Big Data Spells Big Problems ...
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Transcript of Big Data Spells Big Problems ...
Gary D. Kennedy
Founder & CEO
“Big Data” Spells Big Problems
R&D Informatics Track
Molecular Med TRI-CON
February 21, 2012
“Big Data” Spells Big Problems
The data
problems we
have been
struggling with
for years are
only the tip of
the iceberg…
Life Science Progress Is Impeded By Data Problems
• Too many disparate data sources
• Lack or surfeit of data standards
• Multi-site collaboration is essential for
breakthroughs
• Increasingly complex regulatory
landscape
• Inconsistent terminology standards
Why Haven’t We Solved the Data Problem?
• Inherent conflicts in the existing ecosystem
• Step forward causes steps backward in other areas
• Moribund architectures exacerbate the problem
• Disruptive business models have not kicked in
• We have always done it that way
• This is not any easy problem to solve
Start from Common Ground
• Everyone would like to solve the
data problem
• Pattern recognition is essential
for all stakeholders
• Enlightened insight into the
future is desirable
• Competition sharpens all swords
• Status quo is untenable
Registry-Centric Architecture:Preparing For Big Data
Claims Data
Reports/Dashboards
Biospecimen Data
Clinical Trials Management
Quality Improvement
Paper Records
Disease
Associations
National Registries
‘omics Data
Cohort Selection
University Partners
small molecule
research database
Imaging
EDC From Patients
in Trials
Patient Reported
Data
EMR
Principal Investigators
Query &
Reporting
Common
Repository
Master
Ontology
Regulators
A Rising Tide Lifts All Ships…
• Everyone benefits from a single 360° perspective of
all patients, subjects, procedures, or disease
• Early pattern recognition enables competitive
advantage
• Phenotypic data is fully integrated with genotypic
data
• Single, definitive source of truth
• Quality and adverse events see the same data as
reimbursement and clinical affairs
• When all data is presented together everybody wins
“Big Data” Requires “Big Changes”
1. Flexible yet structured data model
2. All tools and applications under the
control of the Master Ontology
3. Modern, browser-based platform
that is fully configurable
4. Pattern recognition through a 360°
view of patients or subjects
5. Registries are automatically linked for cross-disease research
“mind-forged manacles”
- William Blake
Flexible yet Structured Data Model
Level of Flexibility
Str
uctu
red O
nto
logy
EHR
Build From
Scratch Spreadsheets
Low High
Hig
h
EDC
CTMS
RDMSSweetSpot
Tools Are Open and Controlled by the Ontology
Tools Utilize Harmonized Data
Bre
adth
of
the T
ool
Set
Narr
ow
Yes
Wid
e
Open Source
Tools
Build From
Scratch Public
Ontologies
EDC
Tools
EDC
Tools
No
CompetitiveAdvantage
Same Thing Only Different
Personalized To An Organization
Siz
e o
f th
e U
ser
Base
General Specific
Larg
e
EHR
Build From
Scratch
AML
Registry
Oncology
Registry
CTMS
Holy
Grail
Sm
all
Patient Specific Or Population Oriented
Population Centric
360
Vie
w o
f Each P
ati
ent
No Yes
Yes
Patient
Registry
Build From
Scratch Quality
Registry
Outcomes
Registry
Clinical
Registry
Best of BothWorlds
Stand-Alone or Linked for Cross Disease Research
All Registries Linked
Each R
egis
try S
tands
Alo
ne
No Yes
Hig
h
Diabetes
Registry
Build From
Scratch
Excel
Auto-
Immune
National
Registry
PatternRecognition
“Big Data” Spells Big Opportunity• Produces a competitive advantage for those who get
it….and act
• Implement “Little Data” right with infinite flexibility and scalability and you are there
• The first to recognize patterns wins
• Paradigm shifts present disruptive opportunities
• Predictive informatics
better directs your limited
research investment
• Going to collect this data
anyway…might as well
use it