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The Connected Data Imperative: Why Graphs? at Neo4j GraphDay New York City

Philip Rathle, VP of Product at Neo4j, presents on the Connected Data Imperative at Neo4j GraphDay NYC

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The Connected Data Imperative: Why Graphs? at Neo4j GraphDay New York City

  1. 1. NEW YORK CITY April 18, 2017 09:00-09:30 09:30-10:15 10:15-11:00 11:00-11:30 11:30-12:30 12:30-13:30 13:30-17:00 Breakfast and Registration The Connected Data Imperative: Why Graphs Transform Your Data: A worked example Break Enterprise Ready: A Look at 
 Neo4j in Production Lunch Training Session Agenda
  2. 2. 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%
  3. 3. The Connected Data Imperative: Neo4j in the Enterprise SOFTWARE FINANCIAL SERVICES RETAIL MEDIA & OTHER SOCIAL NETWORKS TELECOM HEALTHCARE
  4. 4. Takeaways for Today 1. Where graphs databases fit into your existing IT portfolio 2. What are others doing with graphs, particularly in 
 Financial Services? 3. How can you use graphs to advance your own business
  5. 5. Latency & Freshness Function of your technology Batch- Precompute Real-Time Connectedness Function of your data & question Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid) Evolutions in Data Processing
  6. 6. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid) Evolutions in Data Processing
  7. 7. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid) Evolutions in Data Processing Phase I: “Data”
  8. 8. Data Management in 1979 Paper Forms Tiny RAM Spinning Platters (Low Capacity / Sequential IO)
  9. 9. The RDBMS Era (1979 - Present)
  10. 10. Key-Value, Column-Family, Document Database Aggregate-Oriented* NoSQL DBMSs The NoSQL Era (Circa 2009 - Present) Source: Martin Fowler, NoSQL Distilled https://martinfowler.com/books/nosql.html
  11. 11. Evolutions in Data Processing Phase II: “Information” Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid) RDBMS & Aggregate- Oriented NoSQL
  12. 12. Hordes of Data Hoardes of Data Data Management Circa 2005
  13. 13. Trending & Aggregation Finding Needles in Haystacks Data Management Circa 2005
  14. 14. Data Management Circa 2005 Commodity Server Farms Cheap & Abundant Storage
  15. 15. The Hadoop Era (2005 - Present)
  16. 16. Evolutions in Data Phase III: Data Relationships Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid) RDBMS & Aggregate- Oriented NoSQL Hadoop / MapReduce
  17. 17. The internet Data Management in 2017
  18. 18. Data Management in 2017
  19. 19. Data Management in 2017
  20. 20. Data Management in 2017 Dynamic Real-World Systems Abundant RAM SSD/Flash (High-Capacity Storage & Ultra-Fast Random I/O)
  21. 21. The Graph Era (Now & the Future)
  22. 22. The Graph Era (Now & the Future)
  23. 23. Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid) RDBMS & Aggregate- Oriented NoSQL Hadoop / MapReduce |<————————- Graph Database & ————————>| Graph Compute Engine Connected DataDiscrete Data A View of the Data Management Portfolio
  24. 24. Latency & Freshness (Function of your technology) Batch- Precompute Real-Time
  25. 25. Insight Action Data Professionals Direct Access to Data Customer + Employeers + Autonomous Devices Access via Applications “Data Warehousing/ Analytic/OLAP/Off-Line” “Real-Time / Transactional/ Operational/OLTP” Another View of the Data Management Portfolio Systems of Insight vs. Action
  26. 26. Data Professionals Direct Access to Data Customer + Employeers + Autonomous Devices Access via Applications “Data Warehousing/ Analytic/OLAP/Off-Line” “Real-Time / Transactional/ Operational/OLTP” Another View of the Data Management Portfolio Systems of Insight vs. Action
