SlideShare a Scribd company logo
1 of 29
Download to read offline
Lori Ball, Chief Operating Officer, President of Integrated Client Solutions,
BioStorage Technologies, Inc.
Brian J. Brunner, Senior Manager, Clinical Practice, LabAnswer
Suresh Chandrasekaran, Senior Vice President, Denodo Technologies
Building an Intelligent Biobank to
Power Research Decision-Making
1:00 – 1:05 pm Workshop Introduction by ISBER
1:05 – 1:15 pm Research Sample Intelligence: The Growing Need for Global Data Integration
Lori Ball, Chief Operating Officer, President of Integrated Client Solutions,
BioStorage Technologies, Inc.
1:15-1:25 pm Building a Research Data Integration Plan and Cloud Sourcing Strategy
Brian J. Brunner, Senior Manager Clinical Practice, LabAnswer
1:25-1:35 pm How Data Virtualization Works and the Value it Delivers
Suresh Chandrasekaran, Senior Vice President, Denodo Technologies
1:35 -2:00 pm Panel led Workshop Questions
2:00 -2:15 pm Panel Solution Discussion
2:20 – 2:30 pm Participant Q&A
Agenda
Lori Ball
Lori Ball, MBA
Chief Operating Officer, President of Integrated
Client Solutions
BioStorage Technologies, Inc.
Research Sample Intelligence:
The Growing Need for Global
Data Integration
Biobank
Sample and Data Stakeholders
Biobank Inventory Managers
Manage sample inventories at biobanks to
support research scientist requests
Research Scientists and Organizations
Require samples to support translational science,
biomarker development and research studies
(ex. Industry, Academia, Foundations, Government,
Hospitals, Research Institutes)
Biobank Operations Personnel
Support biobank managers in day-to-day
management of sample storage, logistics,
retrieval, materials and data management.
Patients and Physicians
Collect samples from patients to utilize for
testing, diagnosis and therapy decisions
Development of effective data management
systems is the #1 challenge to individuals
managing biobanks today.1
Analysis of sample data enables us to detect
genetic differences in individuals which can
improve therapy decisions and success.
• 30% of patients over-express the
HER2 protein and are unresponsive to
standard therapy
• 60% of melanoma patients who have the
B-Raf protein were not effectively treated for
skin cancer until vemurafenib was developed
in 2011
Personalized Medicine requires
Samples and Data
“Researchers and doctors both rely heavily on biorepositories to provide them with high-
quality samples that they need to advance personalized medicine and to treat patients.”1
Source:
1 IQPC Global Biobanking Study, February 2015
BiopharmaAcademics
Hospitals
Non-Profits
Laboratories
Research
Institutions
The most important part of data management
• Sample and Data Types – Range of
complexity
• Data Sources - >25B connected devices
today and >50B estimated by 2020
• Data Virtualization (Data Integration) –
Broad virtual data access with open APIs
• Data Visualization – Intuitive, flexible
decision-support
• Data Devices / Tools – Need for standards
• Data Analytics / Analysis Tools
Global Sample Data Integration
Considerations
1Centerwatch, “Cloud Computing Expanding into All Areas of Clinical Trial Conduct”, August 2014
“As cloud computing extends its reach into clinical trials, data analysis, analytics and
visualization are becoming key areas demanded by trial managers1”
Value of
Global Sample Data Integration
● Consolidate global
sample inventory data
● Virtually connect
global sample data to
relevant research data
● Access sample data
anytime from the
cloud from anywhere
in the world.
● Create visual
dashboards and
reports of sample data
to support analytics
and deliver business
insights
● Assess the quality
and value of samples
stored for future
research
● Select the best
samples that are
consented for
research
● Dispose of samples
not needed or
compromised
● Determine which
sample data and
assets to utilize or
share with research
partners
Virtualization Visualization Valorization
Development of effective data management systems is the #1 challenge
facing individuals managing biobanks today.1
Global Sample Data Integration
Opportunities / Challenges
Sources: 1 PharmaVoice, “The Internet of Things, March 2015
Consolidated
Data Center /
Logical Data
Warehouse
SAS
SQL
Oracle
ERP
Data Integration
(physical & virtual)
Cloud Storage Virtualization
Valorization
Visualization
“Technology promises to deliver more value to clinical trials as it enables a real-time view of
the patient experience and collects data that had previously gone uncaptured.”2
Laboratory
Data LIMS
Clinical Trial Data
CTMS
Biobank Data
SMS
Biorepository Data
SMS
Researcher
Data
SDMS
Hospital/Clinic
Data
SDMS
Consent
Brian J. Brunner
Building a Research Data
Integration Plan and Cloud
Sourcing Strategy
Brian J. Brunner
Senior Manager, Clinical Practice
LabAnswer
Development of effective data management systems is the #1 challenge
facing individuals managing biobanks today.1
Global Sample Data Integration
Opportunities / Challenges
Sources: 1 PharmaVoice, “The Internet of Things, March 2015
“Technology promises to deliver more value to clinical trials as it enables a real-time view of
the patient experience and collects data that had previously gone uncaptured.”2
There are 3 primary strategies in the market today for Sample Data Integration:
1. Systems Consolidation
2. Systems Integration
3. Systems Externalization
All of these strategies include an Integration Platform, Harmonized Data Store and
Visualization to enable a single view into Sample Data.
These options will be discussed in the context of cost, complexity and maintenance
On a scale of 1 – 5 with 5 being the highest
• Create a single, consolidated and integrated view to all sample data
• Integration can be either traditional ETL, ESB or Virtualized Database
• Integration requires interfaces with a wide range of systems
• Client and External source data is integrated as required
Operational Data Stores Integration Platform Analytics/Decision Support
Global Sample Data Integration
Laboratory Data
LIMS
Clinical Trial
Data
CTMS
Biobank
Data SMS
Biorepository
Data
SMS
Researcher
Data
SDMS
Hospital/Clinic
Data
SDMS
Consent
Data Aggregation & Visualization Model
Systems Consolidation
Global Sample Data Integration
Opportunities / Challenges
Cost: 5
- Cost to transition all systems
- Infrastructure
- SW Costs
Complexity: 4
- Challenge to harmonize and
standardize during development
- Challenge for single platform
Maintenance: 2
- Lower integration cost ongoing
- Internal System Dependencies
considered for system changes
Internal Data External Data Internal Data External Data
Visualization
Harmonized
Data Store
Integration
Assume: Mass Systems Consolidation, ETL/ESB Integration, Internal Infrastructure
Systems Integration
Global Sample Data Integration
Opportunities / Challenges
Cost: 4
- Cost to integrate all systems
- Infrastructure Costs
- SW Costs
Complexity: 3
- Need harmonize internal data
through integration platform
- Need to integrate ext data
Maintenance: 3
- Integration cost ongoing
- Ongoing Infrastructure and SW
maintenance cost
Internal Data External Data
Visualization
Harmonized
Data Store
Integration
Internal Data External Data
Integration
Assume: ETL/ESB Integration, Complex Master Data Management (MDM), Internal Infrastructure
Systems Externalization
Global Sample Data Integration
Opportunities / Challenges
Cost: 2
- Cost to integrate systems
- SW & Infra costs are leased
- Data Virt lowers integrate cost
Complexity: 3
- Need to harmonize internal data
through integration platform
- Need to integrate ext. data
Maintenance: 2
- Integration cost reduced
- SW & Infra are elastic with
demand and growth
Internal Data External Data
Internal Data External Data
Visualization
Harmonized
Data Store
Integration
Assume: Data Virtualization & Integration Platform, Complex MDM, Cloud Infrastructure
• The Cloud Security Alliance defines cloud computing as:
Cloud computing (‘cloud’) is an evolving term that describes the development of many existing
technologies and approaches to computing into something different. Cloud separates
application and information resources from the underlying infrastructure, and the mechanisms
used to deliver them.
• Other perspectives:
– Cloud computing is processing and storing of data as a service outside of the traditional
processing that takes place inside an organization
– Enables organizations to treat data as a commodity that can be processed, transformed and
delivered by outside organizations in a secure, compliant manner
• Amazon Web Services approved by U.S. DoD as a Service Provider
http://www.informationweek.com/government/cloud-computing/amazon-cloud-services-wins-dod-authorization/d/d-id/1141529
• Other Resources:
– EU Agency for Network and Information Security Risk Assessment Tool
http://www.enisa.europa.eu/activities/risk-management/files/deliverables/cloud-computing-risk-assessment
– DoD Guidance on Cloud Computing
Cloud Sourcing Discussion
Data Integration Strategy Summary
Operational Data Stores Integration Layer Analytics/Decision Support
Selecting the Right Data Integration Strategy
Laboratory Data
LIMS
Clinical Trial
Data CTMS
Biobank
Data SMS
Biorepository
Data
SMS
Researcher
Data
SDMS
Hospital/Clinic
Data
SDMS
Consent
• Consolidate Duplicative Systems, utilize COTS Platforms as basis for Core Systems
• Cloud Platform used for secure processing and storage; elastic based on need
• Data Virtualization enables agile, flexible data integration
• External Service providers can be used to process, harmonize and visualize data
Suresh Chandrasekaran
Suresh Chandrasekaran
Senior Vice President,
Denodo
How Data Virtualization
Works and the Value it
Delivers
What is Data Virtualization?
Data Virtualization combines disparate data sources into a single “virtual” data
abstraction layer (aka information fabric) that provides unified access and integrated
data services to consuming applications in real-time (right-time).
Base View Base View
Base View
Derived View
Connect
&
Virtualize
Combine
&
Integrate
Publish
as a Data
Service
Data Service
(SQL, SOAP, REST,
Widgets …)
DV Enables Agile Info
Architecture – Many to Many
Data Services
Agile BI & Apps
Faster Solution
Optimized
Performance
Any Data,
Anywhere
Integrate New
Sources
BI /Reporting Analytics Visualization Data SharingApplication
Data Virtualization in
Life Sciences
Disparate Data
Any Source
Any Format
Data Virtualization
Right Information
at the Right Time
Business Solutions
Access Information-
as-a-Service
Bio Sample and Research
Intelligence
Data Virtualization Functions
• Access heterogeneous data sources
• Create logical abstraction layer
• Deliver canonical business views
• Data services – access anywhere
• Agile data integration and quality
• Minimize replication (cost, time)
• Enterprise to cloud scalable, secure
Business Need
(Canonical Views)
Real World
(Source Data Views)
Data Virtualization Layer
TrialCRO
Samples
Clients
Consent
Laboratory Data
LIMS
Clinical Trial
Data
CTMS
Biobank
Data SMS
Biorepository
Data
SMS
Researcher
Data
SDMS
Hospital/Clinic
Data
SDMS
Consent
Major Companies Find
Compelling Benefits
NNSA's Product Realization Integrated Digital
Enterprise (PRIDE) program is built on a data
virtualization fabric
“Accessing and aggregating data from
different NNSA sites would not be possible
without Data Virtualization. It expedites
projects by enabling fast and secure
movement of data through different
facilities.“ - Stephanie Elsea, Pantex Plant,
NNSA
Top CIOs in Life Sciences Use
Agile Data & Cloud Intelligence
Amgen, a $17.3 billion pharmaceutical company uses
statistical analysis of data points collected in real time.
The analytics system includes virtualized data
warehousing tools from Denodo. Amgen can draw
conclusions much faster than in the past, says CIO Diana
McKenzie.
Now You Can Too!
WORKSHOP SESSION
Take 2 minutes and discuss as a table
Write down common issues on note cards
WORKSHOP QUESTION #1
What is your current state of sample data integration?
Take 2 minutes and discuss as a table
Write down common issues on note cards
WORKSHOP QUESTION #2
What does an intelligent biobank look like to you?
Take 2 minutes and discuss as a table
Write down common issues on note cards
WORKSHOP QUESTION #3
What obstacles are preventing your success?
Speaker Panel - Solutions Discussion
Q&A
Audience - Q&A Session

