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Connections Drive Digital Transformation

Jeff Morris, Head of Product Marketing at Neo4j, discusses the path to the highly connected enterprise, and why connections are crucial to staying ahead of the competition.

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Connections Drive Digital Transformation

  1. 1. 1 Welcome to Graph Tour Jeff Morris Head of Product Marketing Neo4j, Inc. @jeffmmorris
  2. 2. • Introduction • Graphs and Neo4j Explained • Use Cases in Government and Finance • Digital Transformation and the Future 2 Agenda
  3. 3. I’ve been listening to a lot of books lately…67 and counting Adjacent Possibilities City as a Maze Connecting with PeopleInnovation
  4. 4. 4 Connectedness Is the Common Theme
  5. 5. Connectedness Represented in Graphs C C A AA U S S SS S USER_ACCESS CONTROLLED_BY SUBSCRIBED _BY User Customers Accounts Subscriptions VP Staff Staff StaffStaff DirectorStaffDirector Manager Manager Manager Manager Fiber Link Fiber Link Fiber Link Ocean Cable Switch Switch Router Router Service Organizational Hierarchy Product Subscriptions Network Operations Social Networks
  6. 6. Who We Are: The Graph Platform Neo4j is an enterprise-grade native graph platform that enables you to: • Store, reveal and query data relationships • Traverse and analyze any levels of depth in real-time • Add context and connect new data on the fly • Performance • ACID Transactions • Agility • Graph Algorithms 6 Designed, built and tested natively for graphs from the start for: • Developer Productivity • Hardware Efficiency • Global Scale • Graph Adoption
  7. 7. The Rise of Connections in Data Networks of People Business Processes Knowledge Networks E.g., Risk management, Supply chain, Payments E.g., Employees, Customers, Suppliers, Partners, Influencers E.g., Enterprise content, Domain specific content, eCommerce content Data connections are increasing as rapidly as data volumes
  8. 8. CONSUMER DATA PRODUCT DATA PAYMENT DATA SOCIAL DATA SUPPLIER DATA The next wave of competitive advantage will be all about using connections to identify and build knowledge Graphs in The Age of Connections
  9. 9. Neo4j Advantages 9
  10. 10. Store / Retrieve Native Graph Architecture Advantage
  11. 11. Load Data Store / Retrieve Native Graph Architecture Advantage
  12. 12. Load Data Connectedness drives data value Store / Retrieve Neo4j reveals connections in your data Native Graph Architecture Advantage
  13. 13. CAR name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Neo4j Invented the Labeled Property Graph Model Nodes • Can have Labels to classify nodes • Labels have native indexes Relationships • Relate nodes by type and direction Properties • Attributes of Nodes & Relationships • Stored as Name/Value pairs • Can have indexes and composite indexes MARRIED TO LIVES WITH PERSON PERSON 13
  14. 14. Connectedness Differentiates Neo4j Conceive Code Compute Store Non-Native Graph DBNative Graph DB RDBMS Optimized for graph workloads
  15. 15. 10M+ Downloads 3M+ from Neo4j Distribution 7M+ from Docker Events 400+ Approximate Number of Neo4j Events per Year 50k+ Meetups Number of Meetup Members Globally Largest pool of graph technologists 50k+ Trained/certified Neo4j professionals Trained Developers
  16. 16. How Neo4j Fits — Common Architecture Patterns From Disparate Silos To Cross-Silo Connections From Tabular Data To Connected Data From Data Lake Analytics to Real-Time Operations
  17. 17. 17 Real-Time Recommendations Fraud Detection Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management Common Graph Technology Use Cases AirBnb
  18. 18. Software Financial Services Telecom Retail & Consumer Goods Media & Entertainment Other Industries Airbus Over 250 Enterprises and 10s of Thousands of Projects on Neo4j
  19. 19. Connecting Roles & Projects around Enterprise Data Hub Data Scientists Real-time Graph traversal Applications Developers & Prod Mgrs Analysts and Business Users Chief Officers of … Compliance, Data, Digital, Information, Innovation, Marketing, Operations, Risk & Security… Big Data IT & Architecture ID, Auth & Security Network & IT Ops Metadata Management 360⁰ Marketing Customer 360 Real-time Cybersecurity Account navigation • Multiple paths through organization • Graphs have strong appetite for data to add nodes & increase density of relationships • Value of graph increase according to Metcalfe’s Law (V=n2) • Customer applications iterate every 3 months
  20. 20. Graph Use Cases 20
  21. 21. 21 I was raised on the Space Program
  22. 22. “Lessons Learned Database” A half-century of collective NASA engineering knowledge “How do we make sure the command module doesn’t tip over and sink?”
