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Neo4j GraphTalks Oslo - Next Generation Solutions built on Neoej

The document discusses next generation solutions built on Neo4j graph database. It provides an agenda for the talk including solutions using Neo4j, recommendations, GDPR, and conclusions. It discusses how graph-based solutions with Neo4j enable flexibility, intuitiveness, and high performance for connected data scenarios. It also provides examples of using Neo4j for recommendation engines in retail, logistics, fraud detection and more. Case studies describe how Walmart and eBay improved recommendations and routing with Neo4j.

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Next Generation Solutions built on Neo4j Kees Vegter, Neo4j Graphtalk Oslo, May 9 2018
OSLO, MAY 9, 2018! Agenda! ●  Solutions using Neo4j! ●  Recommendations! ●  GDPR! ●  Conclusions!
OSLO, MAY 9, 2018! Solutions: new mindset required?!
OSLO, MAY 9, 2018! Solutions: new mindset! Yesterday:! -  Static Applications! -  Designed to fulfill current requirements! -  Performance Constraints! -  Domain experts versus IT experts! Tomorrow:! -  Flexible Applications! -  Designed to fulfill tomorrows requirements! -  Performance is not limiting ! -  Domain experts work hand in hand with IT experts!
OSLO, MAY 9, 2018! Look at this data…
OSLO, MAY 9, 2018! Swap glasses…
OSLO, MAY 9, 2018! … now look at it again, this time as a graph
Speed: Real time query enabled Graph Based Solutions! Enables Up-Sell / Cross-sell Key Features Added Value 360 degree view on data Using data Connections as a value Intuitive: Supports Business Needs Flexible: enabled for additional requirements Finding patterns within the data Detect anomalies Prevent rather than detect Enables conversation across Functions Comply to regulations What-if Analysis Telco OSS GDPR Fraud Telco BSS Recomm endations MDM Resource efficient
OSLO, MAY 9, 2018! Evolution using Neo4j! ! Neo4j Platform! ! Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists ! 3rd Party Tools! ! ! “The Graph Advantage”! ! ! Domain know-how! ! ! Professional Services! ! ! PS Packages! ! ! Graph Based Solution! !
OSLO, MAY 9, 2018! Evolution using Neo4j! ! Neo4j Platform! ! Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists ! 3rd Party Tools! ! ! “The Graph Advantage”! ! ! Domain know-how! ! ! Professional Services! ! ! PS Packages! ! ! Graph Based Solution! ! Neo4j enables Graph Based Solutions with a need for:! -  Agility! -  Intuitiveness ! -  High Performance to support connected data scenarios! -  Scalable on traversing through connected data!
OSLO, MAY 9, 2018! Recommendation Engines Building Powerful Recommendation Engines With Neo4j!
OSLO, MAY 9, 2018! “If you liked this, you might like that…” Powerful, real-time, recommendations and personalization engines have become fundamental for creating superior user experience and commercial success in retail
OSLO, MAY 9, 2018! Creating Relevance in an Ocean of Possibilities
OSLO, MAY 9, 2018! How Graph Based Recommendations Transformed the Consumer Web! People Graph “People you may know” Disruptor: Facebook! Industry: Media Ad-business! Disruptor: Amazon! Industry: Retail! People & Products “Other people also bought” People & Content “You might also like” Disruptor: Netflix! Industry: Broadcasting Media!
OSLO, MAY 9, 2018! Today Recommendation Engines are At the Core of Digitization in Retail! Product Recommendations Effective product recommendation algorithms has become the new standard in online retail — directly affecting revenue streams and the shopping experience.! Logistics/Delivery Routing recommendations allows companies to save money on routing and delivery, and provide better and faster service.! Promotion recommendations Building powerful personalized promotion engines is another area within retail that requires input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j.!
OSLO, MAY 9, 2018! ... and Recommendation Engines are at the core of: Content Recommendations Content recommendation algorithms are the basis to use portals providing value added content — directly affecting the behaviour of the users and have them stay on the web page! Fraud Taking timely action based on patterns / recommendations you find inside connected data . May require input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j.! ! Social Networks Building powerful personalized engines to recommend new contacts, friends, based on patterns, preferences, status „friends-of-friends“ taking advantage of the value of connected data!
