Data and Analytics is foundational to the success of GE Healthcare’s digital transformation and market competitiveness. This use case focuses on a heavy platform transformation that GE Healthcare drove in the last year to move from an On prem legacy data platforming strategy to a cloud native and completely services oriented strategy. This was a huge effort for an 18Bn company and executed in the middle of the pandemic. It enables GE Healthcare to leap frog in the enterprise data analytics strategy.

1. Transforming GE Healthcare with
Data Platform Strategy
GE Healthcare
Where every voice makes a difference, and every difference builds a healthier world
Abey Varughese
Director – Architecture, Data & Analytics
Asha Poulose Johnson
VP – Data & Analytics

2. Agenda
Asha Poulose Johnson
Business & Technology Context
Abey Varughese
Data Platform Architecture

3. Business & Technology Context

4. GE Healthcare : Vision and Key Facts
Business Overview
$17B 160+ $1B+
2019 Revenue Countries
R&D annual
investment
Using our
imaging
agents
imaging, mobile
diagnostic &
monitoring
Generated
/ year
3 patients
Every Sec
4M units
world
wide
2B+
scans
OUR
PUR POSE
A healthier world with more precise and
efficient care
OUR
VISION
Be the leading global medical technology and
digital solutions innovator enabling Precision
Health for better clinical and operational
outcomes
ST R AT E GIC
THOUGHTS
Focus for growth
Driving outcomes with digitally-led solutions
Innovate for technology leadership

5. Data and Analytics Strategy
Guiding PrinciplesKey Trends
• Regionalization : Data regulations are tightening
across globe. (GDPR, China security law, CCPA);
Regional data segregation (China data lake)
• External data: Contextual awareness to support
decisions including use of 3rd party/public data
• Responsiveness different for Go to Markets
domains vs fulfillment domains; (Near Real time,
Real time, Batch)
• Impact of COVID : Changed expectation on TAT of
outcomes – Weeks to days/hours
• Data culture : Enabling self service; citizen data
analysts & citizen data scientists
• Data quality: Increasingly becoming a hindrance to
drive incremental outcomes
• Regionalization : Data regulations are tightening
across globe. (GDPR, China security law, CCPA);
Regional data segregation
• External Data: Contextual awareness to support
decisions including use of 3rd party/public data
• Responsiveness different for Go to Markets
domains vs fulfillment domains; (Near Real time,
Real time, Batch)
• Impact of COVID : Changed expectation on TAT of
outcomes – Weeks to days/hours
• Data culture : Enabling self service; citizen data
analysts & citizen data scientists
• Data quality: Increasingly becoming a hindrance to
drive incremental outcomes
• Regionalization : Data regulations are tightening
across globe. (GDPR, China security law, CCPA);
Regional data segregation (China data lake)
• External data: Contextual awareness to support
decisions including use of 3rd party/public data
• Responsiveness different for Go to Markets
domains vs fulfillment domains; (Near Real time,
Real time, Batch)
• Impact of COVID : Changed expectation on TAT of
outcomes – Weeks to days/hours
• Data culture : Enabling self service; citizen data
analysts & citizen data scientists
• Data quality: Increasingly becoming a hindrance to
drive incremental outcomes
•Integrated Data Ecosystem & Platform Strategy: High
quality mash up of equipment, enterprise & external data.
easily accessible, Purpose built, Secure, Scalable, Flexible
•Invest in Data management and Quality: Data cataloging ,
lineage , visual discovery , metadata management &
master data
•Data, insights, Analytics as a Service: Self service &
Subscription based consumption framework to enable
reuse and scale. More Usage = better outcomes
•Data Driven Culture: Data Literacy, Mission based teams,
Horizontal working, outcome focused, Develop technical
talent

