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Neo4j GraphTalks

Herzlich Willkommen!
!
!
!
!
Oktober 2016!
bruno.ungermann@neotechnology.com
Neo4j GraphTalks !
•  09:00-09:30 Frühstück und Networking
•  09:30-10:00 Einführung in Graph-Datenbanken und Neo4j 
 
 
 
 
(Bruno Ungermann, Neo4j)
•  10:00-10.30 ADAMA: From Data Sharing to Knowledge Management
(Georgiana Francescotti, Digital & Knowledge Services, ADAMA AGRICULTURE B.V.)
•  10.30-11.00 ADAMA: Erfahrungswerte aus der Implementierung und Demo
(Darko Krizic, CTO PRODYNA AG)
•  Open End (PRODYNA: Christoph Körner, Moritz Schmidt, Michael Joszt, Buket Oezcan, Lena Hawelky
NeoTechnology: Dirk Möller, Alexander Erdl)
Berlin Metro Map
The Internet (oT)
Domain Model Logistics Process
Traditional Approach: Tables
Graph Model: Nodes & Relationships
Container !
Load!
USING ROUTE
Depart 2014-04-15
Arrive 2014-04-28
FROM
TO
USING ROUTE
Depart 2014-04-15
Arrive 2014-04-28
FUELING
USING_CARRIER
LOAD
USING CARRIER
Vessel!
Physical !
Container!
Container !
Load!
Shipment!
Carrier!
Emission!
Class A!
Shipment!
Carrier!
Route!
10520km!
Route!
823km!
!
Fueling!
Max Weight 80!
Type of Gas B!
!
Town:!
Tokyo!
Town:!
Hong Kong!
Town:!
Hamburg!
Container !
Load!Container !
Load!Container !
Load!
Parcel !
Weight
15.5kg!
Intuitiveness
A Naturally Adaptive Model
Flexibility
“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 other tested database types!
Speed
Discrete Data
Minimally 

connected data
Neo4j is designed for data relationships
Other NoSQL
 Relational DBMS
 Neo4j Graph DB
Connected Data
Focused on
Data Relationships
Development Benefits

Easy model maintenance

Easy query
Deployment Benefits

Ultra high performance
Minimal resource usage
Use the Right Database for the Right Job
2000 2003 2007 2009 ! 2011! 2013! 2014! 2015!2012!
GraphConnect, 

first conference for
graph DBs
First 

Global 2000 

Customer
	
	
Introduced 

first and only
declarative query
language for
property graph

Published
O’Reilly 

book

on Graph
Databases
First 

native 

graph DB 

in 24/7
production

Invented
property
graph
model
Contributed
first graph DB
to open
source
Extended 

graph data
model to 

labeled
property
graph

150+ customers
50K+ monthly

downloads
500+ graph 

DB events

worldwide	
	
Neo4j: The Graph Database Leader
Financial

Services
Communications
Health &

Life Sciences
HR &

Recruiting
Media &

Publishing
Social

Web
Industry 

& Logistics
Entertainment
 Consumer Retail
 Information Services
Business Services
Neo4j Leads the Graph Database Revolution
2012 à 2015
“Forrester estimates that over 25% of enterprises will be using graph
databases by 2017”
“Neo4j is the current market leader in graph databases.”
“Graph analysis is possibly the single most effective competitive
differentiator for organizations pursuing data-driven operations and
decisions after the design of data capture.”
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
Graph Databases – and Their Potential to Transform How We Capture Interdependencies (Enterprise Management Associates)

http://blogs.enterprisemanagement.com/dennisdrogseth/2013/11/06/graph-databasesand-potential-transform-capture-interdependencies/
Neo4j Leads the Graph Database Revolution
Graph Based Success
NEO4j USE CASES
Real Time Recommendations
Meta Data Management
Fraud Detection
Identity & Access Management
Graph Based Search
Network & IT-Operations
			Logistik	
RD
BM
S
					CRM	
RD
BM
S
					Mails	Ma
ils
yst
Dokume
nte	Fil
es
ys
em
Media	
Library	Fil
es
ys
em
						CMS	
RD
BM
S
				Social	
RD
BM
S
		LogFiles	
RD
BM
S
Ecommer
ce	RD
BM
S
HASOPENED_ACCOUNT
BRIDGES
POWERS
POWERS
INCLUD
E
INCLUD
E
CREA
TE
IN
SOUR
CE
IN
IN
TRUST
S
TRUST
S
AUTHENTICAT
ES
CAN_RE
AD
!
Business Problem
•  Optimize walmart.com user experience
•  Connect complex buyer and product data to gain
super-fast insight into customer needs and
product trends
•  RDBMS couldn’t handle complex queries
Solution and Benefits
•  Replaced complex batch process real-time online
recommendations
•  Built simple, real-time recommendation system with
low-latency queries
•  Serve better and faster recommendations by
combining historical and session data
Background

