Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptx

© 2022 Neo4j, Inc. All rights reserved.
1
The Art Of The
Possible With Graph
Technology
Emil Eifrém
Founder & CEO, Neo4j

2
Image showing case study goes here
Placeholder
PFIZER
Readiness for
billions
of vaccinations was
an unprecedented
supply chain and
logistics challenge
for Pfizer.

3
LUND
City of 92,000. In
the province of
Skåne, Sweden.
The birthplace of
Neo4j.

8
Image showing case study goes here
Placeholder
BACK TO PFIZER
Rolled out billions
of vaccinations
worldwide at record
speed thanks to
optimized
manufacturing,
supply chain and
distribution.

9
At the heart of every enterprise challenge
is an explosion of data complexity

10 © 2023 Neo4j, Inc. All rights reserved.
DATA GROWTH IS ACCELERATING.
EVERYONE IS CONNECTED TO EVERYTHING
200+
Zettabytes
In cloud data storage
by 2025
65
Average number of
enterprise SaaS apps
41B
Connected IoT
devices by 2025

11
Organizations are drowning in data
but lacking in insights

12
80% of business leaders
say data is critical in
decision-making, yet
41% cite a lack of
understanding of data
because it is too
complex or not
accessible enough
- Salesforce Global Survey, 2023

Data silos
hide valuable
context
THE CHALLENGE

14
TODAY’S REALITY
Connections
in data are as
valuable as the
data itself

CONNECTION COMPLEXITY
How do you navigate this data
explosion at scale?

16
Legacy
systems
can’t keep up
“Relational” Databases
don’t handle relationships well
NoSQL Databases
don’t handle relationships at all

17
A fundamentally new approach is needed

Remember this?
Graph creates a
more intuitive and
connected view of
data relationships,
unlocking deeper
insights and context

19
THE PROPERTY GRAPH: SIMPLY POWERFUL
Employee City
Company
Nodes represent
objects (nouns)
Relationships are directional
Relationships connect nodes are
represent actions (verbs)
Relationships can have
properties (name/value pairs)
Nodes can have
properties (name/value
pairs)
name: Amy Peters
date_of_birth: 1984-03-01
employee_ID: 1
:HAS_CEO
start_date: 2008-01-20
:LOCATED_IN

INDEX-FREE ADJACENCY: FAST, FLEXIBLE, SCALABLE
Relational Database
Person Person-Friend Friend
Native Graph Database
At write time
Data is .connected.
as it is stored
At read time
.Lightning-fast. retrieval of data and
relationships via pointer chasing

QUERY PERFORMANCE AS # OF JOINS INCREASE
Connectedness and Size of Data Set
Response
Time
Relational and
other NoSQL
databases
Native Graph Database
1000x Advantage
Minutes to milliseconds
5+ hops
3+ degrees
Thousands of connections
0 to 2 hops
0 to 3 degrees
Few connections

22
Graph enables organizations to quickly and easily
uncover hidden relationships and patterns
across billions of data connections
Powering applications that are
impossible with other technologies

© 2023 Neo4j, Inc. All rights reserved. 23
Fraud Detection
Real-Time
Recommendations
Network & IT
Management
Supply Chain
Management
Digital Twin
Identity & Access
Management
x360
23
Cyber Security
Expanding Realm of Graph Use Cases

20 / 20
Top US banks
3 / 5
Top Aircraft Manufacturers
7 / 10
Top Telcos
3 / 5
Top Hotel Groups
8 / 10
Top Insurance Companies
10 /10
Top Automakers
7 / 10
Top Retailers
5 / 5
Top Pharmaceuticals
Trusted by
75 of the

NEO4J
The first-ever
graph database
Creator of the
market category
Continued market
leader
The Forrester WaveTM
For Graph Data Platforms

HUGE INTEREST IN GRAPH FUELED BY AI & ML
“50% of Gartner
inquiries on the topic
of AI involve discussion
of the use of graph
technology.”
35x increase in AI research papers
featuring Graph over the past decade
Source: Dimensions Knowledge System
365
13,040

GRAPH DATA SCIENCE:
MAKE SENSE OF DATA RELATIONSHIPS
Machine Learning Pipeline
Explore the hidden patterns and features in your data
What’s important? What’s unusual? What’s next?

BETTER PREDICTIONS
WITH THE DATA YOU ALREADY HAVE
Machine Learning Pipeline
• Traditional ML ignores network structure
• Graphs use relationships to unlock otherwise unattainable predictions
• Add graph-computed data to ML pipelines to increase predictive accuracy

29
GRAPH DATA SCIENCE MATURITY MODEL
Find the patterns you’re
looking for in connected data
Identify associations,
anomalies, and trends using
unsupervised machine learning
Learn features in your graph
that you don’t even know
are important yet

30
“By 2025, context-driven [i.e. graph]
analytics and AI models will replace
60% of existing models built on
traditional data.”
Top 10 Tech Trends in Data and Analytics 2022, 11 Mar 2022, Afraz Jaffri, et al.

NEO4J: GRAPH DATABASE & ANALYTICS

32
LOVED BY CUSTOMERS
Easy implementation
“Best in a class, easy to
implement, and greatly
beneficial for our
business.”
- Director, $3B-10B company
“Most human-friendly
Cypher dev language
with one of the best
integration capabilities”
Developer friendly Speed to insight
“What used to take
weeks or months, we now
get answers to in minutes
or even seconds.” –
Jonathan L.
Community

33
Exponential Growth in Connected Data
Connected data use-
cases are exploding in
numbers and in depth
Data models the real
world. Therefore data is
becoming increasingly
connected.
The world Is
becoming
increasingly
connected

34
By 2025, graph will be
used in 80% of data and
analytics innovations

35

Engineering Expertise
>800 person-years of dev investment
First mover advantage
Maturity, Most enterprise deployments
Largest community
Largest & Fastest Growing
Transactional
+
Analytics
Workloads
Native Graph
Architecture
+
ACID
+
Schema
Flexible
In-Memory
Graph
Processing
for
Algorithms
and ML
Rich and
Powerful
Tools and
Integrations
Robust
Enterprise-
Grade
Security
Runs
Anywhere
NEO4J: THE BEST OF BREED, FULL GRAPH STACK

Neo4j GraphSummit London March 2023 Emil Eifrem Keynote.pptx

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