The document discusses knowledge graphs and provides examples of how Neo4j has been used by customers for knowledge graph and graph database applications. Specifically, it discusses how Neo4j has helped organizations like Itau Unibanco, UBS, Airbnb, Novartis, Columbia University, Telia, Scripps Networks, and Pitney Bowes with fraud detection, master data management, content management, smart home applications, investigative journalism, and other use cases by building knowledge graphs and connecting diverse data sources.

1. Knowledge Graphs
#1 Database for Connected Data
Jeff Morris
Head of Product
Marketing
jeff@neo4j.com
11/7/17

2. Agenda
• Introduction to Neo4j
• Neo4j Definition of Knowledge Graph
• Examples

3. Who We Are: The Graph Platform for Connected Data
Neo4j is an enterprise-grade native graph platform that enables you to:
• Store, reveal and query data relationships
• Traverse and analyze any levels of depth in real-time
• Add context and connect new data on the fly
• Performance
• ACID Transactions
• Agility
• Graph Algorithms
3
Designed, built and tested natively
for graphs from the start for:
• Developer Productivity
• Hardware Efficiency
• Global Scale
• Graph Adoption

4. CONSUMER
DATA
PRODUCT
DATA
PAYMENT
DATA
SOCIAL
DATA
SUPPLIER
DATA
The next wave of competitive advantage will be all about
using connections to identify and build knowledge
Knowledge Graphs in The Age of Connections

5. Discrete Data Problems Connected Data Problems
Perspective
SELECT foo
FROM emp
SQL
(Ann)-[:LOVES]->(Dan)
CypherQuery
Language
RDBMS GRAPH DB
DBMS
Architectur
e

6. Neoj4’s Amazing Customers
NASA explores graph database
for deep insights into space
International Consortium of Investigative Journalists
Wins Pulitzer Prize

7. Business Problem
• Find relationships between people, accounts,
shell companies and offshore accounts
• Journalists are non-technical
• Biggest “Snowden-Style” document leak ever;
11.5 million documents, 2.6TB of data
Solution and Benefits
• Pulitzer Prize winning investigation resulted in
robust coverage of fraud and corruption
• PM of Iceland & Pakistan resigned, exposed
Putin, Prime Ministers, gangsters, celebrities
(Messi)
• Led to assassination of journalist in Malta
Background
• International Consortium of Investigative
Journalists (ICIJ), small team of data journalists
• International investigative team specializing in
cross-border crime, corruption and accountability
of power
• Works regularly with leaks and large datasets
ICIJ Panama Papers INVESTIGATIVE JOURNALISM
Fraud Detection / Knowledge Graph7

8. Business Problem
• Find relationships between people, accounts,
shell companies and offshore accounts
• Journalists are non-technical
• 2017 Leak from Appleby tax sheltering law firm
matched 13.4 million account records with public
business registrations data from across Caribbean
Solution and Benefits
• Exposed tax sheltering practices of Apple, Nike
• Revealed hidden connections among politicians
and nations, like Wilbur Ross & Putin’s son in law
• Triggered government tax evasion investigations in
US, UK, Europe, India, Australia, Bermuda, Canada
and Cayman Islands within 2 days.
Background
• International Consortium of Investigative
Journalists (ICIJ), Pulitzer Prize winning journalists
• Fourth blockbuster investigation using Neo4j to
reveal connections in text-based, and account-
based data leaked from offshore law firms and
government records about the “1% Elite”
ICIJ Paradise Papers INVESTIGATIVE JOURNALISM
Fraud Detection / Knowledge Graph8

9. “Graph analysis is possibly the single most effective competitive
differentiator for organizations pursuing data-driven operations
and decisions after the design of data capture.”
By the end of 2018, 70% of leading organizations will have one or
more pilot or proof-of-concept efforts underway utilizing graph
databases.
“Forrester estimates that over 25% of enterprises will be using
graph databases by 2017”
IT Market Clock for Database Management Systems, 2014
https://www.gartner.com/doc/2852717/it-market-clock-database-management
TechRadar™: Enterprise DBMS, Q1 2014
http://www.forrester.com/TechRadar+Enterprise+DBMS+Q1+2014/fulltext/-/E-RES106801
Making Big Data Normal with Graph Analysis for the Masses, 2015
http://www.gartner.com/document/3100219
Analyst Expectations Three Years Ago
9