OSLO, MAY 9, 2018! Why Graph Based Recommendation Engines?! •  Increase revenue! •  Create Higher Engagement! •  Mitigate Risk!Value •  Real-Time capabilities! •  Ability to use the most recent transaction data! •  Flexibility to incorporate new data sources!Performance
OSLO, MAY 9, 2018! The Impact of Bad Recommendations! Characteristic! Impact for Recommendations! Examples! •  “Batch Oriented Recommendation”! •  Unable to react on real-time changes! •  Unable to fulfill real-time needs! •  Recommending “out-of-stock” products! •  Content recommendation, eg news: the latest news are the most important ones! •  Lack of Performance! •  Recommendation slow down the user interaction! •  Recommendation alternatives limited! •  Delayed response time lead to customer dissatisfaction! •  Recommend just the obvious (“similarities”) and inability to recommend more complex scenarios (Account specific and product specific and buying history and …)! •  Limited by Data Connections! •  Recommendations are limited by number of hops! •  Inability to recommend more complex correlations (eg product hierarchies and dependencies)! •  No complex recommendation algorithms supported! •  Missing Feedback Loop! •  Inability to react on Feedback! •  Customer never picks Top 3 recommendations! •  Recommendations are getting meaningless! •  No Graph Algorithm support! •  Limitations on Machine Learning approaches! •  “Centrality” for Products to be recommend can be essential!
OSLO, MAY 9, 2018! Case Studies
OSLO, MAY 9, 2018! Case study!Solving real-time recommendations for the World’s largest retailer.! Challenge •  In its drive to provide the best web experience for its customers, Walmart wanted to optimize its online recommendations.! •  Walmart recognized the challenge it faced in delivering recommendations with traditional relational database technology.! •  Walmart uses Neo4j to quickly query customers’ past purchases, as well as instantly capture any new interests shown in the customers’ current online visit – essential for making real-time recommendations.! Use of Neo4j “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 Vada, Walmart • With Neo4j, Walmart could substitute a heavy batch process with a simple and real-time graph database.! Result/Outcome
OSLO, MAY 9, 2018! Case study!eBay Tackles eCommerce Delivery Service Routing with Neo4j! Challenge •  The queries used to select the best courier for eBays routing system were simply taking too long and they needed a solution to maintain a competitive service.! •  The MySQL joins being used created a code base too slow and complex to maintain.! •  eBay is now using Neo4j’s graph database platform to redefine e-commerce, by making delivery of online and mobile orders quick and convenient.! Use of Neo4j •  With Neo4j eBay managed to eliminate the biggest roadblock between retailers and online shoppers: the option to have your item delivered the same day.! •  The schema-flexible nature of the database allowed easy extensibility, speeding up development.! •  Neo4j solution was more than 1000x faster than the prior MySQL Soltution.! Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code. Result/Outcome – Volker Pacher, eBay
OSLO, MAY 9, 2018! Example Recommendation Solution Architecture
Neo4j Database Cluster! Neo4j APOC! Recommen! dation! Algorithms! (Scheduled)! Management Dashboard! Neo4j Bolt Driver! Data Ingest! Mgmt.! …! Customer Data Sources / Systems / Applications! Legend:! Neo4j Provided Components! Custom built Neo4j/Customer! Customer/SI! Batch! Data Buffering (Queue)! Real-Time! Admin UI! System Specific Adapters / Scripts / Connecters! Admin / Superuser! Apps! Websites! Affiliate! Programs! Points of sale! User Interface! Retail Web Shop functionality / ! Shipment /! etc.!
OSLO, MAY 9, 2018! Why Graph is Superior for Recommendation Engines! Recommendation Requirement Traditional Approaches Neo4j Approach Usage of connected data over unlimited amount of „hops“ Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Real-time 360 degree view on data within your System Performance limitations with increasing number of connections / hops Traversing over connections in near real-time provided Effort required to add additional data sources to support reco Days to weeks to rewrite schema and queries Minutes to draw new data connections Time to deployment Months to years Weeks to months Response time to Recommendations Minutes to hours per query Milliseconds per query Machine Learning Enablement Static Database scheme leads to static processes ML algorithms can use Graph algorithms and take advantage of connected data Bottom line Long, ineffective and expensive Easy, fast and affordable
OSLO, MAY 9, 2018! Why Graph is Superior for Recommendation Engines! Recommendation Requirement Traditional Approaches Neo4j Approach Usage of connected data over unlimited amount of „hops“ Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Real-time 360 degree view on data within your System Performance limitations with increasing number of connections / hops Traversing over connections in near real-time provided Effort required to add additional data sources to support reco Days to weeks to rewrite schema and queries Minutes to draw new data connections Time to deployment Months to years Weeks to months Response time to Recommendations Minutes to hours per query Milliseconds per query Machine Learning Enablement Static Database scheme leads to static processes ML algorithms can use Graph algorithms and take advantage of connected data Bottom line Long, ineffective and expensive Easy, fast and affordable A Fortune 500 customer brought in Neo4j to improve content recommendations quality... and will decommission 48 ‘wide column store’ servers (half a million USD in list EC2 hosting costs) in favor of a *3-machine* Neo4j cluster which handles the same load.
