This document discusses how pharmaceutical companies can learn from other industries' use of IoT and streaming data. It outlines how edge computing works and considerations for clinical trials. Examples of IoT use cases in clinical trials are provided. The document also discusses ensuring a big data platform is ready to handle IoT and streaming data from various sources and devices. It emphasizes distributed architectures and cloud solutions.

1. Copyright © 2016, Saama Technologies | Confidential
Is your big-data solution ready for streaming
data
Amit Gulwadi
Karim Damji
November 2018

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Agenda
What Pharma can learn from other industries
Broader use-cases for IoT in Life Sciences
Is your big-data platform ready for IoT
Architectures and cloud solutions to consider

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About Saama
AI and machine augmented analytics platform delivering
“Business Outcomes” for life sciences

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Look How Other Industries are Utilizing IoT
Tesla: Self driving
cars
IoT
has been happening in
MANY industries –
Industrial IoT at scale
today
Pharma: not used to
this volume and
scale of data
• Cisco
• Solved for scale: volume, speed and size of data

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So, what makes IoT different?
What Makes it Efficient is this Distributed Architecture?
IoT
Many devices: Garmin, Apple
Watch, Fitbit
Integrating with other data
sources such as EMR, lab,
biopsies etc. and devices
How do you make sense of this coming
together all at once
True signal versus
false positive
Inter-longitudinal – so
many data sources,
devices, carriers
Volume and speed of
generation of data

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How the Edge Network Works and points to consider
What is Edge
Analytics?
What gets computed at the edge vs centrally or hybrid1
Cost of transferring data to the central analytical hub?2
Curated data transmission at each mode:
• Local
• Central
• Hybrid
• Data Transmission: frequency, latency
3
What are the implications for patient safety?4
What are some
pragmatic
aspects to think
about in a
clinical trial?
How do you fit in the clinical trial site in this ecosytem?5
Online versus offline decision making needs to happen6

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Edge Analytics

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Where should Edge Analytics be Performed?
Sites
Sponsors
CROs
Patients
Devices

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Examples of use cases in clinical trials

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Platform Readiness

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Can we look at this
with a new lens?

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Domain
Clinical Trials
Data
Partner Ecosystem
Technology
Big Data
Compute Engine
GPUs
Math
AI & Deep Learning
Life Science Analytics Cloud
LSAC

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IWRSTMF
Wearables Lab Data
Third Party
Systems
EDCCTMS
IntrakLinks
1
Sources Systems
Principal Investigator
Study Coordinator
Site Manager
Local Trial Manager
Functional Manager
Clinical Trial Manager
Central Monitoring/Data Manager
PI
SC
SM
LTM
FM
CTM
CM
User Roles
Data Ingestion Layer
Data Standardization Layer
Data Transformation Layer
Data Aggregation Layer
Data Consumption Layer
Data Management Layer
DataGovernance
SearchNotificationDataProfiling
Landing Zone
2
BRE
5
DQ
Repo
6
Data
Dictionary
7
Data
Vocabulary
8
BR Repo
Rules UI
9
Data Workbench
14
Operational Analytics
Site Monitoring
Study and site KRIs
Predictive Analytics
Risk Management
Clinical Analytics
Patient Insights
Medical Monitoring
18
UserRoles
PI,SC,SM,LTM,FM,CTM,CM
Capacity Planning
17
Virtual Assistant
20
Natural Language
Processing
19
16
Predictive
Analytics
Risk Alert and Actions
15
Change
Data
Capture
3
Data
Standardization
4
Data Lake
10
11
Operational Pipe + CDM
Patient Pipe + CDM
Aggregation Layer
12
Operational
Data
Patient
Data
DataSecurity(RoleBasedAccess(RBAC),
Authentication,Authorization,Encryption,Masking)
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13
Solution Architecture
Financial Data
Claims
Data
Citeline
Data
CT.GOV
Data