10. The Largest Graph Innovation Network
10,000,000+ Downloads & Docker pulls
Neo4j Downloads
250+ customers, 500+ startups
50% from Global 2000
100+
Technology and Services Partners
450+ annual events & 10k attendees
Graph and Neo4j awareness and training
1,000+
Neo4j GraphConnect NYC Attendees
100,000+
Online and Classroom Education Registrants & Meetup Members

11. SOFTWARE
FINANCIAL
SERVICES
RETAIL MEDIA
SOCIAL
NETWORKS
TELECOM HEALTH
Neo4j Adoption

12. Users Love Neo4j

13. 13
“Neo4j continues to dominate the
graph database market.”
Noel Yuhanna
Forrester Market Overview:
Graph Database Vendors
October, 2017

14. Why is Neo4j Succeeding?
Focus on Simplifying the Adoption, Awareness and Success of Graphs
Open Source business model
• Commitment to developers – DevRel, Training, Events, etc.
• Commitment to sharing Cypher, the SQL for graphs, on Apache
Native Graph Technology Leadership
• Commitment to data integrity, scale and performance
• Expanding User Communities to Data Scientists, IT, Analysts & Business Users
Highest Investment in Customer Success
• Applications offer real impact, and we spread these success stories

15. 15
Neo4j Graph Platform
Development &
Administration
Analytics
Tooling
BUSINESS USERS
DEVELOPERS
ADMINS
Graph
Analytics
Graph
Transactions
Data Integration
Discovery & Visualization
DATA
ANALYSTS
DATA
SCIENTISTS
Drivers & APIs
APPLICATIONS
AI
BIG DATA IT

16. 16
Grow Graphs
by reaching deeper into the
enterprise with support for more
users, roles and use cases

17. Connecting Roles in the Enterprise
Data Scientists
Real-time
Graph traversal
Application
Data Lake
& DWHS
Big Data IT &
Architecture
Developers
& Prod Mgrs
AI
Analysts and
Business Users
Chief Officers of …
Knowledge
Graphs
Digital
Transformation
Initiatives
Compliance, Data, Digital, Information,
Innovation, Marketing, Operations, Risk
& Security…

18. Real-Time
Recommendations
Dynamic Pricing
Artificial Intelligence
& IoT-applications
Fraud Detection
Network
Management
Customer
Engagement
Supply Chain
Efficiency
Identity and Access
Management
Relationship-Driven Applications

19. Sample of Connected Graphs
Organization Identity & Access Network & IT Ops

20. The Knowledge Graph Problem
Organizations have difficulty maintaining their corporate memory due to a
variety of reasons:
• Growth which drives need for new and continuous education
• Digitalization / Digital Transformation initiatives to identify new markets
• Turnover where long term knowledge is lost
• Aging infrastructures and siloed information

21. Negative Consequences
• Lack of knowledge sharing slows project progress, and creates
inconsistencies even among team members.
• Organizations don’t know what they don’t know, nor do they know what
they know.
• Data Scientists, and therefore the organization, are slow to recognize or
react to changing market conditions, therefore they miss opportunities to
innovate
• Bad information is spread inadvertently which erodes corporate trust
• Brand damage when using this info in front of customers

22. Purchases
RELATIONAL DB WIDE COLUMN
STORE
Views
DOCUMENT
STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE
STORE
Product
Catalogue
DOCUMENT
STORE
Category Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory LocationCategory Price ConfigurationsLocation Purchase ViewReviewReturn In-store PurchasesInventory
Products Customers / Users
Location
Data Lives Across the Enterprise