OSLO, MAY 9, 2018! How Neo4j Differentiates from other Databases! Visualization Queries Processing Storage Non-Native Graph DB Native Graph DB RDBMS Optimized for graph workloads
OSLO, MAY 9, 2018! What about Machine Learning?! Neo4j is an enabler technology:! •  Detecting Recommendation patterns via Cypher queries! •  Recommendation Algorithms based on scores! •  Feedback loop ! •  Learn from feedback (eg never used “friends recommendation”) and change scoring! •  Algorithm to automatically add relevant connections! •  ….!
OSLO, MAY 9, 2018! Neo4j powered Recommendation Engine!Characteristic! Benefit for Recommendation Solution! •  Agility! •  Constant learning of recommendations given feedback enabled! •  Enabled for Future Requirements! •  Solution can be built iteratively! •  Fast implementation cycles! •  Schema free DB supports “connect anything”! •  Intuitiveness! •  Enable Business Analysts to use technology! •  All channels and data sources can be easily connected! •  Speed! •  Unlimited number of traversals to detect potential recommendations ! •  Response time enables fraud prevention! •  Leverage Data Connections! •  360 degree customer view enabled / provided! •  Scalability! •  Hardware efficiency with real-time patterns! •  TCO/ROI! •  Adding on top of existing infrastructure protects investments!
GDPR Compliance
OSLO, MAY 9, 2018! GDPR Summary! •  GDPR = General Data Protection Regulation! •  Adopted by the EU Parliament on 24th May 2016! •  Will apply from 25th May 2018! •  Applies to both Controllers and Processors! •  Applies to organisations operating within the EU, as well as organisations outside the EU that offer goods or services to individuals in the EU.! •  Covers a broad definition of personal data! •  Defines lawful basis for processing personal data, which include consent and contract! •  Defines significant fines for non-compliance!
OSLO, MAY 9, 2018! Individual Rights Under GDPR! Right to be informed! Right of access! Right to rectification! Right to erasure! Right to restriction of processing! Right to data portability! Right to object! Rights regarding automated decision making!
OSLO, MAY 9, 2018! Key GDPR Requirements ! Organizations that embrace the new GDPR regulations and provide the right levels of transparency and traceability for personal information have a big opportunity to win the hearts, minds and business of consumers. What data do you have? Is it accurate? Where is the data stored? How and when did you obtain the data? Why do you have the data? Who has access to the data? Do you have permission to use the data? For what purpose? Is the data secure? How does the data travel through your systems? Does the data ever cross international borders?
OSLO, MAY 9, 2018! GDPR: Risk Mitigation vs. Competitive Advantage! Be a leader and have a solution ready on time Improve Brand! Reduce Risk Leverage connected data to drive analytics for threat detection & business forecasts Competitive Advantage Spend is strategic Increase ROI! Reduce Risk Become a trusted enterprise, delight customers and DPA Increase CSAT! Become Trusted! Improve Brand Strategic solution ensures data governance and solution maintenance Reduce Risk! Reduce Cost Stay on the sidelines to see what others are doing Increased Risk Look to get by with bare minimum solution Increased Risk Spend is sunk investment to just mitigate risk Low to No ROI! Unknown Risk Mitigation Solution results in less than happy subjects, DPO and DPA Lower CSAT! Minimal Risk Reduction Focus on data governance and solution maintenance is low Increased Risk! Increased Cost
OSLO, MAY 9, 2018! Why Graphs?!