•  Founded in 1962 and based in Arkansas
•  11,000+ stores in 27 countries with walmart.com
online store
•  2M+ employees and $470 billion in annual
revenues
Walmart RETAIL
Real-Time Recommendations!18!
!
Background
•  One of the world’s largest logistics carriers
•  Projected to outgrow capacity of old system
•  New parcel routing system
Single source of truth for entire network

B2C and B2B parcel tracking

Real-time routing: up to 7M parcels per day
Business Problem
•  Needed 365x24x7 availability
•  Peak loads of 3000+ parcels per second
•  Complex and diverse software stack
•  Need predictable performance, linear scalability
•  Daily changes to logistics network: route from any
point to any point
Solution and Benefits
•  Ideal domain fit: a logistics network is a graph 
•  Extreme availability, performance via clustering
•  Greatly simplified routing queries vs. relational
•  Flexible data model reflect real-world data variance
much better than relational
•  Whiteboard-friendly model easy to understand!
Accenture LOGISTICS
19! Real-Time Routing Recommendations!
!
Background
•  Second largest communications company 

in France
•  Based in Paris, part of Vivendi Group, partnering
with Vodafone
Solution and Benefits
•  Flexible inventory management supports modeling,
aggregation, troubleshooting
•  Single source of truth for entire network
•  New apps model network via near-1:1 mapping
between graph and real world
•  Schema adapts to changing needs
Network and IT Operations !
SFR COMMUNICATIONS
Business Problem
•  Infrastructure maintenance took week to plan due
to need to model network impacts
•  Needed what-if to model unplanned outages
•  Identify network weaknesses to uncover need for
additional redundancy
•  Info lived on 30+ systems, with daily changes
LINKED	
LINKED	
DEPENDS_ON	
Router	 Service	
Switch	 Switch	
Router	
Fiber	Link	 Fiber	Link	
Fiber	Link	
Oceanfloor	
Cable	
20!
!
Background
•  Oslo-based telcom provider is #1 in Nordic
countries and #10 in world
•  Online, mission-critical, self-serve system lets
users manage subscriptions and plans
•  availability and responsiveness is critical to
customer satisfaction
Business Problem
•  Logins took minutes to retrieve relational
access rights
•  Massive joins across millions of plans,
customers, admins, groups
•  Nightly batch production required 9 hours and
produced stale data
Solution and Benefits
•  Shifted authentication from Sybase to Neo4j
•  Moved resource graph to Neo4j
•  Replaced batch process with real-time login
response measured in milliseconds that delivers real-
time data, vw yday’s snapshot
•  Mitigated customer retention risks
Identity and Access Management !
Telenor COMMUNICATIONS
SUBSCRIBED_BY!
CONTROLLED_B
Y!
PART_OF!USER_ACCESS!
Account!
Customer!
Customer!User!
Subscription!
21!
!
Background
•  Top investment bank with $1+ trillion in assets
•  Using a relational database and Gemfire to manage
employee permissions to research document and
application-service resources
•  Permissions for new investment managers and
traders provisioned manually 
Business Problem
•  Lost an average of 5 days per new hire while they
waited to be granted access to hundreds of
resources, each with its own permissions
•  Replace an unsuccessful onboarding process
implemented by a competitor
•  Regulations left no room for error
Solution and Benefits
•  Store models, groups and entitlements in Neo4j
•  Exceeded performance requirements
•  Major productivity advantage due to domain fit
•  Graph visualization ease permissioning process
•  Fewer compromises than with relational
•  Expanded Neo4j solution to online brokerage
London Investment Bank FINANCIAL SERVICES
Identity and Access Management!22!
!
Background
•  Global financial services firm with trillions of
dollars in assets
•  Varying compliance and governance
considerations
•  Incredibly complex transaction systems, with ever-
growing opportunities for fraud
Business Problem 
•  Needed to spot and prevent fraud detection in real
time, especially in payments that fall within “normal”
behavior metrics
•  Needed more accurate and faster credit risk
analysis for payment transactions
•  Needed to dramatically reduce chargebacks
Solution and Benefits
