A look into the non-technical aspects of breaking information silos.

1. Breaking Down Information Silos
Chris L. Waller, Ph.D.

2. Information Silos
An information silo is a management system incapable of reciprocal operation with
other, related management systems.

3. Information Silo Causes
• Technology
– Enterprise data systems are too rigid, slow, prone to
outages, hard to use…
• Process
– Legacy processes don’t factor in the need for
information sharing (the technologies didn’t exist)…
• People
– People are not properly incentivized for collaborative
work and lack trust…

4. Information Silo Effects
• Limits productivity
• Stifles creativity
• Hampers innovation
• Inhibits collaboration
• <Fill in the blank with your favorite pejorative
expression>

5. Information Silo Solutions
• Provide technologies that support information
sharing processes and reward collaborative
behaviors (people).

6. Information Integration Technologies
(Life Sciences)
• Standard Data Models (CDISC, etc.)
• Standard RDB Platforms (Oracle, etc.)
• Standard Ontologies (W3C, etc.)
• Semantic Platforms (IOInformatics, etc.)
• All of the above (Open PHACTS)

7. Collaboration Platforms
(Life Sciences)

8. Collaborative Business Culture
Why Don’t People Collaborate (Share Information)?
• Not knowing the answer.
• Unclear or uncomfortable roles.
• Too much talking, not enough doing.
• Information (over)sharing.
• Fear of fighting.
• More work.
• More hugs than decisions.
• It's hard to know who to praise and who to blame.
http://blogs.hbr.org/cs/2011/12/eight_dangers_of_collaboration.html

9. Collaborative Business Culture
• 10% of Senior HR Execs and 39% of Employees
Believe that their Companies Effectively
Encourage Collaboration
• Mutual Trust (Lack of) is a Significant Barrier
to Collaboration
– 31% of Developed Market R&D Staff Trust
Emerging Market Colleagues
– 22% of Emerging Market R&D Staff Trust
Developed Market Colleagues
Source: Research and Technology Executive Council Research

10. Stimulating Information Sharing (NIH/FDA)
Reports > Harnessing the Potential of Data Mining and Inform ation Sharing 12/ 9/ 11 10:17 AM
Home > About FDA > Reports, Manuals, & Forms > Reports
About FDA
Harnessing the Potential of Data Mining and Information Sharing
With the establishment of NCATS in the Previous Section: Expedited Drug Development Pathway 1
fall of 2011, NIH aims to reengineer the FDA currently houses the largest known
As noted in PCAST’s Report to the President on Health Information Technology, IT has the potential to transform healthcare and—
through innovative capabilities—improve safety and efficiency in the development of new tools for medicine, support new clinical
studies for particular interventions that work for different patients, and transform the sharing of health and research data.
translation process by bringing together repository of clinical data (all of which is de-
FDA currently houses the largest known repository of clinical data (all of which is de-identified to protect patients’ privacy),
including all the safety, efficacy, and performance information that has been submitted to the Agency for new products, as well as
an increasing volume of post-market safety surveillance data. The ability to integrate and analyze these data could revolutionize
the development of new patient treatments and allow us to address fundamental scientific questions about how different types of
expertise from the public and private identified to protect patients’
patients respond to therapy. It would also provide an enhanced knowledge of disease parameters— such as meaningful measures
of disease progression and biomarkers of safety and drug responses that can only be gained by analyses of large, pooled data sets
— and would allow a determination of ineffective products earlier in the development process.
sectors in an atmosphere of collaboration privacy), including all the safety, efficacy, and
Additionally, the ability to share information in a public forum about why products fail, without compromising proprietary
information, presents the potential to save companies millions of dollars by preventing duplication of failure. FDA sometimes sees
applications from multiple companies for the same or similar products. Although we may have reason to believe that such a
and precompetitive transparency. performance information that has been
product is likely to fail or that trial design endpoints will not provide necessary information based on a previous application from
another company, we are currently unable to share this information. As a result, companies may pour resources into the
development of products that FDA knows could be dead ends.
submitted to the Agency for new products, as
To harness the potential of information sharing and data mining, FDA is rebuilding its IT and data analytic capabilities and
establishing science enclaves that will allow for the analysis of large, complex datasets while maintaining proprietary data
Through partnerships that capitalize on protections and protecting patients’ information.
well a an increasing volume of post-market
Scientific Computing and the Science Enclaves at FDA
our respective Historically, the vast majority of FDA de-identified clinical trial data has gone un-mined because of the inability to combine data
safety surveillance data. The ability to
from disparate sources and the lack of computing power and tools to perform such complex analyses. However the advent of new
technologies, such as the ability to convert data from flat files or other formats like paper into data that can be placed in flexible
relational database models, dramatic increases in supercomputing power, and the development of new mathematical tools and
strengths, NIH, academia, philanthropy, p integrate and analyze these data could
approaches for analyzing large integrated data sets, has radically changed this situation. Furthermore, innovations in
computational methods, including many available as open-source, have created an explosion of statistical and mathematical
models that can be exploited to mine data in numerous ways to enable scientists to analyze large complex biological and clinical
atient advocates, and the private sector data sets.
revolutionize the development of new
The FDA scientific computing model provides an environment where communities of scientists, known as enclaves, can come
together to analyze large, integrated data sets and address important questions confronting clinical medicine. These communities
can take full advantage of the promise of patient treatments and allow us to address
will be project-based and driven by a specific set of questions that will be asked of a dataset. Each enclave is defined by its
participants, datasets, and sets of interrogations to be performed on the data. Enclaves may be comprised of internal FDA
scientists and reviewers working together or outside collaborators working with FDA scientists under an appropriate set of security
translational science to deliver solutions controls to protect the sensitive and proprietary data of patients and sponsors, respectively. Engagement of industry sponsors as
fundamental scientific questions about how
part of community building will be vigorously pursued, leveraging expertise from the companies that submitted the data in a
public-private partnership model.
to the millions of people who await new The scientific computing environment will also provide a dedicated infrastructure for application development and software testing
different types of patients respond to
for FDA scientists and reviewers. This will allow FDA staff to develop new applications to improve review, monitoring, and business
processes in an environment separate from where regulatory review data is assessed. Additionally, the scientific computing
and better ways to detect, treat, and pre- environment will be used to evaluate novel software developed outside of FDA and to rapidly incorporate innovative developments
therapy.
in support of FDA regulatory reviews. This ability to “test drive” new applications outside the regulatory review environment has
the potential to shorten traditional FDA development cycles and facilitate the adoption of new software that can enhance quality,
efficiency, and accuracy of FDA regulatory reviews, as well as streamline the adaptation of new higher-powered analytical tools
vent disease. into FDA review and research efforts.
http:/ / www.fda.gov/ AboutFDA/ ReportsManualsForm s/ Reports/ ucm 274442.htm Page 1 of 3