OSLO, MAY 9, 2018! GDPR needs: Connected Data & Visualization! Graph database is the perfect solution to this vast amount of connected data; traditional approaches with an RDBMS or other NoSQL databases just cannot cut it
OSLO, MAY 9, 2018! Graph Database is the Right GDPR Foundation ! Neo4j includes powerful visualization tools that enable you to model and track the movement of sensitive data through your systems
OSLO, MAY 9, 2018! Data Modeling and Definition 1 Data Transformation2 Consent Management 3 Entitlement 4 GRAPHS IN METADATA MANAGEMENT
OSLO, MAY 9, 2018! #1 Data Modeling and Definition
OSLO, MAY 9, 2018! Party CUST_SCHEMA Party First Name HAS_LOGICAL_ATT CUST_SCHEMA _PARTY.FIRST _NM CUST_SCHE MA.PARTY SHEMA_HAS_TABLE 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_SCHEMA _PARTY.LAST_ NM TABLE_HAS_COL GENERATES Enterprise! Ontology! Application Logical Model! Physical ! Schema! CUST_SCHE MA.ROLE
OSLO, MAY 9, 2018! #2 Data Transformation
OSLO, MAY 9, 2018! ETL_PR OC_1 SALES_SCH EMA Normali ze_Date SLS_SCHE MA.PROD UCT SLS_SCHEM A.SALES.DA TE SLS_SCH EMA.SAL ES #2 Data Transformation Channel_ Normaliz ation SLS_SCHEM A.SALES.CH ANNEL BusinessView! Integration Middleware!Operational 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: Transact ion_Dat e SENDS RECEIVES CONCEPTUAL_ELEMENT_FOR Star_Sche ma Star_Sc hema.Ti me
OSLO, MAY 9, 2018! #3 Consent management
OSLO, MAY 9, 2018! #3 Consent Management + MDM Amsterdam" NL K.Vegter +316239004 … kees@neo4j .com EMAIL_FOR ADDRESS_FOR kees@gmai l.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 }
OSLO, MAY 9, 2018! #4 Entitlement
OSLO, MAY 9, 2018! #4 Entitlement User 1 User 3 User 2 Exclusio n List G1 Resource 1 Group 1 Resource 2 Group 3 D CRUD MEMBER MEMBER
OSLO, MAY 9, 2018! #4 Entitlement #3 Consent Management #2 Data Transformation #1 Data Modelling and Definition Graphs in Metadata Management and Data Governance! # …
OSLO, MAY 9, 2018! Why Graph is Superior for GDPR ! GDPR Task Traditional Approaches Modern Neo4j Approach Trace data through enterprise systems Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Preserve the integrity of data lineage Broken data paths and lineage, especially with NoSQL databases Continuous, unbroken data paths at all times Effort required to add new data and systems Days to weeks to rewrite schema and queries Minutes to draw new data connections Time to deployment Months to years Weeks to months Response time to GDPR requests Minutes to hours per query Milliseconds per query Form of GDPR responses Text reports that are not visual and prove very little Visuals of personal data and the path it follows through your systems Bottom line Long, ineffective and expensive Easy, fast and affordable
OSLO, MAY 9, 2018! Dashboards & Visual Reports!
Personal Data Map Role Based Dashboards - Subject View!
Personal Data Map!
Role Based Dashboards - Management View!
Consents per Subject
Data Lineage Report for ‘John Doe’!
John Doe
Example Architecture!
OSLO, MAY 9, 2018! Graph Database is the Right GDPR Foundation ! Marketing! CRM! Customer! Service! Online! Store! Logistics! Extract GDPR Events/Data! Financials!
OSLO, MAY 9, 2018! Graph Database is the Right GDPR Foundation ! Extract GDPR Events/Data! Marketing! CRM! Customer! Service! Online! Store! Logistics! Financials!
OSLO, MAY 9, 2018! Conclusion! (graphs)-[:ARE]-> (everywhere) and! (Solutions)-[:NEED]-> (graphs)
OSLO, MAY 9, 2018! Who can help?! ! Neo4j Platform! ! Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists ! 3rd Party Tools! ! ! “The Graph Advantage”! ! ! Domain know-how! ! ! Professional Services! ! ! PS Packages! ! ! Graph Based Solution! ! Professional Services:! -  Extend and leverage Domain Expertise! -  Best Practices! -  Using Building Blocks! -  Don’t “re-invent the wheel”! -  Speed up development and deployment! -  Access to Neo4j infrastructure (Development, Support, Product management)!
Q & A
Extra: Demo

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Neo4j GraphTalks Oslo - Next Generation Solutions built on Neoej

  • 1.
  • 2.
    OSLO, MAY 9,2018! Agenda! ●  Solutions using Neo4j! ●  Recommendations! ●  GDPR! ●  Conclusions!
  • 3.