•  Lowered TCO by simplifying credit risk analysis and
fraud detection processes
•  Identify entities and connections uniquely
•  Saved billions by reducing chargebacks and fraud
•  Enabled building real-time apps with non-uniform
data and no sparse tables or schema changes
London and New York Financial FINANCIAL SERVICES
Fraud Detection!
s!
23!
!
Background
•  Panama based lawyers Mossack & Fonseca do
business in hosting “letterbox companies”
•  Suspected to support tax saving and organized
crime
•  Altogether: 2.6 TB, 11 milo files, 214.000 letter box
companies 
Business Problem 
•  Goal to unravel chains Bank-Person–Client–
Address–Intermediaries – M&F
•  Earlier cases: spreadsheet based analysis (back-
and-forth) & pencil to extract such connections
•  This case: sheer amount of data & arbitrarily chain
length condemn such approaches to fail
Solution and Benefits
•  400 journalists, investigate/update/share, 2 people
with IT background
•  Identify connections quickly and easily
•  Fast Results wouldn‘t be possible without GraphDB
Panama Papers Fraud Detection
Fraud Detection!24!
MDM Status Quo – viele kleine Königreiche
Dr. Andreas Weber | semantic data management | 11.11.2016 !
QS / LIMS
ERP
Logistik
Warehouse-
management
Produkt-management
Technisches
PDM/PLM
Dokumenten-
management
Excel
Excel
Power-point
Power-point
Excel
Excel
!
Adidas Meta Data Management
27! Shared Meta Data Service!
Background
•  Global leader in sporting goods industry services
firm footware, apparel, hardware, 14.5 bln sales,
53,000 people
•  Multitude of products, markets, media, assets and
audiences
Business Problem 
•  Beset by a wide array of information silos including
data about products, markets, social media, master
data, digital assets, brand content and more
•  Provide the most compelling and relevant content to
consumers
•  Offering enhanced recommendations to drive
revenue
Solution and Benefits
•  Save time and cost through stadardized access to
content sharing-system with internal teams, partners,
IT units, fast, reliable, searchable avoiding
reduandancy
•  Inprove customer experience and increase revenue by
providing relevant content and recommentations
!
Background
•  Mid-size German insurer founded in 1858
•  Project executed by Delvin, a subsidiary 

of die Bayerische Versicherung and an IT insurance
specialist
Business Problem
•  Field sales needed easy, dynamic, 24/7 access to
policies and customer data
•  Existing DB2 system unable to meet performance
and scaling demands
Solution and Benefits
•  Enabled flexible searching of policies and associated
personal data
•  Raised the bar on industry practices
•  Delivered high performance and scalability 
•  Ported existing metadata easily
Die Bayerische INSURANCE
Master Data Management!28!
!
Background
•  Leading European Airline 
•  100+ mln passengers 
•  2+ mln tons freight per year 
•  700+ aircrafts
Business Problem
•  Need for flexible high performant Inflight Asset
Management, onboard entertainment, byod
•  Complex data set: CMDB, CMS, Aircraft data feed,
media library
•  Maintain individual configuration for each Aircraft
•  Complex data model, aircrafts, hardware, vitual
containers, licenses, business rules, versions,
content ...
Solution and Benefits
•  Neo4j powers integrated platform that provides fast
access to all aspects needed to maintain complex
system
•  Fast implementation
•  Higly flexible data model enable constant evolution
Lufthansa Digital Asset Mangagement
29! Graph Based Search, Metadata Management!
!
Background
•  Toy Manufacturer, founded 80+ years ago, plastic
figurines sold in 50+ countries
•  100 Mio, 250 employees
•  Production Process in different countries like China
•  Polymer Processing, Children‘s toys, high
responsibility
Business Problem
•  Product related data stored in many different data
stores including SAP, Navision, Laboratory
Systems, Document Systems, Powerpoint, Excel..
•  Hard to find correct answers for authorities, ,
internally, parents
Solution and Benefits
•  Neo4j powers integrated platform that provides
visibility across whole supply chain
•  Domain Experts create and evolve data model
•  Correct answers within seconds
Schleich Product Information Management
30! Master Data Management!
ADAMA!