11. Stimulating Information Sharing (NHS, EU)
Horizon 2020 is the financial instrument
implementing the Innovation Union, a
Europe 2020 flagship initiative aimed at
Prime minister David Cameron has securing Europe's global competitiveness.
announced a package of measures
designed to boost the UK's life sciences
industry. These include a £180 million fund
to support innovation and plans to allow This conference will explore how EU
healthcare companies access to NHS funding can promote economically and
patient records to support research. socially sustainable innovation models with
the aim of more openness, easier
accessibility and higher result-oriented
efficiency.

12. Caveats
A well-constructed system can
enable scientist to test but also
generate new hypotheses using well-
curated, high-content translational
medicine data leading to deeper
understanding of various biological
processes and eventually helping to
develop better treatment options.
Active curation and enterprise data
governance have proven to be
critical aspects of success.

13. The Future: Virtual Life Sciences
• Forrester has identified three themes driving the
future of collaboration and information sharing
technology
– The global, mobile workforce
• 62% of workforce works outside an office at some point (this
number is growing)
– Mobility driven consumerization
• Cloud-based collaboration solutions are being used in
conjunction with numerous devices
– The principle of “any”
• Need to connect anybody, anytime, anywhere on any device

14. Life Science Information Landscape
A rapidly evolving ecosystem
Yesterday Today Tomorrow
Big Life
Science
Company
Yesterday Today Tomorrow
Innovation Innovation inside Searching for Innovation Heterogeneity of collaborations. Part of the
wider ecosystem
Model
IT Internal apps & data Struggling with change Cloud/Services
Security and Trust
Data Mostly inside In and Out Distributed
Portfolio Internally driven and owned Partially shared Shared portfolio 14

15. The Evolving Life Sciences Ecosystem
 Evolving paradigm for the discovery of medicines (Collaborative)
 A vision that points towards open innovation and collaborations
 Open research model to collectively share scientific expertise
 Enhance speed of drug discovery beyond individual resource capabilities (Speed)
 Limited research budgets and capabilities driving greater shared resources
 Goal to see all partners succeed by accelerating the SCIENCE
 Synergize Pfizer’s strengths with Research Partners (Knowledge)
 Pair Pfizer’s design, cutting edge tools, synthetic excellence with research partners (academics, not-for-
profits, venture capitalists, or biotechs) to develop break through science, novel targets, and indications of unmet
medical need
 Current example of academic and not-for-profits partners (Discover and Publish)
 Drive to publish in top journal with science receiving high visibility and interest
Body clock mouse study suggests new drug potential
Mon, Aug 23 2010
By Kate Kelland
LONDON (Reuters) - Scientists have used experimental drugs being developed
by Pfizer to reset and restart the body clock of mice in a lab and say their work
may offer clues on a range of human disorders, from jetlag to bipolar disorder.
a few months ago we entered into a collaboration with
the giant pharmaceutical industry Pfizer to test some of
their leading molecules for potential relevance to HD.
Contacts:
 Travis Wager (travis.t.wager@pfizer.com)
 Paul Galatsis (paul.galatsis@pfizer.com)

16. Public-Private Partnerships
• What is your view on Public-Private
Partnerships (and Consortia in general)?
– Is your organization willing to participate and
share information?
– What information types do (would) you share
– What types do (would) you not share?

17. Collaboration and Information Sharing
Barometer
• Does your company..
– …motivate and link innovation efforts by
identifying and routinely communicating key areas
for innovation activity?
– …have a strategy that allows for geographically
dispersed staff to access the resources necessary
to collaborate and share information?
– …have tools that support rapid collaboration, such
as data sharing and analysis or crowdsourcing
platforms?

18. Technology
• Will the current technologies be sufficient for
the “big data” needs (both horizontal and
vertical) that are emerging as the information
silos are integrated?

19. Thank You
• Chris L. Waller, Ph.D.
• http://www.linkedin.com/in/wallerc