    OSLO, MAY 9,2018! Solutions: new mindset required?!
  • 4.
    OSLO, MAY 9,2018! Solutions: new mindset! Yesterday:! -  Static Applications! -  Designed to fulfill current requirements! -  Performance Constraints! -  Domain experts versus IT experts! Tomorrow:! -  Flexible Applications! -  Designed to fulfill tomorrows requirements! -  Performance is not limiting ! -  Domain experts work hand in hand with IT experts!
  • 5.
    OSLO, MAY 9,2018! Look at this data…
  • 6.
    OSLO, MAY 9,2018! Swap glasses…
  • 7.
    OSLO, MAY 9,2018! … now look at it again, this time as a graph
  • 8.
    Speed: Real timequery enabled Graph Based Solutions! Enables Up-Sell / Cross-sell Key Features Added Value 360 degree view on data Using data Connections as a value Intuitive: Supports Business Needs Flexible: enabled for additional requirements Finding patterns within the data Detect anomalies Prevent rather than detect Enables conversation across Functions Comply to regulations What-if Analysis Telco OSS GDPR Fraud Telco BSS Recomm endations MDM Resource efficient
  • 9.
    OSLO, MAY 9,2018! Evolution using Neo4j! ! Neo4j Platform! ! Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists ! 3rd Party Tools! ! ! “The Graph Advantage”! ! ! Domain know-how! ! ! Professional Services! ! ! PS Packages! ! ! Graph Based Solution! !
  • 10.
    OSLO, MAY 9,2018! Evolution using Neo4j! ! Neo4j Platform! ! Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists ! 3rd Party Tools! ! ! “The Graph Advantage”! ! ! Domain know-how! ! ! Professional Services! ! ! PS Packages! ! ! Graph Based Solution! ! Neo4j enables Graph Based Solutions with a need for:! -  Agility! -  Intuitiveness ! -  High Performance to support connected data scenarios! -  Scalable on traversing through connected data!
  • 11.
    OSLO, MAY 9,2018! Recommendation Engines Building Powerful Recommendation Engines With Neo4j!
  • 12.
    OSLO, MAY 9,2018! “If you liked this, you might like that…” Powerful, real-time, recommendations and personalization engines have become fundamental for creating superior user experience and commercial success in retail
  • 13.
    OSLO, MAY 9,2018! Creating Relevance in an Ocean of Possibilities
  • 14.
    OSLO, MAY 9,2018! How Graph Based Recommendations Transformed the Consumer Web! People Graph “People you may know” Disruptor: Facebook! Industry: Media Ad-business! Disruptor: Amazon! Industry: Retail! People & Products “Other people also bought” People & Content “You might also like” Disruptor: Netflix! Industry: Broadcasting Media!
  • 15.
    OSLO, MAY 9,2018! Today Recommendation Engines are At the Core of Digitization in Retail! Product Recommendations Effective product recommendation algorithms has become the new standard in online retail — directly affecting revenue streams and the shopping experience.! Logistics/Delivery Routing recommendations allows companies to save money on routing and delivery, and provide better and faster service.! Promotion recommendations Building powerful personalized promotion engines is another area within retail that requires input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j.!
  • 16.
    OSLO, MAY 9,2018! ... and Recommendation Engines are at the core of: Content Recommendations Content recommendation algorithms are the basis to use portals providing value added content — directly affecting the behaviour of the users and have them stay on the web page! Fraud Taking timely action based on patterns / recommendations you find inside connected data . May require input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j.! ! Social Networks Building powerful personalized engines to recommend new contacts, friends, based on patterns, preferences, status „friends-of-friends“ taking advantage of the value of connected data!
  • 17.
    OSLO, MAY 9,2018! Why Graph Based Recommendation Engines?! •  Increase revenue! •  Create Higher Engagement! •  Mitigate Risk!Value •  Real-Time capabilities! •  Ability to use the most recent transaction data! •  Flexibility to incorporate new data sources!Performance
  • 18.
