Lance Walter, CMO, Neo4j

1. Lance Walter, CMO, Neo4j
Neo4j GraphTour Toronto
June 25, 2019

2. Welcome!
#graphtour #neo4j

4. Agenda
• Graphs 101
• State of the Graph Union
• The Future of Graphs

5. Frederik Obermaier, Süddeutsche Zeitung, on the
importance of networks in journalism. From Panel at
Columbia University Feb 23, 2018.
“I’ve only come
across 3 or 4
stories in my
career that
weren’t about
networks.”

7. ACCOUNT
HAS
REGISTERED
ADDRESS
PERSON
IS_OFFICER_OF
PERSON
NAME
STREET
BANK
WITH
LIVES_AT
LIVES_AT
NAME
COMPANY
BANK
BAHAMAS
2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc…

8. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc… Person B
Bank US
Account
123
Person A
Acme Inc
Bank
Bahamas
Address X
HAS_ACCOUNT
REGISTERED
IS_OFFICER_OF
WITH
LIVES_AT
LIVES_AT
NODE
RELATIONSHIP

9. 2.6 TB
11.5 million documents
Emails, Scanned Documents,
Bank Statements etc…

13. ICIJ Pulitzer Price Winner 2017

14. Common Graph Use Cases
Fraud
Detection
Real-Time
Recommendations
Network & IT
Operations
Master Data
Management
Knowledge
Graph
Identity & Access
Management

15. Common Graph Use Cases
airbnb
Fraud
Detection
Real-Time
Recommendations
Network & IT
Operations
Master Data
Management
Knowledge
Graph
Identity & Access
Management

16. “Forrester estimates that over
25% of enterprises will be using
graph databases by 2017.”
Forrester, 2014

17. Popularity of Graphs
DB-engines Ranking of Database Categories
• Graph DBMS
• Key-value stores
• Document stores
• Wide column store
• RDF stores
• Time stores
• Native XML DBMS
• Object oriented DBMS
• Multivalue DBMS
• Relational DBMS
Graph DB
2013 2014 2015 2016 2017 2018 2019

18. >50%of enterprises were using
graph databases
In 2017
Source: Forrester Vendor Landscape:
Graph Databases, October 6, 2017

19. Trend No. 5: Graph
…
The application of graph processing and graph DBMSs will grow at 100
percent annually through 2022 to continuously accelerate data
preparation and enable more complex and adaptive data science.
…
Graph analytics will grow in the next few years due to the need to ask
complex questions across complex data, which is not always
practical or even possible at scale using SQL queries.
https://www.gartner.com/en/newsroom/press-releases/2019-02-18-gartner-identifies-top-10-data-and-analytics-technolo
February 18, 2019

20. The Hadoop Market is Unsteady

21. From Collections to Connections
“50% of database
workloads will move to
graphs in the next 19
years”
Michael Moore, Ph.D.
EY

23. Strictly ConfidentialStrictly Confidential
Strategic initiative, led by Thomas Kurian, CEO of Google
Cloud
• Goal to be #2 Enterprise Cloud as the “open source
friendly” alternative to AWS
• Work with known/proven leaders across key areas
• Neo4j/GCP integrated solution beta by EoY 2019
• Initial release of Neo4j DBaaS will be available via the
Google marketplace
23
Google Cloud Partnership
• Fully managed services running in the cloud, with best efforts made to optimize performance
and latency between the service and application.
• A single user interface to manage apps, which includes the ability to provision and manage
the service from the Google Cloud Console.
• Unified billing, so you get one invoice from Google Cloud that includes the partner’s service.
• Google Cloud support to manage and log support tickets in a single window and not have to
deal with different providers.

24. Retail
7 of top 10
Finance
20 of top 25 7 of top 10
Software
Hospitality
3 of top 5
Telco
4 of top 5
Airlines
3 of top 5
Logistics
3 of top 5
76%
FORTUNE 100
have adopted or
are piloting Neo4j

25. 2000+

26. Neo4j Startup Program Expansion
• Free access for startups with up to 50 employees;
under $3M in revenue
• Neo4j Enterprise Edition
• Neo4j Bloom
• Apply at http://neo4j.com/startup-program
• Notable alumni include:
Medium