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Neo4j GraphTalks - Einführung in Graphdatenbanken

  • 1. Neo4j GraphTalks Herzlich Willkommen! ! ! ! ! Oktober 2016! bruno.ungermann@neotechnology.com
  • 2. Neo4j GraphTalks ! •  09:00-09:30 Frühstück und Networking •  09:30-10:00 Einführung in Graph-Datenbanken und Neo4j (Bruno Ungermann, Neo4j) •  10:00-10.30 ADAMA: From Data Sharing to Knowledge Management (Georgiana Francescotti, Digital & Knowledge Services, ADAMA AGRICULTURE B.V.) •  10.30-11.00 ADAMA: Erfahrungswerte aus der Implementierung und Demo (Darko Krizic, CTO PRODYNA AG) •  Open End (PRODYNA: Christoph Körner, Moritz Schmidt, Michael Joszt, Buket Oezcan, Lena Hawelky NeoTechnology: Dirk Möller, Alexander Erdl)
  • 7. Graph Model: Nodes & Relationships Container ! Load! USING ROUTE Depart 2014-04-15 Arrive 2014-04-28 FROM TO USING ROUTE Depart 2014-04-15 Arrive 2014-04-28 FUELING USING_CARRIER LOAD USING CARRIER Vessel! Physical ! Container! Container ! Load! Shipment! Carrier! Emission! Class A! Shipment! Carrier! Route! 10520km! Route! 823km! ! Fueling! Max Weight 80! Type of Gas B! ! Town:! Tokyo! Town:! Hong Kong! Town:! Hamburg! Container ! Load!Container ! Load!Container ! Load! Parcel ! Weight 15.5kg!
  • 9. A Naturally Adaptive Model Flexibility
  • 10. “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 other tested database types! Speed
  • 11. Discrete Data Minimally 
 connected data Neo4j is designed for data relationships Other NoSQL Relational DBMS Neo4j Graph DB Connected Data Focused on Data Relationships Development Benefits
 Easy model maintenance
 Easy query Deployment Benefits
 Ultra high performance Minimal resource usage Use the Right Database for the Right Job
  • 12. 2000 2003 2007 2009 ! 2011! 2013! 2014! 2015!2012! GraphConnect, 
 first conference for graph DBs First 
 Global 2000 
 Customer Introduced 
 first and only declarative query language for property graph Published O’Reilly 
 book
 on Graph Databases First 
 native 
 graph DB 
 in 24/7 production Invented property graph model Contributed first graph DB to open source Extended 
 graph data model to 
 labeled property graph 150+ customers 50K+ monthly
 downloads 500+ graph 
 DB events
 worldwide Neo4j: The Graph Database Leader
  • 13. Financial
 Services Communications Health &
 Life Sciences HR &
 Recruiting Media &
 Publishing Social
 Web Industry 
 & Logistics Entertainment Consumer Retail Information Services Business Services Neo4j Leads the Graph Database Revolution
  • 15. “Forrester estimates that over 25% of enterprises will be using graph databases by 2017” “Neo4j is the current market leader in graph databases.” “Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.” 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 Graph Databases – and Their Potential to Transform How We Capture Interdependencies (Enterprise Management Associates)
 http://blogs.enterprisemanagement.com/dennisdrogseth/2013/11/06/graph-databasesand-potential-transform-capture-interdependencies/ Neo4j Leads the Graph Database Revolution
  • 17. NEO4j USE CASES Real Time Recommendations Meta Data Management Fraud Detection Identity & Access Management Graph Based Search Network & IT-Operations Logistik RD BM S CRM RD BM S Mails Ma ils yst Dokume nte Fil es ys em Media Library Fil es ys em CMS RD BM S Social RD BM S LogFiles RD BM S Ecommer ce RD BM S HASOPENED_ACCOUNT BRIDGES POWERS POWERS INCLUD E INCLUD E CREA TE IN SOUR CE IN IN TRUST S TRUST S AUTHENTICAT ES CAN_RE AD
  • 18. ! Business Problem •  Optimize walmart.com user experience •  Connect complex buyer and product data to gain super-fast insight into customer needs and product trends •  RDBMS couldn’t handle complex queries Solution and Benefits •  Replaced complex batch process real-time online recommendations •  Built simple, real-time recommendation system with low-latency queries •  Serve better and faster recommendations by combining historical and session data Background •  Founded in 1962 and based in Arkansas •  11,000+ stores in 27 countries with walmart.com online store •  2M+ employees and $470 billion in annual revenues Walmart RETAIL Real-Time Recommendations!18!