    OSLO, MAY 9,2018! The Impact of Bad Recommendations! Characteristic! Impact for Recommendations! Examples! •  “Batch Oriented Recommendation”! •  Unable to react on real-time changes! •  Unable to fulfill real-time needs! •  Recommending “out-of-stock” products! •  Content recommendation, eg news: the latest news are the most important ones! •  Lack of Performance! •  Recommendation slow down the user interaction! •  Recommendation alternatives limited! •  Delayed response time lead to customer dissatisfaction! •  Recommend just the obvious (“similarities”) and inability to recommend more complex scenarios (Account specific and product specific and buying history and …)! •  Limited by Data Connections! •  Recommendations are limited by number of hops! •  Inability to recommend more complex correlations (eg product hierarchies and dependencies)! •  No complex recommendation algorithms supported! •  Missing Feedback Loop! •  Inability to react on Feedback! •  Customer never picks Top 3 recommendations! •  Recommendations are getting meaningless! •  No Graph Algorithm support! •  Limitations on Machine Learning approaches! •  “Centrality” for Products to be recommend can be essential!
  • 19.
    OSLO, MAY 9,2018! Case Studies
  • 20.
    OSLO, MAY 9,2018! Case study!Solving real-time recommendations for the World’s largest retailer.! Challenge •  In its drive to provide the best web experience for its customers, Walmart wanted to optimize its online recommendations.! •  Walmart recognized the challenge it faced in delivering recommendations with traditional relational database technology.! •  Walmart uses Neo4j to quickly query customers’ past purchases, as well as instantly capture any new interests shown in the customers’ current online visit – essential for making real-time recommendations.! Use of Neo4j “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 Vada, Walmart • With Neo4j, Walmart could substitute a heavy batch process with a simple and real-time graph database.! Result/Outcome
  • 21.
    OSLO, MAY 9,2018! Case study!eBay Tackles eCommerce Delivery Service Routing with Neo4j! Challenge •  The queries used to select the best courier for eBays routing system were simply taking too long and they needed a solution to maintain a competitive service.! •  The MySQL joins being used created a code base too slow and complex to maintain.! •  eBay is now using Neo4j’s graph database platform to redefine e-commerce, by making delivery of online and mobile orders quick and convenient.! Use of Neo4j •  With Neo4j eBay managed to eliminate the biggest roadblock between retailers and online shoppers: the option to have your item delivered the same day.! •  The schema-flexible nature of the database allowed easy extensibility, speeding up development.! •  Neo4j solution was more than 1000x faster than the prior MySQL Soltution.! Our Neo4j solution is literally thousands of times faster than the prior MySQL solution, with queries that require 10-100 times less code. Result/Outcome – Volker Pacher, eBay
  • 22.
    OSLO, MAY 9,2018! Example Recommendation Solution Architecture
  • 23.
    Neo4j Database Cluster! Neo4jAPOC! Recommen! dation! Algorithms! (Scheduled)! Management Dashboard! Neo4j Bolt Driver! Data Ingest! Mgmt.! …! Customer Data Sources / Systems / Applications! Legend:! Neo4j Provided Components! Custom built Neo4j/Customer! Customer/SI! Batch! Data Buffering (Queue)! Real-Time! Admin UI! System Specific Adapters / Scripts / Connecters! Admin / Superuser! Apps! Websites! Affiliate! Programs! Points of sale! User Interface! Retail Web Shop functionality / ! Shipment /! etc.!
  • 24.
    OSLO, MAY 9,2018! Why Graph is Superior for Recommendation Engines! Recommendation Requirement Traditional Approaches Neo4j Approach Usage of connected data over unlimited amount of „hops“ Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Real-time 360 degree view on data within your System Performance limitations with increasing number of connections / hops Traversing over connections in near real-time provided Effort required to add additional data sources to support reco Days to weeks to rewrite schema and queries Minutes to draw new data connections Time to deployment Months to years Weeks to months Response time to Recommendations Minutes to hours per query Milliseconds per query Machine Learning Enablement Static Database scheme leads to static processes ML algorithms can use Graph algorithms and take advantage of connected data Bottom line Long, ineffective and expensive Easy, fast and affordable
  • 25.
    OSLO, MAY 9,2018! Why Graph is Superior for Recommendation Engines! Recommendation Requirement Traditional Approaches Neo4j Approach Usage of connected data over unlimited amount of „hops“ Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Real-time 360 degree view on data within your System Performance limitations with increasing number of connections / hops Traversing over connections in near real-time provided Effort required to add additional data sources to support reco Days to weeks to rewrite schema and queries Minutes to draw new data connections Time to deployment Months to years Weeks to months Response time to Recommendations Minutes to hours per query Milliseconds per query Machine Learning Enablement Static Database scheme leads to static processes ML algorithms can use Graph algorithms and take advantage of connected data Bottom line Long, ineffective and expensive Easy, fast and affordable A Fortune 500 customer brought in Neo4j to improve content recommendations quality... and will decommission 48 ‘wide column store’ servers (half a million USD in list EC2 hosting costs) in favor of a *3-machine* Neo4j cluster which handles the same load.
