Using data relationships to make connections between individual data records transforms the data you already have into something much more powerful. This webinar will explain how both young and established companies have adopted graph thinking - and how they’ve risen to dominate their fields.

1. Graph Thinking
Andreas Kollegger
Product Designer

2. Graph Thinking
the missing link
in your data

3. Chilenje Clinic
Lusaka, Zambia

4. • Patient Care
• Decision Support
• Public Health Research
• WHO Reporting
Medical Record
System

5. • Current data
• Prior history
• Local context
• How is the data related?
• What patterns emerge from the relationships?
• Which patterns matter?
• "Assessment" makes connections,
creating new data
How does
this work?

6. Some data
is missing...

7. • Epidemiology
• Cultural Norms
• Environmental Factors
• Agricultural Practices
• Patient Relationships
• Doctor Relationships
• Related Research
• ...

8. How much data
do you need?

9. "The person with the
most data, wins."
– Tim O'Reilly

10. 1. Close relationships determine
relevance
Number of relationships increases
importance
What data
do you need?

11. Naturally, that leads to
pattern matching...

14. The internet

15. Genetic Ancestry of One Single Corn Variety

16. Andreas' Linkedin Network
Andreas Kollegger

17. aph Thinking has created some of the most successful companies in the wo

18. WHAT IS A GRAPH?

19. A way of representing data
DATA DATA

20. Relational
Database
Good for:
Well-understood data structures
that don’t change too frequently
A way of representing data
Known problems involving discrete
parts of the data, or minimal
connectivity

21. Graph
Database
Relational
Database
Good for:
Well-understood data structures
that don’t change too frequently
Known problems involving discrete
parts of the data, or minimal
connectivity
A way of representing data
Good for:
Dynamic systems: where the data
topology is difficult to predict
Dynamic requirements:
the evolve with the business
Problems where the relationships in
data contribute meaning & value

22. THE PROPERTY
GRAPH DATA
MODEL

23. A Graph Is

24. ROAD
TRAFFIC
LIGHTS
A Graph Is

25. HAS
HOTEL
ROOMS
AVAILABLE
A Graph Is

26. KNOWS
KNOWS
WORKS_AT
WORKS_AT
WORKS_AT
COMPANY
STANFORD
STUDIED_AT
NEO
COLUMBIA
STUDIED_AT NAME:ANNE
A Graph
RELATIONSHIPS
NODE
PROPERTY

27. A Graph
NAME:ANNE

29. Graph Thinking in Practice
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

30. Graph Thinking in Practice
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
GRAPH THINKING:
Real Time Recommendations

31. “As the current market leader in graph
databases, and with enterprise features
for scalability and availability, Neo4j is the
right choice to meet our demands.”Marcos Wada
Software Developer, Walmart
Graph Thinking with Neo4j
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

32. Graph Thinking in Practice
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
GRAPH THINKING:
Master Data Management

33. Neo4j is the heart of Cisco HMP: used for
governance and single source of truth and a
one-stop shop for all of Cisco’s hierarchies.
Graph Thinking with Neo4j
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

34. Graph Thinking in Practice
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
GRAPH THINKING:
Fraud Detection

35. “Graph databases offer new methods of
uncovering fraud rings and other
sophisticated scams with a high-level of
accuracy, and are capable of stopping
advanced fraud scenarios in real-time.”Gorka Sadowski
Cyber Security Expert
Graph Thinking with Neo4j
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

36. GRAPH THINKING:
Graph Based Search
IN
Graph Thinking in Practice
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

37. Uses Neo4j to manage the digital assets inside
of its next generation in-flight entertainment
system.
Graph Thinking with Neo4j
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

38. Graph Thinking in Practice
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
GRAPH THINKING:
Network & IT-Operations

39. Uses Neo4j for network topology
analysis for big telco service
providers
Graph Thinking with Neo4j
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

40. GRAPH THINKING:
Identity And Access Management
Graph Thinking in Practice
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

41. UBS was the recipient of the
2014 Graphie Award for “Best
Identify And Access
Management App”
Graph Thinking with Neo4j
Real Time Recommendations
Master Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations

42. WHY GRAPH THINKING?

43. Intuitivness
Speed
Agility

44. Intuitivness
Speed
Agility

45. Intuitivness

46. Intuitivness
Speed
Agility

47. Speed
“We found Neo4j to be literally thousands of times faster
than our prior MySQL solution, with queries that require
10-100 times less code. Today, Neo4j provides eBay with
functionality that was previously impossible.”
- Volker Pacher, Senior Developer
“Minutes to milliseconds” performance
Queries up to 1000x faster than RDBMS or other NoSQL

48. Intuitivness
Speed
Agility

49. A Naturally Adaptive Model
A Query Language Designed
for Connectedness
+
=Agility

50. Cypher
Typical Complex SQL Join The Same Query using Cypher
MATCH (boss)-[:MANAGES*0..3]->(sub),
(sub)-[:MANAGES*1..3]->(report)
WHERE boss.name = “John Doe”
RETURN sub.name AS Subordinate,
count(report) AS Total
Project Impact
Less time writing queries
• More time understanding the answers
• Leaving time to ask the next question
Less time debugging queries:
• More time writing the next piece of code
• Improved quality of overall code base
Code that’s easier to read:
• Faster ramp-up for new project members
• Improved maintainability & troubleshooting

51. Ann DanLoves
CYPHER

52. Ann DanLoves

53. Ann DanLoves
(Dan)(Ann) -[:LOVES]->

54. Ann DanLoves
(:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})

55. Ann DanLoves
(:Person {name:”Ann"}) –[:LOVES]-> (:Person {name:"Dan"})

56. Ann DanLoves
Node Relationship Node
(:Person {name:"Ann"}) –[:LOVES]-> (:Person {name:"Dan"})

57. Query: Whom does Ann love?
MATCH (:Person {name:"Ann"})–[:LOVES]->(whom)
RETURN whom

58. Users Love Cypher

59. Graph Thinking
focuses on relationships
to turn data into information
and uses patterns to find meaning

60. It's all about
relationships & patterns

61. THANK YOU!