  23. 23. Let’s Hear a Few Stories — David Meza, Chief Knowledge Architect at NASA “Neo4j saved well over two years of work and one million dollars of taxpayer funds.” Impact
  24. 24. The ICIJ and Fraud Detection
  25. 25. Business Problem • Find relationships between people, accounts, shell companies and offshore accounts • Journalists are non-technical • Biggest “Snowden-Style” document leak ever; 11.5 million documents, 2.6TB of data Solution and Benefits • Pulitzer Prize winning investigation resulted in robust coverage of fraud and corruption • PM of Iceland & Pakistan resigned, exposed Putin, Prime Ministers, gangsters, celebrities (Messi) • Led to assassination of journalist in Malta Background • International Consortium of Investigative Journalists (ICIJ), small team of data journalists • International investigative team specializing in cross-border crime, corruption and accountability of power • Works regularly with leaks and large datasets ICIJ Panama Papers INVESTIGATIVE JOURNALISM Fraud Detection / Knowledge Graph26
  27. 27. Bank US Account Person A Company Bank Bahamas AddressNODE RELATIONSHIP Person B
  28. 28. ICIJ Pulitzer Price Winner 2017
  29. 29. Business Problem • Find relationships between people, corporations, accounts, shell companies and offshore accounts • Journalists are non-technical • 2017 Leak from Appleby tax sheltering law firm matched 13.4 million account records with public business registrations data from across Caribbean Solution and Benefits • Exposed tax sheltering practices of Apple, Nike • Revealed hidden connections among politicians and nations, like Wilbur Ross & Putin’s son in law • Triggered government tax evasion investigations in US, UK, Europe, India, Australia, Bermuda, Canada and Cayman Islands within 2 days. Background • International Consortium of Investigative Journalists (ICIJ), Pulitzer Prize winning journalists • Fourth blockbuster investigation using Neo4j to reveal connections in text-based, and account- based data leaked from offshore law firms and government records about the “1% Elite” • Appends Neo4j-based, “Offshore Leaks Database” ICIJ Paradise Papers INVESTIGATIVE JOURNALISM Fraud Detection / Knowledge Graph30
  30. 30. Paradise Papers Metadata Model
  31. 31. Endpoint-Centric Analysis of users and their end-points 1. Navigation Centric Analysis of navigation behavior and suspect patterns 2. Account-Centric Analysis of anomaly behavior by channel 3. PC:s Mobile Phones IP-addresses User ID:s Comparing Transaction Identity Vetting Traditional Fraud Detection Methods DISCRETE ANALYSIS
  32. 32. Endpoint-Centric Analysis of users and their end-points 1. Navigation Centric Analysis of navigation behavior and suspect patterns 2. Account-Centric Analysis of anomaly behavior by channel 3. PC:s Mobile Phones IP-addresses User ID:s Comparing Transaction Identity Vetting Traditional Fraud Detection Methods DISCRETE ANALYSIS Unable to detect • Fraud rings • Fake IP addresses • Hijacked devices • Synthetic Identities • Stolen Identities • And more… Weaknesses
  33. 33. Entity Linking Analysis of relationships to detect organized crime and collusion 5. Endpoint-Centric Analysis of users and their end-points 1. Navigation Centric Analysis of navigation behavior and suspect patterns 2. Account-Centric Analysis of anomaly behavior by channel 3. PC:s Mobile Phones IP-addresses User ID:s Comparing Transaction Identity Vetting Augment Methods by Examining Connected Data DISCRETE ANALYSIS CONNECTED ANALYSIS Cross Channel Analysis of anomaly behavior correlated across channels 4.