More Related Content

What's hot

What's hot (20)

Biobanking
BiobankingBiobanking
Biobanking
 
Vip immunity to infections
Vip immunity to infectionsVip immunity to infections
Vip immunity to infections
 
Autoimmunity
AutoimmunityAutoimmunity
Autoimmunity
 
Immunopathology
ImmunopathologyImmunopathology
Immunopathology
 
The role of infections in autoimmune disease
The role of infections in autoimmune diseaseThe role of infections in autoimmune disease
The role of infections in autoimmune disease
 
Stem Cells and their Therapeutic applications
Stem Cells and their Therapeutic applicationsStem Cells and their Therapeutic applications
Stem Cells and their Therapeutic applications
 
Liver tissue engineering
Liver tissue engineeringLiver tissue engineering
Liver tissue engineering
 
Tolerance & autoimmunity
Tolerance & autoimmunityTolerance & autoimmunity
Tolerance & autoimmunity
 
Tumor immunity
Tumor immunityTumor immunity
Tumor immunity
 
Inflamation
InflamationInflamation
Inflamation
 
Cytokine
CytokineCytokine
Cytokine
 
Hematopoeitic stem cells
Hematopoeitic stem cellsHematopoeitic stem cells
Hematopoeitic stem cells
 
Stem cells niche and therapeutic applications
Stem cells niche and therapeutic applications Stem cells niche and therapeutic applications
Stem cells niche and therapeutic applications
 
Tissue Engineering & Regenerative Medicine
Tissue Engineering & Regenerative MedicineTissue Engineering & Regenerative Medicine
Tissue Engineering & Regenerative Medicine
 
Primary and secondary lymphoid organs
Primary and secondary lymphoid organsPrimary and secondary lymphoid organs
Primary and secondary lymphoid organs
 
Infectious Mononucleosis - Pathology
Infectious Mononucleosis - PathologyInfectious Mononucleosis - Pathology
Infectious Mononucleosis - Pathology
 
43-immunesystem-text
 43-immunesystem-text 43-immunesystem-text
43-immunesystem-text
 
Immunotherapy for cancer
Immunotherapy for cancerImmunotherapy for cancer
Immunotherapy for cancer
 
Cytokines
CytokinesCytokines
Cytokines
 
Transplantation
Transplantation Transplantation
Transplantation
 

Viewers also liked

Reconciling your Enterprise Data Warehouse to Source Systems
Reconciling your Enterprise Data Warehouse to Source SystemsReconciling your Enterprise Data Warehouse to Source Systems
Reconciling your Enterprise Data Warehouse to Source SystemsMethod360
 
