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![#3 Consent Management + MDM
2 Blackfriars
Bridge rd.
J.Barrasa
+44776611…
jesus@neo4j
.com
PHO
NE_FO
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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 }](https://image.slidesharecdn.com/gt-frankfurt-jbarrasa-171123100606/85/GraphTalks-Frankfurt-Graph-based-Metadata-Management-Data-Governance-35-320.jpg)
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GraphTalks Frankfurt - Graph-based Metadata Management & Data Governance
1) The document discusses using graph databases and graph-based approaches for metadata management, data governance, and complying with GDPR regulations. 2) Key applications mentioned include using graphs for data modelling and definition, tracking data lineage, managing consent for personal data use, and implementing fine-grained entitlement and access controls. 3) Examples of organizations using Neo4j, a graph database, are provided for applications like metadata lineage tracking, identity and consent management, and assessing impacts of data changes for GDPR compliance.
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GraphTalks Frankfurt - Graph-based Metadata Management & Data Governance
- 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 AGRAPH?
- 3. A way ofrepresenting data DATA DATA
- 4. Relational Database Good for: Well-understood datastructures 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 datastructures 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
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- 15. Look at thisdata…
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- 17. … now lookat it again, this time as a graph
- 18. Use of Graphshas 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 theworld’s first “off the shelf” graph database designed to derive value from data relationships
- 20. 13.4 million leakedfiles
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- 22. Panama Papers: Thesize and the shape
- 25. WHAT DID WELEARN 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 RealTime Recommendations Fraud Detection Identity & Access Mgmnt Dependency Modelling in Telecoms Knowledge Graph
- 28. VALUE FROM GRAPHS INMDM, DATA GOVERNANCE AND METADATA MANAGEMENT
- 29. Data Modelling andDefinition 1 Data Lineage2 Consent Management 3 Entitlement 4 GRAPHS IN METADATA MANAGEMENT
- 30. #1 Data Modellingand 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 DataModelling 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
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- 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 IntegrationMiddlewareOperational 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
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- 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 }
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- 37. #4 Entitlement User 1User 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 onGDPR & Governance
- 41. Big 4 Bankin London: Using Neo4j for Metadata Lineage for GDPR
- 42. • GDPR: Protecting, Categorisingand Auditing personal data. • Lineage • Changes tracking • Impact analysis APM + ITOA Graph Database APM + ITOA Data Classification
- 43. Global Information ServicesGroup: Using Neo4j for Identity and Consent Management
- 44. Identity and consentmanagement • 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”
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