Ce diaporama a bien été signalé.
Nous utilisons votre profil LinkedIn et vos données d’activité pour vous proposer des publicités personnalisées et pertinentes. Vous pouvez changer vos préférences de publicités à tout moment.

GraphTalks Frankfurt - Graph-based Metadata Management & Data Governance

204 vues

Publié le

GraphTalks Frankfurt
Jesus Barrasa, Neo4j

Publié dans : Technologie
  • Soyez le premier à commenter

GraphTalks Frankfurt - Graph-based Metadata Management & Data Governance

  1. 1. Graph-based Metadata Management & Data Governance Dr. Jesús Barrasa - @BarrasaDV Leverage the power of Graphs for GDPR 14 November 2017 GraphTalks - Frankfurt
  2. 2. WHAT IS A GRAPH?
  3. 3. A way of representing data DATA DATA
  4. 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. 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. 6. THE PROPERTY GRAPH DATA MODEL
  7. 7. A Graph Is
  8. 8. ROAD Incident LIGHT A Graph Is LIGHT
  9. 9. HAS AVAILABLE HOTEL ROOMS AVAILABILITY A Graph Is
  10. 10. ACCOUNT ADDRESS PERSON PERSON NAME STREET BANK NAME COMPANY BANK BAHAMAS A Graph Is
  11. 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. 12. A Graph NAME:ANNE SINCE:2012
  13. 13. GRAPH THINKING
  14. 14. Look at this data…
  15. 15. Swap glasses…
  16. 16. … now look at it again, this time as a graph
  17. 17. 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%
  18. 18. Neo4j is the world’s first “off the shelf” graph database designed to derive value from data relationships
  19. 19. 13.4 million leaked files
  20. 20. A year ago…
  21. 21. Panama Papers: The size and the shape
  22. 22. WHAT DID WE LEARN FROM THE PANAMA PAPERS?
  23. 23. Finance HR & Recruiting Manufacturing & Logistics Health CareTelco Retail Today we see graph-projects in virtually every industry Government
  24. 24. NEO4j USE CASES Real Time Recommendations Fraud Detection Identity & Access Mgmnt Dependency Modelling in Telecoms Knowledge Graph
  25. 25. VALUE FROM GRAPHS IN MDM, DATA GOVERNANCE AND METADATA MANAGEMENT
  26. 26. Data Modelling and Definition 1 Data Lineage2 Consent Management 3 Entitlement 4 GRAPHS IN METADATA MANAGEMENT
  27. 27. #1 Data Modelling and Definition
  28. 28. 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
  29. 29. m #2 Data Lineage
  30. 30. 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
  31. 31. #3 Consent management
  32. 32. #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 }
  33. 33. #4 Entitlement
  34. 34. #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
  35. 35. Recent Projects on GDPR & Governance
  36. 36. Big 4 Bank in London: Using Neo4j for Metadata Lineage for GDPR
  37. 37. • GDPR: Protecting, Categorising and Auditing personal data. • Lineage • Changes tracking • Impact analysis APM + ITOA Graph Database APM + ITOA Data Classification
  38. 38. Global Information Services Group: Using Neo4j for Identity and Consent Management
  39. 39. 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”
  40. 40. DEMO
  41. 41. THANK YOU!

×