Breaking Down Information Silos


Published on

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

  • Be the first to comment

  • Be the first to like this

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Breaking Down Information Silos

  1. 1. Breaking Down Information Silos Chris L. Waller, Ph.D.
  2. 2. Information SilosAn information silo is a management system incapable of reciprocal operation with other, related management systems.
  3. 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. 4. Information Silo Effects• Limits productivity• Stifles creativity• Hampers innovation• Inhibits collaboration• <Fill in the blank with your favorite pejorative expression>
  5. 5. Information Silo Solutions• Provide technologies that support information sharing processes and reward collaborative behaviors (people).
  6. 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. 7. Collaboration Platforms (Life Sciences)
  8. 8. Collaborative Business CultureWhy 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.• Its hard to know who to praise and who to blame.
  9. 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. 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 SharingWith the establishment of NCATS in the Previous Section: Expedited Drug Development Pathway 1fall 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 ofexpertise 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 aand 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 dataThrough partnerships that capitalize on protections and protecting patients’ information. well a an increasing volume of post-market Scientific Computing and the Science Enclaves at FDAour 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 andstrengths, 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 clinicalatient 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 communitiescan 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 securitytranslational 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 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 computingand 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 toolsvent disease. into FDA review and research efforts. http:/ / AboutFDA/ ReportsManualsForm s/ Reports/ ucm 274442.htm Page 1 of 3
  11. 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 Europes global competitiveness. announced a package of measures designed to boost the UKs 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. 12. CaveatsA well-constructed system canenable scientist to test but alsogenerate new hypotheses using well-curated, high-content translationalmedicine data leading to deeperunderstanding of various biologicalprocesses and eventually helping todevelop better treatment options.Active curation and enterprise datagovernance have proven to becritical aspects of success.
  13. 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. 14. Life Science Information Landscape A rapidly evolving ecosystem Yesterday Today Tomorrow Big Life Science Company Yesterday Today TomorrowInnovation Innovation inside Searching for Innovation Heterogeneity of collaborations. Part of the wider ecosystemModelIT Internal apps & data Struggling with change Cloud/Services Security and TrustData Mostly inside In and Out DistributedPortfolio Internally driven and owned Partially shared Shared portfolio 14
  15. 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 (  Paul Galatsis (
  16. 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. 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. 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. 19. Thank You• Chris L. Waller, Ph.D.•
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.