  • 19. ! Background •  One of the world’s largest logistics carriers •  Projected to outgrow capacity of old system •  New parcel routing system Single source of truth for entire network
 B2C and B2B parcel tracking
 Real-time routing: up to 7M parcels per day Business Problem •  Needed 365x24x7 availability •  Peak loads of 3000+ parcels per second •  Complex and diverse software stack •  Need predictable performance, linear scalability •  Daily changes to logistics network: route from any point to any point Solution and Benefits •  Ideal domain fit: a logistics network is a graph •  Extreme availability, performance via clustering •  Greatly simplified routing queries vs. relational •  Flexible data model reflect real-world data variance much better than relational •  Whiteboard-friendly model easy to understand! Accenture LOGISTICS 19! Real-Time Routing Recommendations!
  • 20. ! Background •  Second largest communications company 
 in France •  Based in Paris, part of Vivendi Group, partnering with Vodafone Solution and Benefits •  Flexible inventory management supports modeling, aggregation, troubleshooting •  Single source of truth for entire network •  New apps model network via near-1:1 mapping between graph and real world •  Schema adapts to changing needs Network and IT Operations ! SFR COMMUNICATIONS Business Problem •  Infrastructure maintenance took week to plan due to need to model network impacts •  Needed what-if to model unplanned outages •  Identify network weaknesses to uncover need for additional redundancy •  Info lived on 30+ systems, with daily changes LINKED LINKED DEPENDS_ON Router Service Switch Switch Router Fiber Link Fiber Link Fiber Link Oceanfloor Cable 20!
  • 21. ! Background •  Oslo-based telcom provider is #1 in Nordic countries and #10 in world •  Online, mission-critical, self-serve system lets users manage subscriptions and plans •  availability and responsiveness is critical to customer satisfaction Business Problem •  Logins took minutes to retrieve relational access rights •  Massive joins across millions of plans, customers, admins, groups •  Nightly batch production required 9 hours and produced stale data Solution and Benefits •  Shifted authentication from Sybase to Neo4j •  Moved resource graph to Neo4j •  Replaced batch process with real-time login response measured in milliseconds that delivers real- time data, vw yday’s snapshot •  Mitigated customer retention risks Identity and Access Management ! Telenor COMMUNICATIONS SUBSCRIBED_BY! CONTROLLED_B Y! PART_OF!USER_ACCESS! Account! Customer! Customer!User! Subscription! 21!
  • 22. ! Background •  Top investment bank with $1+ trillion in assets •  Using a relational database and Gemfire to manage employee permissions to research document and application-service resources •  Permissions for new investment managers and traders provisioned manually Business Problem •  Lost an average of 5 days per new hire while they waited to be granted access to hundreds of resources, each with its own permissions •  Replace an unsuccessful onboarding process implemented by a competitor •  Regulations left no room for error Solution and Benefits •  Store models, groups and entitlements in Neo4j •  Exceeded performance requirements •  Major productivity advantage due to domain fit •  Graph visualization ease permissioning process •  Fewer compromises than with relational •  Expanded Neo4j solution to online brokerage London Investment Bank FINANCIAL SERVICES Identity and Access Management!22!
  • 23. ! Background •  Global financial services firm with trillions of dollars in assets •  Varying compliance and governance considerations •  Incredibly complex transaction systems, with ever- growing opportunities for fraud Business Problem •  Needed to spot and prevent fraud detection in real time, especially in payments that fall within “normal” behavior metrics •  Needed more accurate and faster credit risk analysis for payment transactions •  Needed to dramatically reduce chargebacks Solution and Benefits •  Lowered TCO by simplifying credit risk analysis and fraud detection processes •  Identify entities and connections uniquely •  Saved billions by reducing chargebacks and fraud •  Enabled building real-time apps with non-uniform data and no sparse tables or schema changes London and New York Financial FINANCIAL SERVICES Fraud Detection! s! 23!