23. Data Lake
Purchases
RELATIONAL DB
Product
Catalogue
DOCUMENT
STORE
WIDE COLUMN
STORE
Views
DOCUMENT
STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE
STORE
Recommendations require an operational
workload — it’s in the moment, real-time!
Good for Analytics, BI, Map Reduce
Non-Operational, Slow Queries

24. Purchases
RELATIONAL DB
Product
Catalogue
DOCUMENT
STORE
WIDE COLUMN
STORE
Views
DOCUMENT
STORE
User Review
RELATIONAL DB
In-Store
Purchase
Shopping
Cart
KEY VALUE
STORE
Connector
Apps and Systems
Real-Time
Queries

25. Customer
Adress
Store
Phone
Customer
Email
EmailAdress
Phone
Product
Product
Category
Y
Street
Region
Product
Store
Street
Category
X
Simple Enterprise Knowledge Graphs
Customer Graph
Product Graph
Supply Graph

26. Customer Graph
Customer
Adress
Store
Phone
Customer
Email
EmailAdress
Phone
Product
Product
Category
Y
Street
Region
Product
Store
Street
Category
X
Product Graph
Supply Graph
Simple Enterprise Knowledge Graph

27. Customer Graph
Customer
Adress
Store
Phone
Customer
Email
EmailAdress
Phone
Product
Product
Category
Y
Street
Region
Product
Store
Street
Category
X
Product Graph
Supply Graph
Unlock the Institutional Memory
Real-time product
recommendations
Fraud
Detection
Real-time supply chain
management
Risk Management

28. How it should be
• Information, especially in Analytics, Research departments and customer
service should have a searchable, consistent repository, or representation
of a repository, from which to store and draw institutional knowledge.
• Corporations who maintain a knowledge graph will develop higher
degrees of consistency across all areas of business.
• Improving long term corporate memory should be a mandate from the C-
suite

29. What’s required to get there
• Institutional memory requires a solution that can integrate diverse data sets, often in
text due to the legacy nature of that information and return “Context” as a result.
• Connections and relationships, cause and effect correlation needs to be materialized
and persisted permanently.
• All information must be indexed, searchable and shareable.
• The solution must be agile, easily expandable and adaptable to changing business
conditions
• The solution needs to be a combination of text-based NLP, ElasticSearch and Graphs.
• Information must be easy to visualize and leverage in your processes and workflows

30. Money
Transferring
Purchases Bank
Services
Neo4j powers
360° view and
update of
information in
real-time
Neo4j
Cluster
SENSE
Transaction
stream
RESPOND
Alerts &
notification
SETS Context for Traversals
Relational
database
ElasticSearch &
Data Lake
Visualization UI
Fine Tune Patterns
Develop Patterns
Data Science-team
Merchant
Data
Credit
Score
Data
Other 3rd
Party Data
Data-set used to
explore new
insights and
develop new
algorithms as
graph expands
Neo4j In Action

31. 31
Graph Boosted Artificial Intelligence
Knowledge Graphs
Provide Rich
Context for AI
AI Visibility
Human-Friendly
Graph Visualization
Graph Enhanced AI Models
Faster, More
Accurate Development
Graph Execution of AI
Operationalize Real-Time
OLAP and Monitoring
Graph Analytics
Enrich AI Inputs with
Graph Algorithms
Graph System of Record
Maintain a Source of
Connected AI Truth

32. Case Studies
Neo4j Case Studies

33. Background
• Brazil's largest bank, #38 on Forbes G2000
• $61B annual sales 95K employees
• Most valuable brand in Brazil
• 28.9M credit card & 25.6M debit card accounts
• High integrity, customer-centric values
Business Problem
• Data silos made assessing credit worthiness hard
• High sensitivity to fraud activity
• 73% of all transactions over internet and mobile
• Needed real-time detection for 2,000 analysts
• Scale to trillions of relationships
Solution and Benefits
• Credit monitoring and fraud detection application
• 4.2M nodes & 4B relationships for 100 analysts
• Grow to 93T relationships for 2000 analysts by 2021
• Real time visibility into money flow across multiple
customers
Itau Unibanco FINANCIAL SERVICES
Fraud Detection / Credit Monitoring33
CE Customer since 2016 Q1EE Customer since Q2 2017