Reconciliation Testing Aspects of Trading Systems Software Failures
Reconciliation Testing Aspects of Trading Systems Software FailuresReconciliation Testing Aspects of Trading Systems Software Failures
Reconciliation Testing Aspects of Trading Systems Software FailuresIosif Itkin
 
Hadoop and the Data Warehouse: Point/Counter Point
Hadoop and the Data Warehouse: Point/Counter PointHadoop and the Data Warehouse: Point/Counter Point
Hadoop and the Data Warehouse: Point/Counter PointInside Analysis
 
JBoss Enterprise Data Services (Data Virtualization)
JBoss Enterprise Data Services (Data Virtualization)JBoss Enterprise Data Services (Data Virtualization)
JBoss Enterprise Data Services (Data Virtualization)plarsen67
 
Akili Upstream Oil & Gas Data Conversion Solution
Akili Upstream Oil & Gas Data Conversion SolutionAkili Upstream Oil & Gas Data Conversion Solution
Akili Upstream Oil & Gas Data Conversion Solutionrnaramore
 
Dasar dasar pengujian perangkat lunak
Dasar dasar pengujian perangkat lunakDasar dasar pengujian perangkat lunak
Dasar dasar pengujian perangkat lunakerwingmanplp
 
Data Mining & Data Warehousing Lecture Notes
Data Mining & Data Warehousing Lecture NotesData Mining & Data Warehousing Lecture Notes
Data Mining & Data Warehousing Lecture NotesFellowBuddy.com
 
Akili Data Integration using PPDM
Akili Data Integration using PPDMAkili Data Integration using PPDM
Akili Data Integration using PPDMrnaramore
 
Making the leap to BI on Hadoop by Mariani, dave @ atscale
Making the leap to BI on Hadoop by Mariani, dave @ atscaleMaking the leap to BI on Hadoop by Mariani, dave @ atscale
Making the leap to BI on Hadoop by Mariani, dave @ atscaleTin Ho
 
SOP dan Peraturan SPAN (Sistem Perbendaharaan dan Anggaran Negara)
SOP dan Peraturan SPAN (Sistem Perbendaharaan dan Anggaran Negara)SOP dan Peraturan SPAN (Sistem Perbendaharaan dan Anggaran Negara)
SOP dan Peraturan SPAN (Sistem Perbendaharaan dan Anggaran Negara)Ahmad Abdul Haq
 
Contoh Proposal Usaha Digital Printing
Contoh Proposal Usaha Digital PrintingContoh Proposal Usaha Digital Printing
Contoh Proposal Usaha Digital PrintingAlfi Nugraha
 
Hadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data WarehouseHadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data WarehouseDataWorks Summit
 
Sistem Informasi Manajemen Rumah Sakit (SIMRS) + Bridging BPJS
Sistem Informasi Manajemen Rumah Sakit (SIMRS) + Bridging BPJSSistem Informasi Manajemen Rumah Sakit (SIMRS) + Bridging BPJS
Sistem Informasi Manajemen Rumah Sakit (SIMRS) + Bridging BPJSonemedic
 
Data virtualization, Data Federation & IaaS with Jboss Teiid
Data virtualization, Data Federation & IaaS with Jboss TeiidData virtualization, Data Federation & IaaS with Jboss Teiid
Data virtualization, Data Federation & IaaS with Jboss TeiidAnil Allewar
 

Viewers also liked (19)

Reconciling your Enterprise Data Warehouse to Source Systems
Reconciling your Enterprise Data Warehouse to Source SystemsReconciling your Enterprise Data Warehouse to Source Systems
Reconciling your Enterprise Data Warehouse to Source Systems
 
Reconciliation Testing Aspects of Trading Systems Software Failures
Reconciliation Testing Aspects of Trading Systems Software FailuresReconciliation Testing Aspects of Trading Systems Software Failures
Reconciliation Testing Aspects of Trading Systems Software Failures
 
UAPPA W dalam SAPP
UAPPA W dalam SAPPUAPPA W dalam SAPP
UAPPA W dalam SAPP
 
Hadoop and the Data Warehouse: Point/Counter Point
Hadoop and the Data Warehouse: Point/Counter PointHadoop and the Data Warehouse: Point/Counter Point
Hadoop and the Data Warehouse: Point/Counter Point
 
Sakti for satker
Sakti for satkerSakti for satker
Sakti for satker
 
JBoss Enterprise Data Services (Data Virtualization)
JBoss Enterprise Data Services (Data Virtualization)JBoss Enterprise Data Services (Data Virtualization)
JBoss Enterprise Data Services (Data Virtualization)
 
Mengenal Lebih Jauh SPAN
Mengenal Lebih Jauh SPANMengenal Lebih Jauh SPAN
Mengenal Lebih Jauh SPAN
 
Akili Upstream Oil & Gas Data Conversion Solution
Akili Upstream Oil & Gas Data Conversion SolutionAkili Upstream Oil & Gas Data Conversion Solution
Akili Upstream Oil & Gas Data Conversion Solution
 
Dasar dasar pengujian perangkat lunak
Dasar dasar pengujian perangkat lunakDasar dasar pengujian perangkat lunak
Dasar dasar pengujian perangkat lunak
 
Data Mining & Data Warehousing Lecture Notes
Data Mining & Data Warehousing Lecture NotesData Mining & Data Warehousing Lecture Notes
Data Mining & Data Warehousing Lecture Notes
 
Span dan sakti alt
Span dan sakti altSpan dan sakti alt
Span dan sakti alt
 
Sistem informasi manajemen
Sistem informasi manajemenSistem informasi manajemen
Sistem informasi manajemen
 
Akili Data Integration using PPDM
Akili Data Integration using PPDMAkili Data Integration using PPDM
Akili Data Integration using PPDM
 
Making the leap to BI on Hadoop by Mariani, dave @ atscale
Making the leap to BI on Hadoop by Mariani, dave @ atscaleMaking the leap to BI on Hadoop by Mariani, dave @ atscale
Making the leap to BI on Hadoop by Mariani, dave @ atscale
 
SOP dan Peraturan SPAN (Sistem Perbendaharaan dan Anggaran Negara)
SOP dan Peraturan SPAN (Sistem Perbendaharaan dan Anggaran Negara)SOP dan Peraturan SPAN (Sistem Perbendaharaan dan Anggaran Negara)
SOP dan Peraturan SPAN (Sistem Perbendaharaan dan Anggaran Negara)
 
Contoh Proposal Usaha Digital Printing
Contoh Proposal Usaha Digital PrintingContoh Proposal Usaha Digital Printing
Contoh Proposal Usaha Digital Printing
 
Hadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data WarehouseHadoop and Enterprise Data Warehouse
Hadoop and Enterprise Data Warehouse
 
Sistem Informasi Manajemen Rumah Sakit (SIMRS) + Bridging BPJS
Sistem Informasi Manajemen Rumah Sakit (SIMRS) + Bridging BPJSSistem Informasi Manajemen Rumah Sakit (SIMRS) + Bridging BPJS
Sistem Informasi Manajemen Rumah Sakit (SIMRS) + Bridging BPJS
 
Data virtualization, Data Federation & IaaS with Jboss Teiid
Data virtualization, Data Federation & IaaS with Jboss TeiidData virtualization, Data Federation & IaaS with Jboss Teiid
Data virtualization, Data Federation & IaaS with Jboss Teiid
 