  • 24. ! Background •  Panama based lawyers Mossack & Fonseca do business in hosting “letterbox companies” •  Suspected to support tax saving and organized crime •  Altogether: 2.6 TB, 11 milo files, 214.000 letter box companies Business Problem •  Goal to unravel chains Bank-Person–Client– Address–Intermediaries – M&F •  Earlier cases: spreadsheet based analysis (back- and-forth) & pencil to extract such connections •  This case: sheer amount of data & arbitrarily chain length condemn such approaches to fail Solution and Benefits •  400 journalists, investigate/update/share, 2 people with IT background •  Identify connections quickly and easily •  Fast Results wouldn‘t be possible without GraphDB Panama Papers Fraud Detection Fraud Detection!24!
  • 25.
  • 26. MDM Status Quo – viele kleine Königreiche Dr. Andreas Weber | semantic data management | 11.11.2016 ! QS / LIMS ERP Logistik Warehouse- management Produkt-management Technisches PDM/PLM Dokumenten- management Excel Excel Power-point Power-point Excel Excel
  • 27. ! Adidas Meta Data Management 27! Shared Meta Data Service! Background •  Global leader in sporting goods industry services firm footware, apparel, hardware, 14.5 bln sales, 53,000 people •  Multitude of products, markets, media, assets and audiences Business Problem •  Beset by a wide array of information silos including data about products, markets, social media, master data, digital assets, brand content and more •  Provide the most compelling and relevant content to consumers •  Offering enhanced recommendations to drive revenue Solution and Benefits •  Save time and cost through stadardized access to content sharing-system with internal teams, partners, IT units, fast, reliable, searchable avoiding reduandancy •  Inprove customer experience and increase revenue by providing relevant content and recommentations
  • 28. ! Background •  Mid-size German insurer founded in 1858 •  Project executed by Delvin, a subsidiary 
 of die Bayerische Versicherung and an IT insurance specialist Business Problem •  Field sales needed easy, dynamic, 24/7 access to policies and customer data •  Existing DB2 system unable to meet performance and scaling demands Solution and Benefits •  Enabled flexible searching of policies and associated personal data •  Raised the bar on industry practices •  Delivered high performance and scalability •  Ported existing metadata easily Die Bayerische INSURANCE Master Data Management!28!
  • 29. ! Background •  Leading European Airline •  100+ mln passengers •  2+ mln tons freight per year •  700+ aircrafts Business Problem •  Need for flexible high performant Inflight Asset Management, onboard entertainment, byod •  Complex data set: CMDB, CMS, Aircraft data feed, media library •  Maintain individual configuration for each Aircraft •  Complex data model, aircrafts, hardware, vitual containers, licenses, business rules, versions, content ... Solution and Benefits •  Neo4j powers integrated platform that provides fast access to all aspects needed to maintain complex system •  Fast implementation •  Higly flexible data model enable constant evolution Lufthansa Digital Asset Mangagement 29! Graph Based Search, Metadata Management!
  • 30. ! Background •  Toy Manufacturer, founded 80+ years ago, plastic figurines sold in 50+ countries •  100 Mio, 250 employees •  Production Process in different countries like China •  Polymer Processing, Children‘s toys, high responsibility Business Problem •  Product related data stored in many different data stores including SAP, Navision, Laboratory Systems, Document Systems, Powerpoint, Excel.. •  Hard to find correct answers for authorities, , internally, parents Solution and Benefits •  Neo4j powers integrated platform that provides visibility across whole supply chain •  Domain Experts create and evolve data model •  Correct answers within seconds Schleich Product Information Management 30! Master Data Management!