  27. 27. Real-Time Processing Recommendations based on activity from yesterday Batch Processing Overnight/Intermittent Loading and Calculations Results in lag between activity & knowledge response System-wide local pre-calculations are computationally inefficient Real-Time Writes & Writes Up-to-the-moment freshness “Just-in-time” processing most efficient for “local” queries Recommendations that reflects your latest activity Another View of the Data Management Portfolio Systems of Insight vs. Action
  28. 28. Latency & Freshness Batch- Precompute Real-Time Connectedness Illustration by David Somerville based on the original by Hugh McLeod (@gapingvoid) Neo4j Solves Connected, Real-Time Problems
  29. 29. UNIQUE ADVANTAGES OF A NATIVE GRAPH DATABASE
  30. 30. Intuitiveness Speed Agility
  31. 31. 33 A unified view for ultimate agility • Easily understood • Easily evolved • Easy collaboration between business and IT #1 Benefit: Project Agility
 The Whiteboard Model Is the Physical Model
  32. 32. Connectedness and Size of Data Set ResponseTime Relational and Other NoSQL Databases 0 to 2 hops 0 to 3 degrees Thousands of connections 1000x Advantage Tens to hundreds of hops Thousands of degrees Billions of connections Neo4j “Minutes to milliseconds” #2 Benefit:
 “Minutes to Milliseconds” Real-Time Query Performance
  33. 33. “We found Neo4j to be literally thousands of times faster than our prior MySQL solution, with queries that require 10-100 times less code. Today, Neo4j provides eBay with functionality that was previously impossible.” - Volker Pacher, Senior Developer “Minutes to milliseconds” performance Queries up to 1000x faster than RDBMS or other NoSQL #3 Benefit:
 “Minutes to Milliseconds” Real-Time Query Performance
  34. 34. At Write Time: data is connected as it is stored At Read Time: Lightning-fast retrieval of data and relationships via pointer chasing Index free adjacency Key Ingredient #1 of 3:
 Graph Optimized Memory & Storage
  35. 35. 37 Example HR Query in SQL The Same Query using Cypher MATCH (boss)-[:MANAGES*0..3]->(sub), (sub)-[:MANAGES*1..3]->(report) WHERE boss.name = “John Doe” RETURN sub.name AS Subordinate, 
 count(report) AS Total Project Impact Less time writing queries • More time understanding the answers • Leaving time to ask the next question Less time debugging queries: • More time writing the next piece of code • Improved quality of overall code base Code that’s easier to read: • Faster ramp-up for new project members • Improved maintainability & troubleshooting Key Ingredient #2 of 3:
 A Productive and Powerful Graph Query Language
  36. 36. Graph Transactions Over ACID Consistency Graph Transactions Over Non-ACID DBMSs 38 Maintains Integrity Over Time Becomes Corrupt Over Time Key Ingredient #3 of 3:
 ACID Graph Writes
  37. 37. VALUE FROM GRAPHS IN FINANCIAL SERVICES
  38. 38. Asset Graph 1 Customer Graph2 Payment Graph3Master Data Graph 4 Entitlement Graph 5 THE FIVE GRAPHS OF FINANCE
  39. 39. #1 Asset Graph
  40. 40. Bond z HAS_INTEREST Hedge Fund Mutual Fund Stock OWNS OWNS Stock ETF OWNS OWNS Stock ISSUES HAS Options ON ISSUES COMPANY HAS O W NS #1 Asset Graph
  41. 41. #1 Asset Graph
  42. 42. #1 Asset Graph
  43. 43. Impact analysis Portfolio analytics Risk assessment Trading Dynamic Pricing Key Applications Asset Graph – Key Values Example Neo4j-customers
  44. 44. #2 Customer Graph
  45. 45. Manager Research VP Dallas Director United States Central Region CEO North America Strategy ANALYST Wholesale Banking Texas #2 Customer Graph
  46. 46. Upsell/Cross-Sell Customer Targeting Sales Operations Human Capital Management Key Applications Customer Graph – Key Values Example Neo4j-industries Manufacturing Health CareFinance Telecom Retail Human Resources