  • 26.
    OSLO, MAY 9,2018! How Neo4j Differentiates from other Databases! Visualization Queries Processing Storage Non-Native Graph DB Native Graph DB RDBMS Optimized for graph workloads
  • 27.
    OSLO, MAY 9,2018! What about Machine Learning?! Neo4j is an enabler technology:! •  Detecting Recommendation patterns via Cypher queries! •  Recommendation Algorithms based on scores! •  Feedback loop ! •  Learn from feedback (eg never used “friends recommendation”) and change scoring! •  Algorithm to automatically add relevant connections! •  ….!
  • 28.
    OSLO, MAY 9,2018! Neo4j powered Recommendation Engine!Characteristic! Benefit for Recommendation Solution! •  Agility! •  Constant learning of recommendations given feedback enabled! •  Enabled for Future Requirements! •  Solution can be built iteratively! •  Fast implementation cycles! •  Schema free DB supports “connect anything”! •  Intuitiveness! •  Enable Business Analysts to use technology! •  All channels and data sources can be easily connected! •  Speed! •  Unlimited number of traversals to detect potential recommendations ! •  Response time enables fraud prevention! •  Leverage Data Connections! •  360 degree customer view enabled / provided! •  Scalability! •  Hardware efficiency with real-time patterns! •  TCO/ROI! •  Adding on top of existing infrastructure protects investments!
  • 29.
  • 30.
    OSLO, MAY 9,2018! GDPR Summary! •  GDPR = General Data Protection Regulation! •  Adopted by the EU Parliament on 24th May 2016! •  Will apply from 25th May 2018! •  Applies to both Controllers and Processors! •  Applies to organisations operating within the EU, as well as organisations outside the EU that offer goods or services to individuals in the EU.! •  Covers a broad definition of personal data! •  Defines lawful basis for processing personal data, which include consent and contract! •  Defines significant fines for non-compliance!
  • 31.
    OSLO, MAY 9,2018! Individual Rights Under GDPR! Right to be informed! Right of access! Right to rectification! Right to erasure! Right to restriction of processing! Right to data portability! Right to object! Rights regarding automated decision making!
  • 32.
    OSLO, MAY 9,2018! Key GDPR Requirements ! Organizations that embrace the new GDPR regulations and provide the right levels of transparency and traceability for personal information have a big opportunity to win the hearts, minds and business of consumers. What data do you have? Is it accurate? Where is the data stored? How and when did you obtain the data? Why do you have the data? Who has access to the data? Do you have permission to use the data? For what purpose? Is the data secure? How does the data travel through your systems? Does the data ever cross international borders?
  • 33.
    OSLO, MAY 9,2018! GDPR: Risk Mitigation vs. Competitive Advantage! Be a leader and have a solution ready on time Improve Brand! Reduce Risk Leverage connected data to drive analytics for threat detection & business forecasts Competitive Advantage Spend is strategic Increase ROI! Reduce Risk Become a trusted enterprise, delight customers and DPA Increase CSAT! Become Trusted! Improve Brand Strategic solution ensures data governance and solution maintenance Reduce Risk! Reduce Cost Stay on the sidelines to see what others are doing Increased Risk Look to get by with bare minimum solution Increased Risk Spend is sunk investment to just mitigate risk Low to No ROI! Unknown Risk Mitigation Solution results in less than happy subjects, DPO and DPA Lower CSAT! Minimal Risk Reduction Focus on data governance and solution maintenance is low Increased Risk! Increased Cost
  • 34.
    OSLO, MAY 9,2018! Why Graphs?!
  • 35.
    OSLO, MAY 9,2018! GDPR needs: Connected Data & Visualization! Graph database is the perfect solution to this vast amount of connected data; traditional approaches with an RDBMS or other NoSQL databases just cannot cut it
  • 36.
    OSLO, MAY 9,2018! Graph Database is the Right GDPR Foundation ! Neo4j includes powerful visualization tools that enable you to model and track the movement of sensitive data through your systems
  • 37.
    OSLO, MAY 9,2018! Data Modeling and Definition 1 Data Transformation2 Consent Management 3 Entitlement 4 GRAPHS IN METADATA MANAGEMENT
  • 38.