  34. 34. ACCOUNT HOLDER 2 ACCOUNT HOLDER 1 ACCOUNT HOLDER 3 CREDIT CARD BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT PHONE NUMBER UNSECURED LOAN SSN 2 UNSECURED LOAN Modeling Fraud Transactions as an Organized Ring At first glance, each account holder looks normal. Each has multiple accounts…
  35. 35. ACCOUNT HOLDER 2 ACCOUNT HOLDER 3 BANK ACCOUNT BANK ACCOUNT BANK ACCOUNT ADDRESS PHONE NUMBER PHONE NUMBER SSN 2 UNSECURED LOAN SSN 2 UNSECURED LOAN ACCOUNT HOLDER 1 Modeling Fraud Transactions as an Organized Ring AHA! But they share common phone numbers, addresses and SSNs! These are difficult to detect using traditional methods CREDIT CARD
  36. 36. Financial Services Regulations
  37. 37. Internal Risk Models Span Investment Data Silos Graph technology unites discrete silos into a unified data source that enables banks to trace compliance data lineage
  38. 38. Tracing Risk Lineage FRTB requires banks to calculate investment risk at the trading-desk level… …requiring them to evaluate the interrelatedness of every investment on their books. Graph technology is the right solution
  39. 39. The Role of Ontologies in Financial Risk Management
  40. 40. Knowledge Graphs
  41. 41. Business Problem • Needed new asset management backbone to handle scheduling, ads, sales and pushing linear streams to satellites • Novell LDAP content hierarchy not flexible enough to store graph-based business content Solution and Benefits • Neo4j selected for performance and domain fit • Flexible, native storage of content hierarchy • Graph includes metadata used by all systems: TV series-->Episodes-->Blocks with Tags--> Linked Content, tagged with legal rights, actors, dubbing et al Background • Nashville-based developer of lifestyle- oriented content for TV, digital, mobile and publishing • Web properties generate tens of millions of unique visitors per month Scripps Networks MEDIA AND ENTERTAINMENT Master Data Management43
  42. 42. Background • SF-based C2C rental platform • Dataportal democratizes data access for growing number of employees while improving discoverability and trust • Data strewn everywhere—in silos, in segmented departments, nothing was universally accessible Business Problem • Data-driven culture hampered by variety and dependability of data, tribal knowledge and word-of-mouth distribution • Needed visibility into information usage, context, lineage and popularity across company of 3,000+ Solution and Benefits • Offers search with context & metadata, user & team-centric pages for origin & lineage • Nodes are resources: data tables, dashboards, reports, users, teams, business outcomes, etc. • Relationships reflect consumption, production, association, etc. • Neo4j, Elasticsearch, Python Airbnb Dataportal TRAVEL TECHNOLOGY Knowledge Graph, Metadata Management44 CE users since 2017
  43. 43. Graphs in Digital Transformation Transformational Customer Experiences
  44. 44. "Neo4j continues to dominate the graph database market.” October, 2017 “Customers choose Neo4j to drive innovation.” February, 2018 “In fact, the rapid rise of Neo4j and other graph technologies may signal that data connectedness is indeed a separate paradigm from the model consolidation happening across the rest of the NoSQL landscape.” March, 2018 Graph is a Unique Paradigm
  45. 45. 47 Harnessing Connections Drives Business Value Enhanced Decision Making Hyper Personalization Massive Data Integration Data Driven Discovery & Innovation Product Recommendations Personalized Health Care Media and Advertising Fraud Prevention Network Analysis Law Enforcement Drug Discovery Intelligence and Crime Detection Product & Process Innovation 360 view of customer Compliance Optimize Operations Connected Data at the Center AI & Machine Learning Price optimization Product Recommendations Resource allocation Digital Transformation Megatrends
  46. 46. Background • Personal shopping assistant • Converses with buyer via text, picture and voice to provide real-time recommendations • Combines AI and natural language understanding (NLU) in Neo4j Knowledge Graph • First of many apps in eBay's AI Platform Business Problem • Improve personal context in online shopping • Transform buyer-provided context into ideal purchase recommendations over social platforms • "Feels like talking to a friend" Solution and Benefits • 3 developers, 8M nodes, 20M relationships • Needed high-performance traversals to respond to live customer requests • Easy to train new algorithms and grow model • Generating revenue since launch eBay ShopBot ONLINE RETAIL Knowledge Graph powers Real-Time Recommendations48 EE Customer since 2016 Q3
  47. 47. Case Study: Knowledge Graphs at eBay
  48. 48. Case Study: Knowledge Graphs at eBay
  49. 49. Case Study: Knowledge Graphs at eBay
  50. 50. Case Study: Knowledge Graphs at eBay
  51. 51. Bags Case Study: Knowledge Graphs at eBay
  52. 52. Men’s Backpack Handbag Case Study: Knowledge Graphs at eBay
  53. 53. Try it out at: Case Study: Knowledge Graphs at eBay
  54. 54. Product Data Real-time (local) inventory Promotions Routing and delivery Real-time Recommendations Personalization Geo-data Payment options Social Data Available Data from Online Stores