6. GE Healthcare One Data Platform
The Data & Analytics Operating system

7. The Operating System for Analytics
Enterprise Data IOT Data
API | Bulk | Event | File |Others
Data Mgmt.
Security &
Regionalizat
ion
Data as a Service (DaaS)*
Data Store
Ingestion/Processing/Enrichment
System Consumers
- Micro Services,
Data Science ,
Analytics , Digital
Assistants, third party
tools
Mirror | Entity | Trusted
IDM
Integration |
Access
Control |
Cloud
Data Catalog
| Data
Quality |
Metadata |
Data
Discovery
Data Engr/
Scientists -
Discover data ,
visualize
relationship
Data
Owners/-
Monitor
definitions/rule
s & quality
Architects, Engineers &
Developers – Build &
support ecosystem,
Ensure security &
compliance , Maintain
vitality & performance
Analytical
Products
Info Consumers -
Customers ,
Regulators, Leaders,
Ops, Others
Data Products/Analytics/Data Science Solutions/Analytics Apps
DATAPLATFORM
Analytics & Data Science as a
Service (ADS)*
Automated Analytics Provisioning
Public/External Data
2
4
1
3
5 7
Data creation & Master data
management – Source systems
Common Ingestion, Mirroring
& Processing
Secure - Data stitching &
store
Integrated mgmt. with Catalog,
Metadata and Lineage
Data consumption service
offerings across persona
sets
Analytics Service improving
accuracy with usage
1
2
3
4
5
6
Information flow
8
Business outcomes with
Analytics Apps that leverage
foundation data services
8
6
Regionalization, Security7
As Service Offering Data StoreData ManagementFoundational Offerings

8. GE Healthcare One Data Platform
Platform Architecture

9. Real-Time Big Data Analytics Architecture on Cloud
Data Platform ArchitectureEnterprise
DataSources
IoT/Machine
DataSources
Data Streaming
Data Replication
CDC/Incremental
Real-time
Processing Engine
Data Lake
Cloud Data
Warehouse
Data Analytics
Real-time
Dashboard
Dashboard w/
Historical Data
Notifications &
Actions
Machine
Learning
Model Verification

10. DaaS – Democratizing Data
• Single source of truth
• Supports self-service
• Accelerate outcomes with democratization
• Domain based APIs
• Data Services @scale & speed by Products/Regions
• Integration of Enterprise/ IoT /Machine data
• Wing to Wing automation of services
• Certified data sets
• Elastic & scalable API integration
• Access integrated with Data Catalog
• Edge acceleration
AIaaS – Democratizing AI
• Stable, Reliable and Secure platform for Analytics
• AIOps- Logs, Events, Metrics; Pattern Discovery and Recognition
• Transform IT operations using machine learning
• Data Notebook & Framework
• Enable Cognitive computing solutions transforming business'
approach to decision-making and problem-solving
• Evolution of Analytics from Descriptive, Diagnostic &
Prescriptive Analytics to more Predictive & Cognitive Analytics
Artificial Intelligence as a Service (AIaaS)Data as a Service (DaaS)
Enterprise
Machine
FeaturesBenefits

11. ODP Cloud Infrastructure & Automation
▪ Bastion/Two-Factor Authentication
▪ Authentication & authorization across the instances is controlled
using two factor authentication (MFA)
▪ Security Tools
▪ Integrate security agents as part of the Infrastructure as a code
▪ Data Lifecycle Manager
▪ Using DLM, Automated snapshots are created for Disaster recovery
and Backup Compliance.
▪ User Home Directory on S3
▪ The S3 User home directory solution is implemented across cloud
accounts for Users.
Infrastructure as a Code & SecDevOps

12. ODP Cloud Infrastructure & Automation
▪ AutoTAG
▪ AutoTAG is a tool built using event-driven architecture for
better cost visibility and improved security governance.
▪ Auto-Remediate S3 Bucket Public Access
▪ Monitor S3 buckets for any ACL/Permission change to public
In case of ACL change, It is auto-remediated and ACL is changed
back to private along with notification.
▪ Auto Instance Scheduler
• Shutdown/start instances during defined business hours
for Cost Optimization.
AIOps: Analytics Driven Automation

13. A scalable & nimble data platform with
right data that is democratized and
consumed by an increasingly data
literate organization is a true multiplier
and will enable exponential growth.

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