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Keynote: Graphs in Government_Lance Walter, CMO

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Keynote: Graphs in Government_Lance Walter, CMO

  1. 1. Lance Walter, CMO, Neo4j Neo4j Graphs in Government Day March 5, 2020
  2. 2. Welcome! #graphtour #neo4j
  3. 3. Strictly ConfidentialStrictly Confidential • Agenda Review • Overview: Connected data and graphs • Market landscape • Neo4j Introduction • Case studies • What’s next in graphs? • Wrap-up Topics
  4. 4. Today’s Agenda
  5. 5. Networks of People Business Processes Knowledge Networks E.g., Risk management, Citizen Service, Payments E.g., Employees, Citizens, Suppliers, Partners, Influencers E.g., Enterprise content, Domain specific content, eCommerce content Data connections are increasing as rapidly as data volumes The Rise of Connections in Data
  6. 6. Graphs have been universally recognized as a great solution for specific types of problems - Graph Problems - and recognition is GROWING!
  7. 7. Look at this data… Element Depends On A B A C A D C H D J E F E G F J G L H I J N J M L M
  8. 8. Element Depends On A B A C A D C H D J E F E G F J G L H I J N J M L M Time challenge #1: What if J fails? ?
  9. 9. Look at this data again…
  10. 10. If your business problem has a lot of dependencies - which in IT / database terms are represented by JOINs between different entities - and if solving for these dependencies in near real time is important to you, then your problem is probably easiest solved with graph technology - and we can safely call it a GRAPH PROBLEM. Identifying Graph Problems
  11. 11. 2.6 TB 11.5 million documents Emails, Scanned Documents, Bank Statements etc… The Graph Problem at Scale: Panama Papers
  12. 12. Common Graph Use Cases Fraud Detection Real-Time Recommendations Network & IT Operations Master Data Management Knowledge Graph Identity & Access Management airbnb
  13. 13. DB-engines Ranking of Database Categories Graph DB 2013 2014 2015 2016 2017 2018 2019 • Graph DBMS • Key-value stores • Document stores • Wide column store • RDF stores • Time stores • Native XML DBMS • Object oriented DBMS • Multivalue DBMS • Relational DBMS Popularity of Graphs
  14. 14. Strictly Confidential 16 Graph is a Top Technology Trend for 2020
  15. 15. “Choice (from 3:00 to 6:00), during which the DBMS technology asset class typically moves from adolescent status into the early mainstream. This is the phase of highest growth in demand (market penetration potentially reaches 50%), during which supply options should grow…” 17 Graphs in the Early Mainstream
  16. 16. Making Graph Database Market History: An Emerging Open Standard
  17. 17. Neo4j Company Update
  18. 18. A Vibrant Growing Community
  19. 19. A Vibrant Growing Community 50% 1000+ Sign ups for Startup Program
  20. 20. A Vibrant Growing Community 60+Events 6Continents Global Graph Day
  21. 21. A Vibrant Growing Community r.neo4j.com/twin4j
  22. 22. Neo4j is one of the Fastest Growing Skills
  23. 23. 76%FORTUNE 100 have adopted or are piloting Neo4jFinance 20 of top 25 7 of top 10 Software Retail 7 of top 10 Airline s 3 of top 5 Logistic s 3 of top 5 Telco 4 of top 5 Hospitalit y 3 of top 5 Growing Adoption in the Enterprise
  24. 24. Case Studies
  25. 25. Background • US IT consulting firm helped US Army streamline equipment deployments and maintenance spending • Saving lives by improving the operational readiness of Army equipment like tanks, radios, transports, aircraft, weaponry, etc. Business Problem • Needed to modernize procurement, budget and logistics processes for equipment & spare parts • Millions of connections among a tank’s bill-of- materials, for example • Improve “what if” cost calculations when planning missions and troop deployments • Mainframe systems required over 60 man-hrs to calculate changes… planning took too long. Solution and Benefits • 118M nodes & 185M relationships • Shed cost estimation times by 88% • Improved parts delivery timing and accuracy • DBA labor required dropped by 77% • Equipment TCO more predictable • Safer soldiers US Army / Calibre Systems Equipment Logistics Parts Assembly & Equipment Maintenance28
