GraphTalks Frankfurt Jesus Barrasa, Neo4j

1. Graph-based Metadata Management &
Data Governance
Dr. Jesús Barrasa - @BarrasaDV
Leverage the power of Graphs for GDPR
14 November 2017
GraphTalks - Frankfurt

2. WHAT IS A GRAPH?

3. A way of representing data
DATA DATA

4. 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

5. 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

6. THE PROPERTY GRAPH
DATA MODEL

7. A Graph Is

8. ROAD
Incident
LIGHT
A Graph Is
LIGHT

9. HAS
AVAILABLE
HOTEL
ROOMS
AVAILABILITY
A Graph Is

10. ACCOUNT
ADDRESS
PERSON
PERSON
NAME
STREET
BANK
NAME
COMPANY
BANK
BAHAMAS
A Graph Is

11. KNOWS
KNOWS
KNOWS
WORKS_AT
WORKS_AT
WORKS_AT
COMPANY
STANFORD
STUDIED_AT
KNOWS
NEO
COLUMBIA
STUDIED_AT
STU
D
IED
_AT
STUDIED_AT
NAME:ANNE
A Graph
SINCE:2012
RELATIONSHIPS
NODE
PROPERTY

12. A Graph
NAME:ANNE
SINCE:2012

14. GRAPH THINKING

15. Look at this data…

16. Swap glasses…

17. … now look at it again, this time as a graph

18. Use of Graphs has created some of the most successful companies in the world
C
34,3%B
38,4%A
3,3%
D
3,8%
1,8%
1,8%
1,8%
1,8%
1,8%
E
8,1%
F
3,9%

19. Neo4j is the world’s first “off the shelf” graph database
designed to derive value from data relationships

20. 13.4 million leaked files

21. A year ago…

22. Panama Papers: The size and the shape

25. WHAT DID WE LEARN FROM THE
PANAMA PAPERS?

26. Finance HR &
Recruiting
Manufacturing
& Logistics
Health CareTelco Retail
Today we see graph-projects in virtually every industry
Government

27. NEO4j USE CASES
Real Time Recommendations
Fraud Detection
Identity & Access Mgmnt
Dependency Modelling in Telecoms
Knowledge Graph

28. VALUE FROM GRAPHS
IN MDM, DATA GOVERNANCE AND
METADATA MANAGEMENT

29. Data Modelling and Definition
1
Data Lineage2
Consent Management 3
Entitlement 4
GRAPHS IN
METADATA
MANAGEMENT

30. #1 Data Modelling and
Definition

31. Party
CUST_SCHEMA
Party
First NameHAS_LOGICAL_ATT
CUST_SCH
EMA.ROLE
CUST_SCHEM
A_PARTY.FIRST_
NM
CUST_SCH
EMA.PARTY
SHEMA_HAS_TABLE
#1 Data Modelling and Definition
Party
Last Name
HAS_LOGICAL_ATT
SHEMA_HAS_TABLE
GENERATES
CONCEPT_FOR
CONCEPT_FOR
CONCEPT_FOR
HAS_NAME
CUSTOMER
NAMECUSTOMER
HAS_PHONE
TABLE_HAS_COL
CUST_SCHEM
A_PARTY.LAST_
NM
TABLE_HAS_COL
GENERATES
Enterprise Ontology
Application Logical
Model
Physical Schema

32. m
#2 Data Lineage

33. ETL_PROC
_1
SALES_SCHEMA
Normaliz
e_Date
SLS_SCHEM
A.PRODUCT
SLS_SCHEMA.S
ALES.DATE
SLS_SCHE
MA.SALES
SHEMA_HAS_TABLE
#2 Data Lineage
Channel_No
rmalization
SHEMA_HAS_TABLE
HAS_COL
SLS_SCHEMA.S
ALES.CHANNELHAS_COL
BusinessView
Integration MiddlewareOperational Systems
HAS_INPUT
HAS_INPUT
Time.time_key
Time.day_of_
week
Enterprise DWH
HAS_OUTPUT
HAS_OUTPUT
HAS_COL
HAS_COL
TechnicalView
Billing
System
EDWH
CDE:
Transaction
_Date
SENDS RECEIVES
CONCEPTUAL_ELEMENT_FOR
Star_Schema
Star_Sch
ema.Time

34. #3 Consent management

35. #3 Consent Management + MDM
2 Blackfriars
Bridge rd.
J.Barrasa
+44776611…
jesus@neo4j
.com
PHO
NE_FO
R
EMAIL_FOR
ADDRESS_FOR
jb@outlook.
com
EMAIL_FOR
{ contrib: ‘XYZ’,
permittedFor: [UC1,UC4],
consentUntil : 31-12-19 }
{ contrib: ‘internal’,
permittedFor: [UC3],
consentUntil : 31-12-20 }
{ contrib: ‘internal’,
permittedFor: [UC3],
consentUntil : 31-12-20 }
{ contrib: ‘internal’,
permittedFor: [UC3],
consentUntil : 31-12-20 }
EMAIL_FOR
{ contrib: ‘LMN’,
permittedFor: [UC2,UC6],
consentUntil : 31-12-20 }

36. #4 Entitlement

37. #4 Entitlement
User 1 User 3User 2
Exclusion
List G1
Resource 1
Group 1
Resource 2
Group 3
D
MEMBER
CRUD
MEMBER
R
M
EM
BER
MEMBER
CRUD

40. Recent Projects on GDPR & Governance

41. Big 4 Bank in London: Using Neo4j for Metadata
Lineage for GDPR

42. • GDPR: Protecting,
Categorising and Auditing
personal data.
• Lineage
• Changes tracking
• Impact analysis
APM +
ITOA
Graph
Database
APM +
ITOA
Data
Classification

43. Global Information Services Group: Using Neo4j for
Identity and Consent Management

44. Identity and consent management
• Sales want to…
“… know which people I can compliantly contact with a mailing
in vertical market x ?”
• Client wants to…
“… see the statement that allowed to contact me”
”… see a full statement of all the information you hold on me”
”… have my contact details suppressed from future mailings”
• Prod Man wants to…
“… assess the impact of removing data from supplier x”
“… test the effects of adjusting consent parameters”

45. DEMO

46. THANK YOU!