Similar to Building an Intelligent Biobank with Global Data Integration

tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...David Peyruc
 
Microsoft: A Waking Giant In Healthcare Analytics and Big Data
Microsoft: A Waking Giant In Healthcare Analytics and Big DataMicrosoft: A Waking Giant In Healthcare Analytics and Big Data
Microsoft: A Waking Giant In Healthcare Analytics and Big DataHealth Catalyst
 
Toward a FAIR Biomedical Data Ecosystem
Toward a FAIR Biomedical Data EcosystemToward a FAIR Biomedical Data Ecosystem
Toward a FAIR Biomedical Data EcosystemGlobus
 
Data Virtualization Modernizes Biobanking
Data Virtualization Modernizes BiobankingData Virtualization Modernizes Biobanking
Data Virtualization Modernizes BiobankingDenodo
 
Microsoft: A Waking Giant in Healthcare Analytics and Big Data
Microsoft: A Waking Giant in Healthcare Analytics and Big DataMicrosoft: A Waking Giant in Healthcare Analytics and Big Data
Microsoft: A Waking Giant in Healthcare Analytics and Big DataDale Sanders
 
CTO Perspectives: What's Next for Data Management and Healthcare?
CTO Perspectives: What's Next for Data Management and Healthcare?CTO Perspectives: What's Next for Data Management and Healthcare?
CTO Perspectives: What's Next for Data Management and Healthcare?Health Catalyst
 
Maven and google pharma r&d (1)
Maven and google pharma r&d  (1)Maven and google pharma r&d  (1)
Maven and google pharma r&d (1)Matt Barnes
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
 
Regulatory Intelligence
Regulatory IntelligenceRegulatory Intelligence
Regulatory IntelligenceArmin Torres
 
Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...
Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...
Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...VMware Tanzu
 
Clinicaldatamanagementindiaasahub 130313225150-phpapp01
Clinicaldatamanagementindiaasahub 130313225150-phpapp01Clinicaldatamanagementindiaasahub 130313225150-phpapp01
Clinicaldatamanagementindiaasahub 130313225150-phpapp01Upendra Agarwal
 
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Aridhia Informatics Ltd
 
Enterprise Analytics: Serving Big Data Projects for Healthcare
Enterprise Analytics: Serving Big Data Projects for HealthcareEnterprise Analytics: Serving Big Data Projects for Healthcare
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
 
Meeting the Computational Challenges Associated with Human Health
Meeting the Computational Challenges Associated with Human HealthMeeting the Computational Challenges Associated with Human Health
Meeting the Computational Challenges Associated with Human HealthPhilip Bourne
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
 
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareThe Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
 
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareThe Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
 
Hospital Cloud Forum - thoughts for panel
Hospital Cloud Forum - thoughts for panelHospital Cloud Forum - thoughts for panel
Hospital Cloud Forum - thoughts for panelKent State University
 
Starting the Hadoop Journey at a Global Leader in Cancer Research
Starting the Hadoop Journey at a Global Leader in Cancer ResearchStarting the Hadoop Journey at a Global Leader in Cancer Research
Starting the Hadoop Journey at a Global Leader in Cancer ResearchDataWorks Summit/Hadoop Summit
 

Similar to Building an Intelligent Biobank with Global Data Integration (20)

tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...
tranSMART Community Meeting 5-7 Nov 13 - Session 5: Recent tranSMART Lessons ...
 
Microsoft: A Waking Giant In Healthcare Analytics and Big Data
Microsoft: A Waking Giant In Healthcare Analytics and Big DataMicrosoft: A Waking Giant In Healthcare Analytics and Big Data
Microsoft: A Waking Giant In Healthcare Analytics and Big Data
 
Toward a FAIR Biomedical Data Ecosystem
Toward a FAIR Biomedical Data EcosystemToward a FAIR Biomedical Data Ecosystem
Toward a FAIR Biomedical Data Ecosystem
 
Data Virtualization Modernizes Biobanking
Data Virtualization Modernizes BiobankingData Virtualization Modernizes Biobanking
Data Virtualization Modernizes Biobanking
 
Microsoft: A Waking Giant in Healthcare Analytics and Big Data
Microsoft: A Waking Giant in Healthcare Analytics and Big DataMicrosoft: A Waking Giant in Healthcare Analytics and Big Data
Microsoft: A Waking Giant in Healthcare Analytics and Big Data
 
CTO Perspectives: What's Next for Data Management and Healthcare?
CTO Perspectives: What's Next for Data Management and Healthcare?CTO Perspectives: What's Next for Data Management and Healthcare?
CTO Perspectives: What's Next for Data Management and Healthcare?
 
Maven and google pharma r&d (1)
Maven and google pharma r&d  (1)Maven and google pharma r&d  (1)
Maven and google pharma r&d (1)
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health System
 
Regulatory Intelligence
Regulatory IntelligenceRegulatory Intelligence
Regulatory Intelligence
 
Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...
Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...
Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...
 
Clinicaldatamanagementindiaasahub 130313225150-phpapp01
Clinicaldatamanagementindiaasahub 130313225150-phpapp01Clinicaldatamanagementindiaasahub 130313225150-phpapp01
Clinicaldatamanagementindiaasahub 130313225150-phpapp01
 
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...
 
Enterprise Analytics: Serving Big Data Projects for Healthcare
Enterprise Analytics: Serving Big Data Projects for HealthcareEnterprise Analytics: Serving Big Data Projects for Healthcare
Enterprise Analytics: Serving Big Data Projects for Healthcare
 
Meeting the Computational Challenges Associated with Human Health
Meeting the Computational Challenges Associated with Human HealthMeeting the Computational Challenges Associated with Human Health
Meeting the Computational Challenges Associated with Human Health
 
Shifting the goal post – from high impact journals to high impact data
 Shifting the goal post – from high impact journals to high impact data Shifting the goal post – from high impact journals to high impact data
Shifting the goal post – from high impact journals to high impact data
 
Data Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health SystemData Harmonization for a Molecularly Driven Health System
Data Harmonization for a Molecularly Driven Health System
 
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareThe Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of Healthcare
 
The Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of HealthcareThe Data Operating System: Changing the Digital Trajectory of Healthcare
The Data Operating System: Changing the Digital Trajectory of Healthcare
 
Hospital Cloud Forum - thoughts for panel
Hospital Cloud Forum - thoughts for panelHospital Cloud Forum - thoughts for panel
Hospital Cloud Forum - thoughts for panel
 
Starting the Hadoop Journey at a Global Leader in Cancer Research
Starting the Hadoop Journey at a Global Leader in Cancer ResearchStarting the Hadoop Journey at a Global Leader in Cancer Research
Starting the Hadoop Journey at a Global Leader in Cancer Research
 

More from Denodo

Enterprise Monitoring and Auditing in Denodo
Enterprise Monitoring and Auditing in DenodoEnterprise Monitoring and Auditing in Denodo
Enterprise Monitoring and Auditing in DenodoDenodo
 
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps ApproachLunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps ApproachDenodo
 
Achieving Self-Service Analytics with a Governed Data Services Layer
Achieving Self-Service Analytics with a Governed Data Services LayerAchieving Self-Service Analytics with a Governed Data Services Layer
Achieving Self-Service Analytics with a Governed Data Services LayerDenodo
 
