected, complex
interactions can be visualized, understood and
analyzed, and project/area-specific ‘views’ of the
model can still be generated
Semantic Information Flow Modeling
custom Graphing Canvases
Lead Optimization
(LO)
(fromIFDs)
PCC-FIH
(fromIFDs)
Target ID/Validation
(fromIFDs)
Lead ID
(fromIFDs)
FIH-PH2B
(fromIFDs)
PH3-File
(fromIFDs)
File-Approval
(fromIFDs)
Approval - Launch
(fromIFDs)
Semantic Information Flow Modeling (sIFM)
15. 15
Results in disparate analysis artifacts (ppt, excel, word/text) with related
information within them that aren’t linked
16. 16
Applying sIFM
Ontologies / Taxonomies /
Relationships
Enhanced workflows,
stewardship models
Improved Integration, Search,
Decision support
Applying sIFM to represent and analyze complex information domains, and knowledge
transitions, in order to successfully identify and implement technologies that enhance
information/knowledge structure, interoperability, and search.
17. 17
Information Management Solution
QUICK – Overview
SIAS characterized several information management challenges which dictated the need for a
knowledgebase of definitive pre-clinical compound data for Pharmacology / Drug Metabolism
Dispersed Historical Data
A lengthy, complicated process is required, on a regular
basis, to retrieve information off hard-drives, shared
drives, and outdated repositories
Duplicative Data Capture / Processing
The precedent of creating Excel copies of data for
upload to Teamsites consumes resources and creates
islands of potentially outdated data
Access / Storage of Definitive Data
Unable to effectively manage definitive data for
compounds
Challenges
Incomplete Data Upload
A large portion of the data generated is not uploaded
into structured repositories
Harmonizing Reporting Standards
Inadequate governance over data upload protocols and
non-standardized assay reporting formats limit data
usability for cross-compound comparisons
Solution
QUantItative PharmaCology
Knowledgebase (‘QUICK”)
Single, authoritative portal for access to
definitive, integrated data sets of clinical and pre-
clinical metabolism and in vivo pharmacology
experimental results
Exposed data will be targeted, but not limited to,
addressing hypothesis generating questions
relating to predictive modeling such as human
dose prediction, study avoidance, and BIC
benchmarking of candidate selection, and
translational PK/PD modeling
Data will be made available in a well-structured
and searchable format allowing easy data
representation and integration with existing and
future data analysis and visualization tools
Centralized &
Structured Data
Improved Retrieval
& Access
18. 18
- 18 -
Information Management Solution
QUICK – Expected Value
Improvement Opportunities Description
Improve Data Collation /
Reporting Efficiency for
Definitive Pre-Clinical Data
Reduce time to collate definitive datasets by ~95%
Enhance Analytical
Productivity and
Opportunities
50-75% increase in efficiency of analysis (comparisons of
results from prior assays)
Enhance Collaboration
Improved collaboration through stewardship and metadata
management, increasing productivity by 50% for modeling and
simulation; increased pharmacology / drug met. productivity
Study Avoidance
Potentially eliminate unnecessary studies due to faster access
to more accurate definitive datasets, resulting in better study
selection and confidence in progressing / killing compounds
QUICK enables decisions to avoid costly studies through better design and decision making
and greater productivity through better data quality, structure, and accessibility; improved data
collation capability; and improved collaboration and cross-functional information sharing