28. Background
• Over 7M citizens suffer from Diabetes
• Connecting over 400 researchers
• Incorporates over 50 databases, 100k’s of Excel
workbooks, 30 database of biological samples
• Sought to examine disease from as many angles as
possible.
Business Problem
• Genes are connected by proteins or to metabolites,
and patients are connected with their diets, etc…
• Needed to improve the utilization of immensely
technical data
• Needed to cater to doctors and researchers with
simple navigation, communication and connections
of the graph.
Solution and Benefits
• Dr. Alexander Jarasch, Head of Bioinformatics and
Data Management
• Scientists can conduct parallel research without asking
the same questions or repeating tests
• Built views like a liver sample knowledge graph
DZD - German Center for Diabetes Research
Medical Genomic Research!28
EE Customer since 2016 Q

29. Background
• Fortune 100 heavy equipment manufacturer
• 27 Million warranty & service documents parsed
• Foundation for AI-based supply chain management
Business Problem
• Improve maintenance predictability
• Need a knowledge base for 27 million warranty
documents and maintenance orders
• Graphs gather context for AI to identify ‘prime
examples’ of connections among parts, suppliers,
customers and their mechanics anticipate when
equipment will need servicing and by whom.
Solution and Benefits
• Text to knowledge graph
• Common ontology for complaints, symptoms & parts
• Anticipates when equipment will need servicing
• Improves customer and brand satisfaction
• Maximizes lifespan and value of equipment
Caterpillar Heavy Equipment Manufacturing
Parts Assembly & Equipment Maintenance!29

30. Background
• Social network of 10M graphic artists
• Peer-to-peer evaluation of art and works-in-progress
• Job sourcing site for creatives
• Massive, millions of updates (reads & writes) to Activity
Feed
• 150 Mongos to 48 Cassandras to 3 Neo4j’s!
Business Problem
• Artists subscribe, appreciate and curate “galleries” of
works of their own and from other artists
• Activities Feed is how everyone receives updates
• 1st implementation was 150 MongoDB instances
• 2nd implementation shrunk to 48 Cassandras, but it
was still too slow and required heavy IT overhead
Solution and Benefits
• 3rd implementation shrunk to 3 Neo4j instances
• Saved over $500k in annual AWS fees
• Reduced data footprint from 50TB to 40GB
• Significantly easier to introduce new features like,
“New projects in you Network”
Adobe Behance Social Network of 10M Graphic Artists
Social Network!30
EE Customer since 2016 Q

31. Home
Security
Internet of
things
Institutional
Memory
Entertainment
Recommendations
Home
Operations
Personalization
Voice Enabled Smart Home

32. !32

33. Background
• Largest Cable TV & Internet Provider in US
• 3rd Largest network on the planet
• xFi is consumer experience in 3M houses
• Internet, router, devices, security, voice & telephony
• Transformational customer experience
Business Problem
• Integrate all experience in a smart home
• Create innovative ideas based on cross-platform and
household member preferences
• Add integrated value of xFinity triple play & quad-
play services (internet, VoIP, cable TV & home
security)
Solution and Benefits
• Custom content per household member
• Security reminders (kids are home, garage left open)
• Serves millions of households
• Makes content recommendations based on occupant,
time of day, permissions and preferences
• Has Siri-like voice commands
COMCAST Xfinity xFi TELECOMMUNICATIONS
Smart Home / Internet of Things!33
EE Customer since 2016 Q

34. AI & Graphs

35. EVIDENCE
BASED
MACHINE
LEARNING SYSTEMS
PRESCRIPTE
ANALYTICS
NATURAL LANGUAGE GENERATION
“Yankees”
“Giants”
“Penguins”
“Jets”
“Bears”
“Red Soxs”
NLP/TEXT MINING
PREDICITVE
ANALYTICS
RECOMMENDATION
ENGINES
DEEP
LEARNING

36. Strictly ConfidentialStrictly Confidential
The Market Sees Strong Synergy between Graphs and
Artificial Intelligence
36
AI research papers focused on graphs
SURGING
INTEREST
New Book:
20K Downloads in first 2 weeks, ⅓ Net-new
CONNECTED
CONTEXT FOR AI/ML
CUSTOMER
TRACTION
German Center for
Diabetes Research
Dozens of AI deals in the
pipeline

37. Graphs Provide Connections
& Context for AI

38. Knowledge Graphs

39. What Your ML Looks Like Today

40. Data Sources

41. Decisions
Machine Learning Pipeline
Data records
(“Features”)

45. “Increasingly we're learning that you can make
better predictions about people by getting
all the information from their friends and
their friends’ friends than you can from the
information you have about the person
themselves”
— Dr. James Fowler

46. Decisions
Machine Learning Pipeline
Data records

47. $
Better Decisions
Machine Learning Pipeline

48. Feature Extraction

49. Connected Feature Extraction

50. Thank You!
http://neo4j.com

51. #neo4j@lancewalter #neo4j