What you need to know about Generative AI and Data Management?
What you need to know about Generative AI and Data Management?What you need to know about Generative AI and Data Management?
What you need to know about Generative AI and Data Management?Denodo
 
Mastering Data Compliance in a Dynamic Business Landscape
Mastering Data Compliance in a Dynamic Business LandscapeMastering Data Compliance in a Dynamic Business Landscape
Mastering Data Compliance in a Dynamic Business LandscapeDenodo
 
Denodo Partner Connect: Business Value Demo with Denodo Demo Lite
Denodo Partner Connect: Business Value Demo with Denodo Demo LiteDenodo Partner Connect: Business Value Demo with Denodo Demo Lite
Denodo Partner Connect: Business Value Demo with Denodo Demo LiteDenodo
 
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...Denodo
 
Drive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory ComplianceDrive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory ComplianceDenodo
 
Знакомство с виртуализацией данных для профессионалов в области данных
Знакомство с виртуализацией данных для профессионалов в области данныхЗнакомство с виртуализацией данных для профессионалов в области данных
Знакомство с виртуализацией данных для профессионалов в области данныхDenodo
 
Data Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
Data Democratization: A Secret Sauce to Say Goodbye to Data FragmentationData Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
Data Democratization: A Secret Sauce to Say Goodbye to Data FragmentationDenodo
 
Denodo Partner Connect - Technical Webinar - Ask Me Anything
Denodo Partner Connect - Technical Webinar - Ask Me AnythingDenodo Partner Connect - Technical Webinar - Ask Me Anything
Denodo Partner Connect - Technical Webinar - Ask Me AnythingDenodo
 
Lunch and Learn ANZ: Key Takeaways for 2023!
Lunch and Learn ANZ: Key Takeaways for 2023!Lunch and Learn ANZ: Key Takeaways for 2023!
Lunch and Learn ANZ: Key Takeaways for 2023!Denodo
 
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way ForwardIt’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way ForwardDenodo
 
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...Denodo
 
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...Denodo
 
How to Build Your Data Marketplace with Data Virtualization?
How to Build Your Data Marketplace with Data Virtualization?How to Build Your Data Marketplace with Data Virtualization?
How to Build Your Data Marketplace with Data Virtualization?Denodo
 
Webinar #2 - Transforming Challenges into Opportunities for Credit Unions
Webinar #2 - Transforming Challenges into Opportunities for Credit UnionsWebinar #2 - Transforming Challenges into Opportunities for Credit Unions
Webinar #2 - Transforming Challenges into Opportunities for Credit UnionsDenodo
 
Enabling Data Catalog users with advanced usability
Enabling Data Catalog users with advanced usabilityEnabling Data Catalog users with advanced usability
Enabling Data Catalog users with advanced usabilityDenodo
 
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...Denodo
 
GenAI y el futuro de la gestión de datos: mitos y realidades
GenAI y el futuro de la gestión de datos: mitos y realidadesGenAI y el futuro de la gestión de datos: mitos y realidades
GenAI y el futuro de la gestión de datos: mitos y realidadesDenodo
 

More from Denodo (20)

Enterprise Monitoring and Auditing in Denodo
Enterprise Monitoring and Auditing in DenodoEnterprise Monitoring and Auditing in Denodo
Enterprise Monitoring and Auditing in Denodo
 
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps ApproachLunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
Lunch and Learn ANZ: Mastering Cloud Data Cost Control: A FinOps Approach
 
Achieving Self-Service Analytics with a Governed Data Services Layer
Achieving Self-Service Analytics with a Governed Data Services LayerAchieving Self-Service Analytics with a Governed Data Services Layer
Achieving Self-Service Analytics with a Governed Data Services Layer
 
What you need to know about Generative AI and Data Management?
What you need to know about Generative AI and Data Management?What you need to know about Generative AI and Data Management?
What you need to know about Generative AI and Data Management?
 
Mastering Data Compliance in a Dynamic Business Landscape
Mastering Data Compliance in a Dynamic Business LandscapeMastering Data Compliance in a Dynamic Business Landscape
Mastering Data Compliance in a Dynamic Business Landscape
 
Denodo Partner Connect: Business Value Demo with Denodo Demo Lite
Denodo Partner Connect: Business Value Demo with Denodo Demo LiteDenodo Partner Connect: Business Value Demo with Denodo Demo Lite
Denodo Partner Connect: Business Value Demo with Denodo Demo Lite
 
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
Expert Panel: Overcoming Challenges with Distributed Data to Maximize Busines...
 
Drive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory ComplianceDrive Data Privacy Regulatory Compliance
Drive Data Privacy Regulatory Compliance
 
Знакомство с виртуализацией данных для профессионалов в области данных
Знакомство с виртуализацией данных для профессионалов в области данныхЗнакомство с виртуализацией данных для профессионалов в области данных
Знакомство с виртуализацией данных для профессионалов в области данных
 
Data Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
Data Democratization: A Secret Sauce to Say Goodbye to Data FragmentationData Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
Data Democratization: A Secret Sauce to Say Goodbye to Data Fragmentation
 
Denodo Partner Connect - Technical Webinar - Ask Me Anything
Denodo Partner Connect - Technical Webinar - Ask Me AnythingDenodo Partner Connect - Technical Webinar - Ask Me Anything
Denodo Partner Connect - Technical Webinar - Ask Me Anything
 
Lunch and Learn ANZ: Key Takeaways for 2023!
Lunch and Learn ANZ: Key Takeaways for 2023!Lunch and Learn ANZ: Key Takeaways for 2023!
Lunch and Learn ANZ: Key Takeaways for 2023!
 
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way ForwardIt’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
It’s a Wrap! 2023 – A Groundbreaking Year for AI and The Way Forward
 
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
Quels sont les facteurs-clés de succès pour appliquer au mieux le RGPD à votr...
 
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
Lunch and Learn ANZ: Achieving Self-Service Analytics with a Governed Data Se...
 
How to Build Your Data Marketplace with Data Virtualization?
How to Build Your Data Marketplace with Data Virtualization?How to Build Your Data Marketplace with Data Virtualization?
How to Build Your Data Marketplace with Data Virtualization?
 
Webinar #2 - Transforming Challenges into Opportunities for Credit Unions
Webinar #2 - Transforming Challenges into Opportunities for Credit UnionsWebinar #2 - Transforming Challenges into Opportunities for Credit Unions
Webinar #2 - Transforming Challenges into Opportunities for Credit Unions
 
Enabling Data Catalog users with advanced usability
Enabling Data Catalog users with advanced usabilityEnabling Data Catalog users with advanced usability
Enabling Data Catalog users with advanced usability
 
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
Denodo Partner Connect: Technical Webinar - Architect Associate Certification...
 