    OSLO, MAY 9,2018! #1 Data Modeling and Definition
  • 39.
    OSLO, MAY 9,2018! Party CUST_SCHEMA Party First Name HAS_LOGICAL_ATT CUST_SCHEMA _PARTY.FIRST _NM CUST_SCHE MA.PARTY SHEMA_HAS_TABLE 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_SCHEMA _PARTY.LAST_ NM TABLE_HAS_COL GENERATES Enterprise! Ontology! Application Logical Model! Physical ! Schema! CUST_SCHE MA.ROLE
  • 40.
    OSLO, MAY 9,2018! #2 Data Transformation
  • 41.
    OSLO, MAY 9,2018! ETL_PR OC_1 SALES_SCH EMA Normali ze_Date SLS_SCHE MA.PROD UCT SLS_SCHEM A.SALES.DA TE SLS_SCH EMA.SAL ES #2 Data Transformation Channel_ Normaliz ation SLS_SCHEM A.SALES.CH ANNEL BusinessView! Integration Middleware!Operational 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: Transact ion_Dat e SENDS RECEIVES CONCEPTUAL_ELEMENT_FOR Star_Sche ma Star_Sc hema.Ti me
  • 42.
    OSLO, MAY 9,2018! #3 Consent management
  • 43.
    OSLO, MAY 9,2018! #3 Consent Management + MDM Amsterdam" NL K.Vegter +316239004 … kees@neo4j .com EMAIL_FOR ADDRESS_FOR kees@gmai l.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 }
  • 44.
    OSLO, MAY 9,2018! #4 Entitlement
  • 45.
    OSLO, MAY 9,2018! #4 Entitlement User 1 User 3 User 2 Exclusio n List G1 Resource 1 Group 1 Resource 2 Group 3 D CRUD MEMBER MEMBER
  • 46.
    OSLO, MAY 9,2018! #4 Entitlement #3 Consent Management #2 Data Transformation #1 Data Modelling and Definition Graphs in Metadata Management and Data Governance! # …
  • 47.
    OSLO, MAY 9,2018! Why Graph is Superior for GDPR ! GDPR Task Traditional Approaches Modern Neo4j Approach Trace data through enterprise systems Complex queries with hundreds of join tables Simple single query traverses all enterprise systems Preserve the integrity of data lineage Broken data paths and lineage, especially with NoSQL databases Continuous, unbroken data paths at all times Effort required to add new data and systems Days to weeks to rewrite schema and queries Minutes to draw new data connections Time to deployment Months to years Weeks to months Response time to GDPR requests Minutes to hours per query Milliseconds per query Form of GDPR responses Text reports that are not visual and prove very little Visuals of personal data and the path it follows through your systems Bottom line Long, ineffective and expensive Easy, fast and affordable
  • 48.
    OSLO, MAY 9,2018! Dashboards & Visual Reports!
  • 49.
    Personal Data Map RoleBased Dashboards - Subject View!
  • 50.
  • 51.
    Role Based Dashboards- Management View!
  • 52.
  • 53.
    Data Lineage Reportfor ‘John Doe’!
  • 54.
  • 55.
  • 56.
    OSLO, MAY 9,2018! Graph Database is the Right GDPR Foundation ! Marketing! CRM! Customer! Service! Online! Store! Logistics! Extract GDPR Events/Data! Financials!
  • 57.
    OSLO, MAY 9,2018! Graph Database is the Right GDPR Foundation ! Extract GDPR Events/Data! Marketing! CRM! Customer! Service! Online! Store! Logistics! Financials!
  • 58.
    OSLO, MAY 9,2018! Conclusion! (graphs)-[:ARE]-> (everywhere) and! (Solutions)-[:NEED]-> (graphs)
  • 59.
    OSLO, MAY 9,2018! Who can help?! ! Neo4j Platform! ! Graph Transactions Graph Analytics Data Integration Development & Admin Analytics Tooling Drivers & APIs Discovery & Visualization Developers Admins Applications Business Users Data Analysts Data Scientists ! 3rd Party Tools! ! ! “The Graph Advantage”! ! ! Domain know-how! ! ! Professional Services! ! ! PS Packages! ! ! Graph Based Solution! ! Professional Services:! -  Extend and leverage Domain Expertise! -  Best Practices! -  Using Building Blocks! -  Don’t “re-invent the wheel”! -  Speed up development and deployment! -  Access to Neo4j infrastructure (Development, Support, Product management)!
  • 60.
  • 61.