toward collaboration: government analytic architecture survey · toward collaboration: government...
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Toward Collaboration: Government Analytic Architecture Survey
November 2016Dr. Ransom Winder, MITREMr. Joseph Jubinski, MITRE
Dr. Angela O’Hanlon, MITRE
The views, opinions and/or findings contained in this report are those of The MITRE Corporation and should not be construed as an official government position, policy, or decision, unless designated by other documentation.
© 2016, The MITRE Corporation. All rights reserved.
Approved for Public Release; Distribution Unlimited. Case Number 16-4256
Introduction
• Established practice vs emerging capability?
• Differences?
• What drives preferences?
• What are the foundational elements of a reference implementation for collaboration?
Analytics and Collaboration
• Analytics: software that discovers patterns in data
• Ideally: data, analytics, and artifacts all equally accessible
• Typically: data & analytics walled off • Proprietary intellectual property
• Sharing artifacts is more likely
Data Analytic Results
AnalyticAnalytic
ResultsArtifactsData
Data
Ideally Typically
Artifacts
Artifacts
Artifacts
Analytic
Analytic
Data
Data
Data Analytic
Government: Defense/Intel and Civilian Agencies
Analytic Architectures in Government:Defense and Intelligence
?
• Established enterprise-wide architectures
• Efforts to refresh existing architectures
• Smaller projects for limited communities
Analytic Architectures in Government:Defense and Intelligence, Implementation Issues
Cloud
MobileDevice
Cloud
MobileDevice
Ideal Real World Conditions (e.g. Battlefield)
Data
Data
DataUseCase
Use Case
UseCase
IP
Intel Consumers
Defense and Intelligence Enterprises
DoD JIE – Department of Defense Joint Information Environment
IC ITE – Intelligence Community Information Technology Enterprise
DI2E – Defense Intelligence Information EnterpriseDiagrams derived from:http://www.afei.org/PE/4A07/Documents/DI2EPlugfest2014-JimMartinBrief-v0.pdf
DCGS
COCOM JIOC
IC ITE JIE
DI2E
Distributed Common Ground System
DCGS –Distributed Common Ground System
Distributed Common Ground System
AF DCGS – Air Force Distributed Common Ground System
Distributed Common Ground System
TCRI – Tactical Cloud Reference Implementation
Distributed Common Ground System
DIB – DCGS Integration Backbone
Analytic Architectures in Government:Civilian Agencies
• Important points• Analytic architectures can fit these contexts, but…
• Architectures must conform to legal restrictions / protections
• Restrictions dictate how data may be used
Centers for Medicare & Medicaid Services (CMS)
• Some enterprise-wide capabilities for data• Integrated Data Repository
• Chronic Condition Warehouse
• Big data sets• 4.5 million claims daily [1]
• Big budget • FY2015: $984.5 billion [2]
[1] https://www.cms.gov/Research-Statistics-Data-and-Systems/Computer-Data-and-Systems/HIGLAS/index.html[2] https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/CMS-Fast-Facts/index.html
Centers for Medicare & Medicaid Services (CMS)
Requirements
• Health Insurance Portability and Accountability Act (HIPAA)
• Affordable Care Act (ACA)
• Analytic expediency and integrity
• Decisions must be understandable and justifiable
However, predictive analytics have aided in catching fraud [1]
[1] https://blog.cms.gov/2016/07/20/42-billion-saved-in-medicare-and-medicaid-primarily-through-prevention/
Department of Veterans Affairs
• VA promotes an open source community • Open Source Electronic Health
Record Alliance (OSEHRA) [2]
• Gives third parties a baseline for interacting
• Activity: Build an ontology to influence future medical record schemas [1]
• Goal: Demonstrate the value of reasoning for health care
[1] Stoutenberg et al, "Developing a Clinical Care Ontology for the Veterans Health Administration“[2] www.osehra.org
Towards an Analytic Architecture for Industry and Government
Some orgs need an enterprise-wide analytic architecture
Others need individual analytic architectures for specific use cases
…But there is no established ubiquitous solution Analysis Exchange Model
1. Hub for collaborative analytic activity
2. Allows fusion across different sources
3. Anticipates contributions from across Industry
SourceMachine
OBP
ABI
Object Based Production &Activity Based Intelligence
• Object Based Production (OBP)• Aggregate knowledge into objects
• Activity Based Intelligence (ABI)• Reveal the underlying patterns
OBP
ABI
OutputHuman
As expressed in:https://info.publicintelligence.net/DIA-ActivityBasedIntelligence.pdf (Slide 8)
Source
Source
Source
Source
AnalystObject
Source
Source
Source
Source
Analyst
Object Based Production Flow for Intelligence Discovery
Previous approach OBP approach
As expressed in:https://info.publicintelligence.net/DIA-ActivityBasedIntelligence.pdf (Slide 4)
Analysis Exchange
Model
Source
Source
Source
Source
Object
Source
Source
Source
Source
Toward an Analytic Architecture for Industry and Government
OBP approach Our approach
Analytic Artifacts
Analytic Artifacts
Analytic Artifacts
Analytic Artifacts
Key Elements of an Analytic Architecture
Orchestrating exchange activityA “lingua franca” for exchangeAdaptation and fusion services
Conclusions
A push exists toward a comprehensive analytic architecture across and beyond their enterprise
Protective legal barriers must be considered
• Analytic architectures across various government agencies• Different needs and objectives
• Different scales of data
• Common need to derive valuable knowledge or intelligence
• No settled solutions for any agency
• Right time to consider alternatives• Foster collaboration between
government and industry • Mutual benefit• Collaboration can provide new
capabilities
Conclusions
• Roundtable’s proposal: Analysis Exchange Model• Hub for collaboration• Flexible for different use cases
Thank you
Questions?