GenAI y el futuro de la gestión de datos: mitos y realidades
GenAI y el futuro de la gestión de datos: mitos y realidadesGenAI y el futuro de la gestión de datos: mitos y realidades
GenAI y el futuro de la gestión de datos: mitos y realidades
 

Recently uploaded

ANEMIA IN PREGNANCY by Dr. Akebom Kidanemariam
ANEMIA IN PREGNANCY by Dr. Akebom KidanemariamANEMIA IN PREGNANCY by Dr. Akebom Kidanemariam
ANEMIA IN PREGNANCY by Dr. Akebom KidanemariamAkebom Gebremichael
 
SCHOOL HEALTH SERVICES.pptx made by Sapna Thakur
SCHOOL HEALTH SERVICES.pptx made by Sapna ThakurSCHOOL HEALTH SERVICES.pptx made by Sapna Thakur
SCHOOL HEALTH SERVICES.pptx made by Sapna ThakurSapna Thakur
 
Giftedness: Understanding Everyday Neurobiology for Self-Knowledge
Giftedness: Understanding Everyday Neurobiology for Self-KnowledgeGiftedness: Understanding Everyday Neurobiology for Self-Knowledge
Giftedness: Understanding Everyday Neurobiology for Self-Knowledgeassessoriafabianodea
 
PULMONARY EDEMA AND ITS MANAGEMENT.pdf
PULMONARY EDEMA AND  ITS  MANAGEMENT.pdfPULMONARY EDEMA AND  ITS  MANAGEMENT.pdf
PULMONARY EDEMA AND ITS MANAGEMENT.pdfDolisha Warbi
 
Screening for colorectal cancer AAU.pptx
Screening for colorectal cancer AAU.pptxScreening for colorectal cancer AAU.pptx
Screening for colorectal cancer AAU.pptxtadehabte
 
Systemic Lupus Erythematosus -SLE PT2.ppt
Systemic  Lupus  Erythematosus -SLE PT2.pptSystemic  Lupus  Erythematosus -SLE PT2.ppt
Systemic Lupus Erythematosus -SLE PT2.pptraviapr7
 
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...Badalona Serveis Assistencials
 
Basic principles involved in the traditional systems of medicine PDF.pdf
Basic principles involved in the traditional systems of medicine PDF.pdfBasic principles involved in the traditional systems of medicine PDF.pdf
Basic principles involved in the traditional systems of medicine PDF.pdfDivya Kanojiya
 
Hypersensitivity and its classification .pptx
Hypersensitivity and its classification .pptxHypersensitivity and its classification .pptx
Hypersensitivity and its classification .pptxAkshay Shetty
 
SGK HÓA SINH NĂNG LƯỢNG SINH HỌC 2006.pdf
SGK HÓA SINH NĂNG LƯỢNG SINH HỌC 2006.pdfSGK HÓA SINH NĂNG LƯỢNG SINH HỌC 2006.pdf
SGK HÓA SINH NĂNG LƯỢNG SINH HỌC 2006.pdfHongBiThi1
 
Myelin Oligodendrocyte Glycoprotein antibody associated disease (MOGAD)
Myelin Oligodendrocyte Glycoprotein antibody associated disease (MOGAD)Myelin Oligodendrocyte Glycoprotein antibody associated disease (MOGAD)
Myelin Oligodendrocyte Glycoprotein antibody associated disease (MOGAD)MohamadAlhes
 
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptxSYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptxdrashraf369
 
Units of Radiation Measurements, Quality Specification, Half-Value Thickness,...
Units of Radiation Measurements, Quality Specification, Half-Value Thickness,...Units of Radiation Measurements, Quality Specification, Half-Value Thickness,...
Units of Radiation Measurements, Quality Specification, Half-Value Thickness,...Dr. Dheeraj Kumar
 
Measurement of Radiation and Dosimetric Procedure.pptx
Measurement of Radiation and Dosimetric Procedure.pptxMeasurement of Radiation and Dosimetric Procedure.pptx
Measurement of Radiation and Dosimetric Procedure.pptxDr. Dheeraj Kumar
 
Primary headache and facial pain. (2024)
Primary headache and facial pain. (2024)Primary headache and facial pain. (2024)
Primary headache and facial pain. (2024)Mohamed Rizk Khodair
 
CCSC6142 Week 3 Research ethics - Long Hoang.pdf
CCSC6142 Week 3 Research ethics - Long Hoang.pdfCCSC6142 Week 3 Research ethics - Long Hoang.pdf
CCSC6142 Week 3 Research ethics - Long Hoang.pdfMyThaoAiDoan
 
Chronic-Fatigue-Syndrome-CFS-Understanding-a-Complex-Disorder.pptx
Chronic-Fatigue-Syndrome-CFS-Understanding-a-Complex-Disorder.pptxChronic-Fatigue-Syndrome-CFS-Understanding-a-Complex-Disorder.pptx
Chronic-Fatigue-Syndrome-CFS-Understanding-a-Complex-Disorder.pptxSasikiranMarri
 
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...MehranMouzam
 
Radiation Dosimetry Parameters and Isodose Curves.pptx
Radiation Dosimetry Parameters and Isodose Curves.pptxRadiation Dosimetry Parameters and Isodose Curves.pptx
Radiation Dosimetry Parameters and Isodose Curves.pptxDr. Dheeraj Kumar
 
HERBS AS HEALTH FOOD - Brief introduction and therapeutic applications of: N...
HERBS AS HEALTH FOOD - Brief introduction and therapeutic applications of:  N...HERBS AS HEALTH FOOD - Brief introduction and therapeutic applications of:  N...
HERBS AS HEALTH FOOD - Brief introduction and therapeutic applications of: N...Divya Kanojiya
 

Recently uploaded (20)

ANEMIA IN PREGNANCY by Dr. Akebom Kidanemariam
ANEMIA IN PREGNANCY by Dr. Akebom KidanemariamANEMIA IN PREGNANCY by Dr. Akebom Kidanemariam
ANEMIA IN PREGNANCY by Dr. Akebom Kidanemariam
 
SCHOOL HEALTH SERVICES.pptx made by Sapna Thakur
SCHOOL HEALTH SERVICES.pptx made by Sapna ThakurSCHOOL HEALTH SERVICES.pptx made by Sapna Thakur
SCHOOL HEALTH SERVICES.pptx made by Sapna Thakur
 
Giftedness: Understanding Everyday Neurobiology for Self-Knowledge
Giftedness: Understanding Everyday Neurobiology for Self-KnowledgeGiftedness: Understanding Everyday Neurobiology for Self-Knowledge
Giftedness: Understanding Everyday Neurobiology for Self-Knowledge
 
PULMONARY EDEMA AND ITS MANAGEMENT.pdf
PULMONARY EDEMA AND  ITS  MANAGEMENT.pdfPULMONARY EDEMA AND  ITS  MANAGEMENT.pdf
PULMONARY EDEMA AND ITS MANAGEMENT.pdf
 
Screening for colorectal cancer AAU.pptx
Screening for colorectal cancer AAU.pptxScreening for colorectal cancer AAU.pptx
Screening for colorectal cancer AAU.pptx
 
Systemic Lupus Erythematosus -SLE PT2.ppt
Systemic  Lupus  Erythematosus -SLE PT2.pptSystemic  Lupus  Erythematosus -SLE PT2.ppt
Systemic Lupus Erythematosus -SLE PT2.ppt
 
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
Presentació "Real-Life VR Integration for Mild Cognitive Impairment Rehabilit...
 
