Graph technology has truly burst onto the scene with diverse new products and services, proving that graph is relevant and that not all graph use cases are equal. Previously relegated to niche implementations and science projects, graph now finds itself deployed as the foundational technology for enterprise analytics solutions and enterprise Data Fabric strategies. It is no surprise that many are calling 2018 “The Year of the Graph”.

1. The Year of the Graph
Ben Szekely, VP Solution Engineering

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Data Lake
Architecture
Semantic
Layer
Graph
Database
Technology

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Data Lake
Architecture
Semantic
Layer
Graph
Database
Technology
Graph
Database
Technology

4. Unstructured
Content
Diverse Data
Sources
Enterprise
Data Lakes
Internal
Databases &
Applications
Industry
Standards &
Open Data
Third-Party
Data
Open Standards Scalability Security Governance
The Enterprise Knowledge Graph

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2018 is the year of the Graph

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Here are three reasons why

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1. Graph Database offerings are showing maturity
in capability and diversity

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On-line Transaction Processing
(OLTP)
On-line Analytical Processing
(OLAP)
Large number of short
transactional queries
(ex: SELECT, UPDATE, DELETE)
Relatively fewer but complex
analytical queries on large data
(ex: AVERAGE, SUM, COUNT)
Graph Database Market Segmentation

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Relational
Databases
Graph
Databases
On-line Transaction Processing
(OLTP)
On-line Analytical Processing
(OLAP)
Large number of short
transactional queries
(ex: SELECT, UPDATE, DELETE)
Relatively fewer but complex
analytical queries on large data
(ex: AVERAGE, SUM, COUNT)
Graph Database Market Segmentation

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Relational
Databases
Graph
Databases
AnzoGraph™
Graph
Databases
Relational
Databases
On-line Transaction Processing
(OLTP)
On-line Analytical Processing
(OLAP)
Large number of short
transactional queries
(ex: SELECT, UPDATE, DELETE)
Relatively fewer but complex
analytical queries on large data
(ex: AVERAGE, SUM, COUNT)
Graph Database Market Segmentation

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2. Graph is being used beyond classical
graph problems

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Classical Graph Application: How is Daniel Related to Grant

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Classical Graph Application: How is Daniel Related to Grant

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Today’s Graph Application
Harmonize and Connect Data Across N>1 Data Sources
Firmographics Data
Citeline Data
Salesforce Data
Multi-value cells
need to be parsed
No schemas exist to
interlink data

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Today’s Graph Application
Harmonize and Connect Data Across N>1 Data Sources
Firmographics Data
Citeline Data
Salesforce Data
Multi-value cells
need to be parsed
No schemas exist to
interlink data

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Today’s Graph Application –
Join and Aggregate Across N>1 Data Sets

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3. Digital Transformation of complex data
requires graph

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Driving Digital Transformation of the Science-Driven Business
Graph provides a
data model flexible
and scalable enough
to digitize even the
most complex
business processes

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Driving Digital Transformation of the Science-Driven Business

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Complex Scientific
Discovery Model

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Beyond Graph
The Semantic Layer makes graph accessible

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The Semantic Layer
A human-readable representation of data that uniquely identifies and connects data with common business terms.
A layer that helps end users access data autonomously, securely and confidently.

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An Information Fabric in the Life Sciences Industry
Drug Discovery Preclinical Product Development FDA Review
Scale Up
Production
Post Marketing
Surveillance
PHASE 1 PHASE 2 PHASE 3
~5,000-10,000
Compounds
250
Compounds
5
Compounds
3-6 Years 6-7 Years 0.5 - 2 Years Indefinite
INDSUBMITTED
NDA/BLASUBMITTED
PREDISCOVERY
Number of Volunteers
20-100 100-500 1,000-5,000
$538m $119m $219m $255m $251m $39m
FDA Approved
Clinical Trial
Exploratory Analytics

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An Information Fabric in the Life Sciences Industry
Drug Discovery Preclinical Product Development FDA Review
Scale Up
Production
Post Marketing
Surveillance
PHASE 1 PHASE 2 PHASE 3
~5,000-10,000
Compounds
250
Compounds
5
Compounds
3-6 Years 6-7 Years 0.5 - 2 Years Indefinite
INDSUBMITTED
NDA/BLASUBMITTED
PREDISCOVERY
Number of Volunteers
20-100 100-500 1,000-5,000
$538m $119m $219m $255m $251m $39m
FDA Approved
Clinical Data
Standards Mgmt
Clinical Trial
Exploratory Analytics
Real World
Research
PV & Safety Case
Management

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An Information Fabric in the Life Sciences Industry
Drug Discovery Preclinical Product Development FDA Review
Scale Up
Production
Post Marketing
Surveillance
PHASE 1 PHASE 2 PHASE 3
~5,000-10,000
Compounds
250
Compounds
5
Compounds
3-6 Years 6-7 Years 0.5 - 2 Years Indefinite
INDSUBMITTED
NDA/BLASUBMITTED
PREDISCOVERY
Number of Volunteers
20-100 100-500 1,000-5,000
R & D
Intelligence (CI)
$538m $119m $219m $255m $251m $39m
FDA Approved
Product Development
& Regulatory
Clinical Trial
Operations
Medical Advisory
Board Analytics
Clinical Data
Standards Mgmt
Clinical Trial
Exploratory Analytics
Real World
Research
Voice of the Customer
Analytics
Source of Influence
Commercial Analytics
PV & Safety Case
Management

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“As the influx of use cases for advanced
analytics and data exploration continues to
rise, we have experienced greater than
expected growth in the intrinsic value of the
information fabric, providing insights
previously requiring human heroics to
achieve.”
The Information Fabric
ASDL Fortune 100 Customer

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What Lies Ahead?
The foundation for leveraging new technologies on existing data
NLP ML AI

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Our Technology
Data Lake
Architecture
Semantic
Layer
Graph
Database
Technology

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Many Organizations Have Established Data Lakes
30
Built to reduce storage costs, and enable
self service access to data

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Data Ingestion
Structured Data
Ingest and ETL
Storage
Infrastructure
Data Cataloging
Basic Metadata
Cataloging
Self Service
Access to Raw
Data Sets
Standard Data Lake Offerings
Focused on ingest and cataloging
Many Organizations Have Established Data Lakes
31

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Data Ingestion
Structured Data
Ingest and ETL
Storage
Infrastructure
Data Cataloging
Basic Metadata
Cataloging
Self Service
Access to Raw
Data Sets
Standard Data Lake Offerings
Focused on ingest and cataloging
Many Organizations Have Established Data Lakes
32
But does not address:
- Unstructured Data/NLP
- Data Prep
- Custom Coding
- Data Integration
- Multiple Vendor Integration

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Anzo Smart Data Lake 4.0 – Bringing Graph to the Data Lake
We bridge that gap

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