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Unstruc
tured
Data
Unstructured Data
Structured Data
Syndicated Data
Transactional
System
Social Media Data
Streaming Data
API
Source
Data
Unstructured Data
Operating Systems
Windows Linux
Cloud Hadoop Distributions
Unstructured
Data
Landing Zone
Unstructured
Data
Connectors
API
JDBC
SDK
Unstructured
Data
Change Data Capture
Unstructured
Data
Data Profiler, Rule Engine
Unstructured
Data
Rules Repo,
Workbench Repo
Postgresql
Unstructured
Data
Data Model
Unstructured
Data
Data Transformation
Unstructured
Data
Web Technologies
Impala
Unstructured
Data
Search
Unstructured
Data
Others
RESTful
MicroServices
Unstructured Data
Security
Authentication
LDAP
Kerberos
SAML
Authorization
RBAC
ACL(Service Level, Directory
Level, Column Level)
Encryption
SSL
Transparent Encryption
AES 256
Unstructured Data
Data Governance
Technology Stack
Containers
Unstructured
Data
DevOps
Elastic
Beanstalk
Cloud Trail

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Unstruc
tured
Data
Unstructured Data
Structured Data
Syndicated Data
Transactional
System
Social Media Data
Streaming Data
API
Source
Data
Unstructured Data
Operating Systems
Windows Linux
Cloud Hadoop Distributions
Unstructured
Data
Landing Zone
Unstructured
Data
Connectors
API
JDBC
SDK
Unstructured
Data
Change Data Capture
Unstructured
Data
Data Profiler, Rule Engine
Unstructured
Data
Rules Repo,
Workbench Repo
Postgresql
Unstructured
Data
Data Model
Unstructured
Data
Data Transformation
Unstructured
Data
Web Technologies
Impala
Unstructured
Data
Search
Unstructured
Data
Others
RESTful
MicroServices
Unstructured Data
Security
Authentication
LDAP
Kerberos
SAML
Authorization
RBAC
ACL(Service Level, Directory
Level, Column Level)
Encryption
SSL
Transparent Encryption
AES 256
Unstructured Data
Data Governance
Technology Stack
Containers
Unstructured
Data
DevOps
Elastic
Beanstalk
Cloud Trail
Life Science Analytics Cloud
LSAC

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How we Work with Data: for Operations Planning & Conduct
Data Ingestion
(SPARK)
Load CMTS, EDC and
and Lab data into LSAC
LSAC
Landing Layer(S3) data as is from source
source
Data Management
Management (Rules
(Rules Engine)
Centralized Repository
Repository of all
Business Rules
Persist Layer
(Redshift/Spectrum)
Raw Data
Standardization –
– Golden Rule; Data
Data Vocabulary
Match & Merge
Data vocab for country,
country, TA, etc.
CDM Model
(Redshift)
Common Data Model
Model for Operational
Operational data store
Metrics
Management
Calculate
KPIs/KRIs/Metric
Values
Analytical Layer
(Redshift)
Aggregate data
Match & Merge; Data
Vocabulary
Patient Data
• Studies
• Site
• Subject
• Study metadata
• Clinical/Audit Records
Parsed XML data
Store
Patient Data Patient data (Clinical Audit
Records) in domain
containers
BRE Engine
DQ
Rules
Golden
Rules
DQ
Rules
Operations Data
Standardized
Data
Raw
Data
Raw
Data
Operational
Metrics
Repository
I(HIVE)
Data
Acquisition
Data
Acquisition
Landing Layer Persist Layer
Common Data
Model
Aggregate/Analytical Layer
Landing Layer Persist Layer Patient Data
Container
Patient Data Model
Oracle Siebel
CTMS
EMR
Flat Files
EMR
External Lab
Data
Additional Source Data
Finance
Drug Logistics
Imaging
Drug Inventory
IvRS
TMF
Integrated
Ops + Patient
Trial
Mgmt.
Site Monitoring
FPNA
Data Review
Medical
Monitoring
Clinical Science
Safety Science
Translational
Science
Site Selection
Patient
Identification

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Workflows are Centralized, Simplified and Self Service

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UX CX
Impersonal Dashboard to Personalized Analytics

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