Basic principles involved in the traditional systems of medicine PDF.pdf
Basic principles involved in the traditional systems of medicine PDF.pdfBasic principles involved in the traditional systems of medicine PDF.pdf
Basic principles involved in the traditional systems of medicine PDF.pdf
 
Hypersensitivity and its classification .pptx
Hypersensitivity and its classification .pptxHypersensitivity and its classification .pptx
Hypersensitivity and its classification .pptx
 
SGK HÓA SINH NĂNG LƯỢNG SINH HỌC 2006.pdf
SGK HÓA SINH NĂNG LƯỢNG SINH HỌC 2006.pdfSGK HÓA SINH NĂNG LƯỢNG SINH HỌC 2006.pdf
SGK HÓA SINH NĂNG LƯỢNG SINH HỌC 2006.pdf
 
Myelin Oligodendrocyte Glycoprotein antibody associated disease (MOGAD)
Myelin Oligodendrocyte Glycoprotein antibody associated disease (MOGAD)Myelin Oligodendrocyte Glycoprotein antibody associated disease (MOGAD)
Myelin Oligodendrocyte Glycoprotein antibody associated disease (MOGAD)
 
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptxSYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
SYNDESMOTIC INJURY- ANATOMICAL REPAIR.pptx
 
Units of Radiation Measurements, Quality Specification, Half-Value Thickness,...
Units of Radiation Measurements, Quality Specification, Half-Value Thickness,...Units of Radiation Measurements, Quality Specification, Half-Value Thickness,...
Units of Radiation Measurements, Quality Specification, Half-Value Thickness,...
 
Measurement of Radiation and Dosimetric Procedure.pptx
Measurement of Radiation and Dosimetric Procedure.pptxMeasurement of Radiation and Dosimetric Procedure.pptx
Measurement of Radiation and Dosimetric Procedure.pptx
 
Primary headache and facial pain. (2024)
Primary headache and facial pain. (2024)Primary headache and facial pain. (2024)
Primary headache and facial pain. (2024)
 
CCSC6142 Week 3 Research ethics - Long Hoang.pdf
CCSC6142 Week 3 Research ethics - Long Hoang.pdfCCSC6142 Week 3 Research ethics - Long Hoang.pdf
CCSC6142 Week 3 Research ethics - Long Hoang.pdf
 
Chronic-Fatigue-Syndrome-CFS-Understanding-a-Complex-Disorder.pptx
Chronic-Fatigue-Syndrome-CFS-Understanding-a-Complex-Disorder.pptxChronic-Fatigue-Syndrome-CFS-Understanding-a-Complex-Disorder.pptx
Chronic-Fatigue-Syndrome-CFS-Understanding-a-Complex-Disorder.pptx
 
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
Study on the Impact of FOCUS-PDCA Management Model on the Disinfection Qualit...
 
Radiation Dosimetry Parameters and Isodose Curves.pptx
Radiation Dosimetry Parameters and Isodose Curves.pptxRadiation Dosimetry Parameters and Isodose Curves.pptx
Radiation Dosimetry Parameters and Isodose Curves.pptx
 
HERBS AS HEALTH FOOD - Brief introduction and therapeutic applications of: N...
HERBS AS HEALTH FOOD - Brief introduction and therapeutic applications of:  N...HERBS AS HEALTH FOOD - Brief introduction and therapeutic applications of:  N...
HERBS AS HEALTH FOOD - Brief introduction and therapeutic applications of: N...
 