  47. 47. m #3 Payment Graph
  48. 48. #3 Payment Graph SMB SMB SMB SMB SMB SMB SMB SMB Am ount: $18,000 Transactions: 10 Amount: $22,000 Transactions: 200 SOLD_TO SOLD_TO SOLD_TO Amount: $32,000Transactions: 170 Am ount: $22,000 Transactions: 200 SO LD_TO SMB SMB SMB Amount: $8,000 Transactions: 14 SOLD_TOAmount: $24,000Transactions: 11 SOLD_TO Amount: $17,000 Transactions: 300 SOLD_TO Am ount: $11,000 Transactions: 199 SOLD_TO Amount:$15,000 Transactions:10 SOLD_TOAmount:$15,000 Transactions:10 SOLD_TO
  49. 49. A Graph of Money Laundering #3 Payment Graph
  50. 50. INVESTIGATE Revolving Debt Number of Accounts INVESTIGATE Normal behavior Fraud Detection with Discrete Analysis Pros Stops rookies Cons False positives False negatives
  51. 51. Revolving Debt Number of Accounts Normal behavior Fraudulent pattern Fraud Detection with Connected Analysis Pros Detects fraud rings Reduces false negatives
  52. 52. Government Example Neo4j-customers Offers & Recommendations Network Effects Chargebacks Anti Fraud / Money Laundering Credit Risk Key Applications Payment Graph – Key Values
  53. 53. #4 Master Data Graph
  54. 54. Systems Planning, Impact Analysis, Data Governance, Micro-Services Enterprise Architecture | System of Systems #4 Master Data Graph
  55. 55. Extracts from “Graph databases for exploring metadata” by Jeremy Ponser #4 Master Data Graph Enterprise Metadata Graphs
  56. 56. #4 Master Data Graph VP Staff Staff StaffStaff DirectorStaffDirector Manager Manager Manager Manager Fiber Link Fiber Link Fiber Link Ocean Cable Switch Switch Router Router Service • Organizational Structures including sales territories, reporting structures, geography • Product Structures including product & feature hierarchies, time dimension • Network Inventories including configuration management, physical and logistics networks Enterprise Hierarchies
  57. 57. Example Neo4j-customers 360° View of the Customer Packaging & Product Bundling Recommendations Human Capital Management Key Applications Master Data Graph – Key Values
  58. 58. #5 Entitlement Graph
  59. 59. GraphConnect 2013: https://vimeo.com/76821847 Using Graph Databases in Real-Time to Solve Resource Authorization at Telenor
  60. 60. GraphConnect 2013: https://vimeo.com/76821847 Using Graph Databases in Real-Time to Solve Resource Authorization at Telenor
  61. 61. GraphConnect 2013: https://vimeo.com/76821847 Using Graph Databases in Real-Time to Solve Resource Authorization at Telenor
  62. 62. Compliance Faster onboarding Real-time provisioning Real-time deprovisioning The Value Entitlement Graph – Key Values
  63. 63. “Graph analysis is possibly the single most effective competitive differentiator for organizations pursuing data-driven operations and decisions after the design of data capture.” “By the end of 2018, 70% of leading organizations will have one or more pilot or proof-of-concept efforts underway utilizing graph databases.” Towards Graph Inevitability
  64. 64. #5 Entitlement Graph#4 Master Data Graph #3 Payment Graph#2 Customer Graph#1 Asset Graph The Five Graphs of Finance
  65. 65. NEW YORK CITY April 18, 2017 09:00-09:30 09:30-10:15 10:15-11:00 11:00-11:30 11:30-12:30 12:30-13:30 13:30-17:00 Breakfast and Registration The Connected Data Imperative: Why Graphs Transform Your Data: A worked example Break Enterprise Ready: A Look at 
 Neo4j in Production Lunch Training Session Agenda
  66. 66. Key Analytics Patterns HDFS/MapReduce/Spark (Storage & Aggregation) Streaming (Filtering & Aggregation) Machine LearningGraph Computation

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