Karim Damji, SVP Product Management and Marketing at Saama discusses how to spend less time wrangling your data. Learn about the latest technological advances have enabled a platform-based approach to help solve complex problems in a data source-agnostic manner. Improve data processing, standardization and creation of analytics-ready datasets. Introduce machine learning capabilities to improve predictions of risks. Enhance the user experience through the use of a conversational experience. Create executive summaries embedded with relevant persona-based insights.

1. Copyright © 2019, Saama Technologies
Tear Down Clinical Data Silos
& Drive Novel Experiences
Karim Damji

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Copyright © 2019, Saama Technologies
Saama’s Vision: Biopharma’s Moore’s Law
Drive a logarithmic decrease in drug development over the next decade through technology, AI and
collaborative open innovation
2019
Optimizing Trial Management with big data
integration and AI in the cloud
Improved Trial Planning with protocol optimization
and feasibility prediction
Patient-level Insights with
sensor data and genomics for
personalized therapies
Curated Clinical Insights,
predicting clinical outcomes using
AI modeling
$3B
Open Innovation Patient-centric Insights Hub for
Sponsors, CROs and HCPs
Open Ecosystem data collaboration platform for
sponsors and CROs
Open APIs to harness the data from
all Data Originators
Clinical data pipelines
Direct to Patient Recruitment leveraging
Blockchain for clinical trials
Virtual Trial Management with site-less programs
for regulatory submissions
Clinical Development Costs LSAC Value Realization
$300M
2030
…costs can be lessened with AI & tech
f(x) = e-(dv/dt . min(clinical trials))
Sources: “CSDD News.” Tufts Center for the Study of Drug Development; Herper, Matthew.
“The Cost Of Developing Drugs Is Insane. That Paper That Says Otherwise Is Insanely
Bad.” Forbes, Forbes Magazine, 16 Oct. 2017; Anisingaraju, Sagar. “In Search of Pharma's
Moore's Law.” What's TRUST Got to Do with It? | Eyeforpharma, 5 July 2018

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Copyright © 2019, Saama Technologies
Trends and Other Inspirations
Millennial Workforce (1/3rd)
Data Deluge (only 20% accessible)
Consumption Models
& New Experiences (UX)

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We're Intuitive Learners
Our Ability to Multitask is Enhanced
How Digital Natives Describe Themselves
We are Social
We Lack Patience
We Feel Empowered By the Internet

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Copyright © 2019, Saama Technologies
AI capabilities in harmonizing data are extremely valuable in bridging existing gaps in clinical
trials
Sources: Rani Hublou, “The Biggest Challenges to Delivering Clinical Trials On-Time & Within Budget”, pharmpro.com; Chen Admati, Yonatan Dolan, Michel J,
McManus, PhD, “AI and Wearables Bring New Data and Analytics to Clinical Trials”, intel.com; informatica.com
38%
Protocol
Development
37%
Study Startup
Analysis
37%
Patient
Recruitment &
Retention
32%Vendor
Selection &
Management
29%
Patient
Management &
Status Reporting
29%
Study Monitoring
23%
Data
Management
14%
Statistical
Analysis
AI-Driven Efficiency Savings in Clinical Trials
Big Data & AI Can Solve Significant Clinical Operations Challenges

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Problem Areas Within an Organization
Study Manager
What is the query resolution rate
for ABC –XXX-YZ?
Chief Medical Officer
how many of studies are at risk? What is our monthly financial burn rate
for clinical trials
Operations Lead
How will start-up delays for 3
studies impact study timelines?
User Personas need specific Insights
“Challenge is Speed to Insights”
Complex Ecosystem
CRO 1
CRO 2
Excel
Clinical
Drug
Supply
Central
Labs
Multiple Siloed System
CTMS
EDC
IVR
Finance
TMF

9. 99Copyright © 2019, Saama Technologies
Other Questions People Try to Answer
What are the quality
areas that are my
greatest risks?
How can I get real-time
access to insights?
How can I provide more
efficient oversight of the
CRO’s work?
How do I consume the
information
Can I trust information

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Products Services
Ownership Access

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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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Improve Data Processing, Standardization, and Creation of
Analytics Ready Data Sets
Strategy of managing
data-pipelines
Right Technology Stack
E.g. Visualizing data pipeline work flows
Manage
Data Quality

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Clinical Development Optimizer Data Pipeline
Data Ingestion Load CMTS, EDC
and Lab data into
Hadoop ecosystem.
Landing Layer data as is from
source
Data
Management
(Rules Engine)
Centralized
Repository of all
Business Rules
Persist Layer Raw Data
Standardization –
Golden Rule;
Data Vocabulary
Match & Merge.
Data vocab for
country, TA etc.
CDM Model Common Data
Model for
Operational data
store
Metrics
Management
Calculate
KPIs/KRIs/Metric
Values
Analytical Layer Aggregate data
Match & Merge;
Data Vocabulary
Patient
Data
Portfolio Analysis
Metrics Dashboards
Bio-Statistical
Analysis
• Studies
• Site
• Subject
• Study metadata
• Clinical/Audit Records
Parsed XML
data Store
Patient Data Patient data (Clinical Audit
Records) in domain
containers
BRE
Engine
Operations 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
EDC
Flat Files
EDC
External
Lab Data
Source Data
Raw
Data
Standardized
Data
Golden
Rules
DQ
Rules
DQ
Rules
Raw
Data

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Workflows had to be Centralized, Simplified and Self Service

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User Experience at Portfolio, Study, Countries, Region, etc.

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UX CX
Impersonal Dashboard to Personalized Analytics

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“Smart Assistant” for Clinical Operations

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Benefits
Improved engagement01
Personalized user experience02
Integration opportunity03
100 % availability (No downtime)04
Understand user preference05
Time saving06
Portability07

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Source : https://www.creditkarma.com/
John Doe
All phase II Studies are on track. Next
DBL for Study 001 Dec 1st 2018.
Study on trackBudget utilization
Avg. study periodStudy at risk
Upcoming DBLTotal Ongoing Studies
Your Study Portfolio looks
good
What the Future Will Look Like

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Conversational Analytics from Drug Development

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Build Experiences to Engage Patients

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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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The future is already here. It’s just
not evenly distributed yet.
- William Gibson
* San Francisco Examiner, April 19, 1992

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Thank You