34. Background
• Large global bank
• Deploying Reference Data to users and systems
• 12 data domains, 18 datasets, 400+ integrations
• Complex data management infrastructure
Business Problem
• Master data silos were inflexible and hard to
consume
• Needed simplification to reduce redundancy
• Reduce risk when data is in consumers’ hands
• Dramatically improve efficiency
Solution and Benefits
• Data distribution flows improved dramatically
• Knowledge Base improves consumer access
• Ad-hoc analytics improved
• Governance, lineage and trust improved
• Better service level from IT to data consumers
UBS FINANCIAL SERVICES
Master Data Management / Knowledge Graph34
CE Customer since 2016 Q1EE Customer since 2015

35. Background
• SF-based C2C rental platform
• Dataportal democratizes data access for
growing number of employees while improving
discoverability and trust
• Data strewn everywhere—in silos, in segmented
departments, nothing was universally accessible
Business Problem
• Data-driven culture hampered by variety and
dependability of data, tribal knowledge and
word-of-mouth distribution
• Needed visibility into information usage, context,
lineage and popularity across company of 3,000+
Solution and Benefits
• Offers search with context & metadata, user &
team-centric pages for origin & lineage
• Nodes are resources: data tables, dashboards,
reports, users, teams, business outcomes, etc.
• Relationships reflect consumption, production,
association, etc.
• Neo4j, Elasticsearch, Python
Airbnb Dataportal TRAVEL TECHNOLOGY
Knowledge Graph, Metadata Management35
CE users since 2017

36. Background
• 5 year long drug discovery research
• Parse & Navigate over 25 Million scientific papers
• Sourced from National Library of Research and
tagging of “Medical Subject Headers” (MeSH tags)
Business Problem
• Seeking to automate phenotype, compound and
protein cell behavior research by using previously
documented research more effectively
• Text mining for research elements like DNA strings,
proteins, RNA, chemicals and diseases
Solution and Benefits
• Found ways to identify compound interaction
behavior from millions of research documents
• Relations between biological entities can be
identified and validated by biologic experts
• Still very challenging to keep up-to-date, add
genomics data, and find a breakthrough
Novartis PHARMACEUTICAL RESEARCH
Content Management / Biomedical Research36
CE Customer since 2016 Q1CE Customer since 2012

37. Background
• How Neo4j is used in investigations
• Non-technical reporters manually gather data
• “Low-tech” data curation
• Journalists want to model data as a story, not
as data
Business Problem
• Identify repeated business relationships among
individuals and their holdings and accounts
• Scan documents and identify possible entities,
then create relationships between people and
documents.
• Names and alias variances
Solution and Benefits
• Uses Neo4j in “story discovery” phase
• Uncovers shortest paths for leads for reporters
• Many investigations underway now
Columbia University EDUCATION
Investigative Journalism / Fraud Detection37
CE Customer since 2016 Q1EE Customer since 2015 Q4

38. Background
• Large Nordic Telecom Provider
• 1M Broadband routers deployed in Sweden
• Half of subscribership are over 55yrs old
• Each household connects 10 devices
• Goal to improve customer experience
Business Problem
• Broadband router enhancement to improve
customer experience
• Context-based in home services
• How to build smart home platform that allows
vendors to build new “home-centric” apps
Solution and Benefits
• New Features deployed to 1M homes
• API-based platform for easy apps that:
• Automatically assemble Spotify playlists
based on who is in the house
• Notify parents when children get home
• Build smart shopping lists
TELIA ZONE TELECOMMUNICATIONS
Smart Home / Internet of Things38
EE Customer since 2016 Q4