  26. 26. Background • The MITRE Corporation is a federally-funded, not- for-profit company that manages cybersecurity for seven national research and development laboratories around the United States including the Center for National Security • Founded in 1958, engaged in numerous public- private partnerships as well as independent research Problem • Constantly-evolving networks – devices, configurations • Huge volumes of “noise” from virus warnings to failed logins • Isolated datapoints with no context to separate the most serious threats from the benign • Existing database could not provide the context or performance to manage a real-time environment Solution and Benefits • CyGraph - Agencies now have scalable, comprehensive analytic and visualization capabilities • Allowed agencies to capture a picture of their cybersecurity environment that connects previously isolated data points Mitre Cybersecurity for Federal Agencies 29 “CyGraph’s comprehensive knowledge base tells a much more complete story than that of basic attack graphs or mission dependency models. [It] includes potential attack-pattern relationships that fill in gaps between known vulnerabilities and threat indicators.” - Steven Noel, Principal Cybersecurity Engineer
  27. 27. Background • Social network of 10M graphic artists • Peer-to-peer evaluation of art and works-in-progress • Job sourcing site for creatives • Massive, millions of updates (reads & writes) to Activity Feed • 150 Mongos to 48 Cassandras to 3 Neo4j’s! Business Problem • Artists subscribe, appreciate and curate “galleries” of works of their own and from other artists • Activities Feed is how everyone receives updates • 1st implementation was 150 MongoDB instances • 2nd implementation shrunk to 48 Cassandras, but it was still too slow and required heavy IT overhead Solution and Benefits • 3rd implementation shrunk to 3 Neo4j instances • Saved over $500k in annual AWS fees • Reduced data footprint from 50TB to 40GB • Significantly easier to introduce new features like, “New projects in you Network” Adobe Behance Social Network of 10M Graphic Artists Social Network30 EE Customer since 2016 Q
  28. 28. Background • Over 7M citizens suffer from Diabetes • Connecting over 400 researchers • Incorporates over 50 databases, 100k’s of Excel workbooks, 30 database of biological samples • Sought to examine disease from as many angles as possible. Business Problem • Genes are connected by proteins or to metabolites, and patients are connected with their diets, etc… • Needed to improve the utilization of immensely technical data • Needed to cater to doctors and researchers with simple navigation, communication and connections of the graph. Solution and Benefits • Dr. Alexander Jarasch, Head of Bioinformatics and Data Management • Scientists can conduct parallel research without asking the same questions or repeating tests • Built views like a liver sample knowledge graph DZD - German Center for Diabetes Research Medical Genomic Research31 EE Customer since 2016 Q
  29. 29. Looking Forward: AI and Graphs
  31. 31. • For AI to be more situationally appropriate & "learn" in a way that leverages adjacency to understand and refine outputs, it needs to be underpinned by context. • AI standards that don't explicitly include contextual information result in subpar outcomes • Narrowly focused and rigid AI, uninterpretable predictions, and less accountability • Graph technologies are a state-of-the-art, purpose-built method for adding and leveraging context from data. • Graph technology is a powerful foundation for AI 34 Neo4j Response to NIST Call for Information on AI Standards https://www.nist.gov/system/files/docu ments/2019/06/03/nist-ai-rfi- neo4j_001.pdf
  32. 32. Neo4j 4.0
  33. 33. By Developers For Developers 80+% of our customers start as individual developers I’ve been playing with @neo4j today and I must say, even though I’ve only about scratched the surface of it, I love it. And about Cypher: I can’t believe how much sense it makes and how easy I got started writing queries. Plus the Desktop app was a very pleasant experience. After learning Neo4j, it’s amazing to me how much better a graph data structure suits real-world relationship models. Bye bye tricky SQL joins! Good god @neo4j's intro console with the movie example is literally the best database engine intro I have ever seen <applause>
  34. 34. The Six Pillars of the Neo4j Database
  35. 35. What do you want?
  36. 36. Here's what we've heard Build Faster Scale Bigger Launch EasierBe More Secure
  37. 37. What’s New in Neo4j 4.0 Easy Management Unlimited Scale Granular security Fast to develop
  38. 38. What’s New in Neo4j 4.0 Multi-DatabaseNeo4j Fabric Schema-Based Security Reactive Database
  39. 39. Wrap Up
  40. 40. Thank You to Our Sponsors!
  41. 41. #neo4j@lancewalter #neo4j