Building an Intelligent Biobank with Global Data Integration

  • 1. Lori Ball, Chief Operating Officer, President of Integrated Client Solutions, BioStorage Technologies, Inc. Brian J. Brunner, Senior Manager, Clinical Practice, LabAnswer Suresh Chandrasekaran, Senior Vice President, Denodo Technologies Building an Intelligent Biobank to Power Research Decision-Making
  • 2. 1:00 – 1:05 pm Workshop Introduction by ISBER 1:05 – 1:15 pm Research Sample Intelligence: The Growing Need for Global Data Integration Lori Ball, Chief Operating Officer, President of Integrated Client Solutions, BioStorage Technologies, Inc. 1:15-1:25 pm Building a Research Data Integration Plan and Cloud Sourcing Strategy Brian J. Brunner, Senior Manager Clinical Practice, LabAnswer 1:25-1:35 pm How Data Virtualization Works and the Value it Delivers Suresh Chandrasekaran, Senior Vice President, Denodo Technologies 1:35 -2:00 pm Panel led Workshop Questions 2:00 -2:15 pm Panel Solution Discussion 2:20 – 2:30 pm Participant Q&A Agenda
  • 3. Lori Ball Lori Ball, MBA Chief Operating Officer, President of Integrated Client Solutions BioStorage Technologies, Inc. Research Sample Intelligence: The Growing Need for Global Data Integration
  • 4. Biobank Sample and Data Stakeholders Biobank Inventory Managers Manage sample inventories at biobanks to support research scientist requests Research Scientists and Organizations Require samples to support translational science, biomarker development and research studies (ex. Industry, Academia, Foundations, Government, Hospitals, Research Institutes) Biobank Operations Personnel Support biobank managers in day-to-day management of sample storage, logistics, retrieval, materials and data management. Patients and Physicians Collect samples from patients to utilize for testing, diagnosis and therapy decisions
  • 5. Development of effective data management systems is the #1 challenge to individuals managing biobanks today.1 Analysis of sample data enables us to detect genetic differences in individuals which can improve therapy decisions and success. • 30% of patients over-express the HER2 protein and are unresponsive to standard therapy • 60% of melanoma patients who have the B-Raf protein were not effectively treated for skin cancer until vemurafenib was developed in 2011 Personalized Medicine requires Samples and Data “Researchers and doctors both rely heavily on biorepositories to provide them with high- quality samples that they need to advance personalized medicine and to treat patients.”1 Source: 1 IQPC Global Biobanking Study, February 2015 BiopharmaAcademics Hospitals Non-Profits Laboratories Research Institutions The most important part of data management
  • 6. • Sample and Data Types – Range of complexity • Data Sources - >25B connected devices today and >50B estimated by 2020 • Data Virtualization (Data Integration) – Broad virtual data access with open APIs • Data Visualization – Intuitive, flexible decision-support • Data Devices / Tools – Need for standards • Data Analytics / Analysis Tools Global Sample Data Integration Considerations 1Centerwatch, “Cloud Computing Expanding into All Areas of Clinical Trial Conduct”, August 2014 “As cloud computing extends its reach into clinical trials, data analysis, analytics and visualization are becoming key areas demanded by trial managers1”
  • 7. Value of Global Sample Data Integration ● Consolidate global sample inventory data ● Virtually connect global sample data to relevant research data ● Access sample data anytime from the cloud from anywhere in the world. ● Create visual dashboards and reports of sample data to support analytics and deliver business insights ● Assess the quality and value of samples stored for future research ● Select the best samples that are consented for research ● Dispose of samples not needed or compromised ● Determine which sample data and assets to utilize or share with research partners Virtualization Visualization Valorization
  • 8. Development of effective data management systems is the #1 challenge facing individuals managing biobanks today.1 Global Sample Data Integration Opportunities / Challenges Sources: 1 PharmaVoice, “The Internet of Things, March 2015 Consolidated Data Center / Logical Data Warehouse SAS SQL Oracle ERP Data Integration (physical & virtual) Cloud Storage Virtualization Valorization Visualization “Technology promises to deliver more value to clinical trials as it enables a real-time view of the patient experience and collects data that had previously gone uncaptured.”2 Laboratory Data LIMS Clinical Trial Data CTMS Biobank Data SMS Biorepository Data SMS Researcher Data SDMS Hospital/Clinic Data SDMS Consent
  • 9. Brian J. Brunner Building a Research Data Integration Plan and Cloud Sourcing Strategy Brian J. Brunner Senior Manager, Clinical Practice LabAnswer
  • 10. Development of effective data management systems is the #1 challenge facing individuals managing biobanks today.1 Global Sample Data Integration Opportunities / Challenges Sources: 1 PharmaVoice, “The Internet of Things, March 2015 “Technology promises to deliver more value to clinical trials as it enables a real-time view of the patient experience and collects data that had previously gone uncaptured.”2 There are 3 primary strategies in the market today for Sample Data Integration: 1. Systems Consolidation 2. Systems Integration 3. Systems Externalization All of these strategies include an Integration Platform, Harmonized Data Store and Visualization to enable a single view into Sample Data. These options will be discussed in the context of cost, complexity and maintenance On a scale of 1 – 5 with 5 being the highest
  • 11. • Create a single, consolidated and integrated view to all sample data • Integration can be either traditional ETL, ESB or Virtualized Database • Integration requires interfaces with a wide range of systems • Client and External source data is integrated as required Operational Data Stores Integration Platform Analytics/Decision Support Global Sample Data Integration Laboratory Data LIMS Clinical Trial Data CTMS Biobank Data SMS Biorepository Data SMS Researcher Data SDMS Hospital/Clinic Data SDMS Consent Data Aggregation & Visualization Model
  • 12. Systems Consolidation Global Sample Data Integration Opportunities / Challenges Cost: 5 - Cost to transition all systems - Infrastructure - SW Costs Complexity: 4 - Challenge to harmonize and standardize during development - Challenge for single platform Maintenance: 2 - Lower integration cost ongoing - Internal System Dependencies considered for system changes Internal Data External Data Internal Data External Data Visualization Harmonized Data Store Integration Assume: Mass Systems Consolidation, ETL/ESB Integration, Internal Infrastructure
  • 13. Systems Integration Global Sample Data Integration Opportunities / Challenges Cost: 4 - Cost to integrate all systems - Infrastructure Costs - SW Costs Complexity: 3 - Need harmonize internal data through integration platform - Need to integrate ext data Maintenance: 3 - Integration cost ongoing - Ongoing Infrastructure and SW maintenance cost Internal Data External Data Visualization Harmonized Data Store Integration Internal Data External Data Integration Assume: ETL/ESB Integration, Complex Master Data Management (MDM), Internal Infrastructure
  • 14. Systems Externalization Global Sample Data Integration Opportunities / Challenges Cost: 2 - Cost to integrate systems - SW & Infra costs are leased - Data Virt lowers integrate cost Complexity: 3 - Need to harmonize internal data through integration platform - Need to integrate ext. data Maintenance: 2 - Integration cost reduced - SW & Infra are elastic with demand and growth Internal Data External Data Internal Data External Data Visualization Harmonized Data Store Integration Assume: Data Virtualization & Integration Platform, Complex MDM, Cloud Infrastructure
  • 15. • The Cloud Security Alliance defines cloud computing as: Cloud computing (‘cloud’) is an evolving term that describes the development of many existing technologies and approaches to computing into something different. Cloud separates application and information resources from the underlying infrastructure, and the mechanisms used to deliver them. • Other perspectives: – Cloud computing is processing and storing of data as a service outside of the traditional processing that takes place inside an organization – Enables organizations to treat data as a commodity that can be processed, transformed and delivered by outside organizations in a secure, compliant manner • Amazon Web Services approved by U.S. DoD as a Service Provider http://www.informationweek.com/government/cloud-computing/amazon-cloud-services-wins-dod-authorization/d/d-id/1141529 • Other Resources: – EU Agency for Network and Information Security Risk Assessment Tool http://www.enisa.europa.eu/activities/risk-management/files/deliverables/cloud-computing-risk-assessment – DoD Guidance on Cloud Computing Cloud Sourcing Discussion
  • 16. Data Integration Strategy Summary Operational Data Stores Integration Layer Analytics/Decision Support Selecting the Right Data Integration Strategy Laboratory Data LIMS Clinical Trial Data CTMS Biobank Data SMS Biorepository Data SMS Researcher Data SDMS Hospital/Clinic Data SDMS Consent • Consolidate Duplicative Systems, utilize COTS Platforms as basis for Core Systems • Cloud Platform used for secure processing and storage; elastic based on need • Data Virtualization enables agile, flexible data integration • External Service providers can be used to process, harmonize and visualize data
  • 17. Suresh Chandrasekaran Suresh Chandrasekaran Senior Vice President, Denodo How Data Virtualization Works and the Value it Delivers
  • 18. What is Data Virtualization? Data Virtualization combines disparate data sources into a single “virtual” data abstraction layer (aka information fabric) that provides unified access and integrated data services to consuming applications in real-time (right-time). Base View Base View Base View Derived View Connect & Virtualize Combine & Integrate Publish as a Data Service Data Service (SQL, SOAP, REST, Widgets …)
  • 19. DV Enables Agile Info Architecture – Many to Many Data Services Agile BI & Apps Faster Solution Optimized Performance Any Data, Anywhere Integrate New Sources BI /Reporting Analytics Visualization Data SharingApplication
  • 20. Data Virtualization in Life Sciences Disparate Data Any Source Any Format Data Virtualization Right Information at the Right Time Business Solutions Access Information- as-a-Service
  • 21. Bio Sample and Research Intelligence Data Virtualization Functions • Access heterogeneous data sources • Create logical abstraction layer • Deliver canonical business views • Data services – access anywhere • Agile data integration and quality • Minimize replication (cost, time) • Enterprise to cloud scalable, secure Business Need (Canonical Views) Real World (Source Data Views) Data Virtualization Layer TrialCRO Samples Clients Consent Laboratory Data LIMS Clinical Trial Data CTMS Biobank Data SMS Biorepository Data SMS Researcher Data SDMS Hospital/Clinic Data SDMS Consent
  • 22. Major Companies Find Compelling Benefits NNSA's Product Realization Integrated Digital Enterprise (PRIDE) program is built on a data virtualization fabric “Accessing and aggregating data from different NNSA sites would not be possible without Data Virtualization. It expedites projects by enabling fast and secure movement of data through different facilities.“ - Stephanie Elsea, Pantex Plant, NNSA
  • 23. Top CIOs in Life Sciences Use Agile Data & Cloud Intelligence Amgen, a $17.3 billion pharmaceutical company uses statistical analysis of data points collected in real time. The analytics system includes virtualized data warehousing tools from Denodo. Amgen can draw conclusions much faster than in the past, says CIO Diana McKenzie. Now You Can Too!
  • 25. Take 2 minutes and discuss as a table Write down common issues on note cards WORKSHOP QUESTION #1 What is your current state of sample data integration?
  • 26. Take 2 minutes and discuss as a table Write down common issues on note cards WORKSHOP QUESTION #2 What does an intelligent biobank look like to you?
  • 27. Take 2 minutes and discuss as a table Write down common issues on note cards WORKSHOP QUESTION #3 What obstacles are preventing your success?
  • 28. Speaker Panel - Solutions Discussion