39. Business Problem
• Needed new asset management backbone to
handle scheduling, ads, sales and pushing linear
streams to satellites
• Novell LDAP content hierarchy not flexible
enough to store graph-based business content
Solution and Benefits
• Neo4j selected for performance and domain fit
• Flexible, native storage of content hierarchy
• Graph includes metadata used by all systems:
TV series-->Episodes-->Blocks with Tags-->
Linked Content, tagged with legal rights, actors,
dubbing et al
Background
• Nashville-based developer of lifestyle-
oriented content for TV, digital, mobile and
publishing
• Web properties generate tens of millions of
unique visitors per month
Scripps Networks MEDIA AND ENTERTAINMENT
Knowledge Graph / Asset Management39

40. Business Problem
• Needed to reimagine existing system to beat
competition and provide 360-degree view of
customers
• Channel complexity necessitated move to graph
database
• Needed an enterprise-ready solution
Solution and Benefits
• Leapfrogged competition and increased digital
business by 23%
• Handles new data from mobile, social networks,
experience and governance sources
• After launch of new Neo4j MDM, Pitney Bowes
stock declared a Buy
Background
• Connecticut-based leader in digital marketing
communications
• Helps clients provide omni-channel experience
with in-context information
Pitney Bowes MARKETING COMMUNICATIONS
Master Data Management40

41. Background
• Large Public University – “U-Dub”
• IT staff for 80K+ students and employees
• Transforming IT systems from mainframe to cloud
• Providing IT & data warehousing services to 3
campuses, 6 hospitals, and 6,300 EDW users
Business Problem
• Old Sharepoint metadata was too complicated
for users, not flexible and not transparent
• $1B project to migrate HR system from
mainframe to Workday needed to be smooth
• Future projects needed repeatable predictability
• Needed new glossary, impact analysis, analytics
Solution and Benefits
• Consulted with NDU peers, built simple model
• Built Visualizer with Elasticsearch, Neo4j & D3.js
• Improved predictability, lineage, and impact
understanding for over 6,300 users
University of Washington EDUCATION & RESEARCH
Metadata Management, IT & Network Operations41
CE Customer since 2016 Q1

42. Background
• World's largest hospitality / hotel company
• 7th largest web site on internet
• 1.5 M hotel rooms offered online by 2018
• Revenue Management System that allows
property managers to update their pricing rates
Business Problem
• Provide the right room & price at the right time
• Old rate program was inflexible and bogged down
as they increased the pricing options per property
per day
• Lay the path to be an innovator in the future
Solution and Benefits
• 2016-era rate program embeds Neo4j as "cache"
• Created a graph per hotel for 4500 properties in 3
clusters
• 1000% increase in volume over 4 years
• 50% decrease in infrastructure costs
• "Use Neo4j Support!"
MARRIOTT TRAVEL & HOSPITALITY SERVICES
Pricing Recommendations Engine42
EE Customer since 2014 Q2

43. Case Studies for Knowledge Graphs
and Recommendation Engines
eBay ShopBot

44. Background
• Personal shopping assistant
• Converses with buyer via text, picture and voice
to provide real-time recommendations
• Combines AI and natural language understanding
(NLU) in Neo4j Knowledge Graph
• First of many apps in eBay's AI Platform
Business Problem
• Improve personal context in online shopping
• Transform buyer-provided context into ideal
purchase recommendations over social platforms
• "Feels like talking to a friend"
Solution and Benefits
• 3 developers, 8M nodes, 20M relationships
• Needed high-performance traversals to respond
to live customer requests
• Easy to train new algorithms and grow model
• Generating revenue since launch
eBay ShopBot ONLINE RETAIL
Knowledge Graph powers Real-Time Recommendations44
EE Customer since 2016 Q3

45. Case Study: Knowledge Graphs at eBay

46. Case Study: Knowledge Graphs at eBay

47. Case Study: Knowledge Graphs at eBay

48. Case Study: Knowledge Graphs at eBay

49. Bags
Case Study: Knowledge Graphs at eBay

50. Men’s Backpack
Handbag
Case Study: Knowledge Graphs at eBay

51. https://shopbot.ebay.com/
Try it out at:
Case Study: Knowledge Graphs at eBay

52. Case Studies for Knowledge Graphs
and Recommendation Engines
eBay ShopBot