  55. 55. Customer Graph Product Graph Supply Graph Real-time product recommendations Real-time supply chain management Real-time risk mitigation Region Street Customer Address Phone Customer Email Email Address Phone Product Product Category Product Category Store Street Store The Graph Behind Online Stores
  56. 56. Business Problem • Optimize 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 online store • 2M+ employees and $470 billion in annual revenues Walmart RETAIL Real-Time Recommendations58
  57. 57. 59
  58. 58. 60
  59. 59. Request
  60. 60. Thank You and Enjoy the Meeting 63
  61. 61. Neo4j — Changing the World ICIJ used Neo4j to uncover the world’s largest journalistic leak to date, The Panama Papers, exposing criminals, corruption and extensive tax evasion. The US space agency uses Neo4j for their “Lessons Learned” database to connect information to improve search ability effectiveness in space mission. eBay uses Neo4j to enable machine learning through knowledge graphs powering “conversational commerce”. Knowledge Graph for AIFraud Detection Knowledge Graph for humans
  62. 62. Neo4j - The Graph Company 720+ 7/10 12/25 8/10 53K+ 100+ 270+ 450+ Adoption Top Retail Firms Top Financial Firms Top Software Vendors Customers Partners • Creator of the Neo4j Graph Platform • ~200 employees • HQ in Silicon Valley, other offices include London, Munich, Paris and Malmö (Sweden) • $80M in funding from Fidelity, Sunstone, Conor, Creandum, and Greenbridge Capital • Over 10M+ downloads • 270+ enterprise subscription customers with over half with >$1B in revenue Ecosystem Startups in program Enterprise customers Partners Meet up members Events per year Industry’s Largest Dedicated Investment in Graphs
  63. 63. 66 • Record “Cyber Monday” sales • About 35M daily transactions • Each transaction is 3-22 hops • Queries executed in 4ms or less • Replaced IBM Websphere commerce • 300M pricing operations per day • 10x transaction throughput on half the hardware compared to Oracle • Replaced Oracle database • Large postal service with over 500k employees • Neo4j routes 7M+ packages daily at peak, with peaks of 5,000+ routing operations per second. Handling Large Graph Work Loads for Enterprises Real-time promotion recommendations Marriott’s Real-time Pricing Engine Handling Package Routing in Real-Time
  64. 64. Modelling the Supply Chain to Find Counterfeit Products Connected Data analysis reveals hidden relationships, which can aid in identifying counterfeiter supply chains.
  65. 65. Legitimate and Counterfeit Supply Chains are Complex Graph Networks
  66. 66. 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 IMPLEMENTATION AT DHL 69 Real-Time Routing Recommendations
  67. 67. Business Problem • Enable delivery in London within 90 minutes • Manage network of routes, carriers and couriers • Calculate delivery options and times in real time across all possible routes • Scale to enable a variety of services, including same-day and consumer-to-consumer shipping Solution and Benefits • Calculates all possible routes in real time • Thousands of times faster than MySQL solution • Queries require up to 100 times less code, improving time-to-market and code quality • Adding new functionality that was previously impossible Background • eBay acquired London-based Shutl bring same- day delivery to London to counter Amazon Prime and to expand its global retail presence • Founded in 2009, Shutl was the UK leader in same-day delivery with 70% of the market eBay Now RETAIL DELIVERY and SUPPLY CHAIN ROUTING Real-Time Routing70
  68. 68. Graph Platform 71
  69. 69. 72 Graph Platform: Connects to Many Roles in Enterprise DEVELOPERS ADMINS Graph Analytics Graph Transactions DATA ANALYSTS DATA SCIENTISTS APPLICATIONS Drivers & APIs Data Integration BIG DATA IT Analytics Tooling BUSINESS USERS Discovery & Visualization Development & Administration
  70. 70. Graph Visualizations in Neo4j Browser
  71. 71. Guided Analysis 74
  72. 72. Cypher: Powerful and Expressive Query Language MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse) MARRIED_TO Dan Ann NODE RELATIONSHIP TYPE LABEL PROPERTY VARIABLE
  73. 73. Why Cypher is Better Ease of use drives adoption & popularity • Demonstrable maturity and proven success • Huge ecosystem and support network • Visibly represents relationships & paths • Declarative language is easy to learn Cypher is Open, Easy and Everywhere Cypher on Apache Spark (CAPS) Cypher ToolingBI Tools Integration Tools Cypher on … Additional Sources Apache Hadoop Accelerating Market Adoption • openCypher participation is growing • Reference model for ISO, other research projects • SQL compatible and complementary • Released for under friendly Apache license Evolving and Expanding Rapidly Incorporating new ideas for Cypher such as: • Return results as graphs OR tables of data (composability) • compose subqueries and chain-linking query algorithms • build graph expressions • define new graph object types like walks, runs and paths
  74. 74. Finds the optimal path or evaluates route availability and quality • Single Source Short Path • All-Nodes SSP • Parallel paths Evaluates how a group is clustered or partitioned • Label Propagation • Union Find • Strongly Connected Components • Louvain • Triangle-Count Determines the importance of distinct nodes in the network • PageRank • Betweeness • Closeness • Degree Data Science Algorithms