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Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and Analytics

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We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down.

Connected data is the key to your business succeeding and growing in today’s connected world.

Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.

Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide:
- Increased visibility between internal groups
- Efficiency gains
- Cross-functional data collaboration
- Core complete and reliable business insights
- Better customer engagement

The live presentation and discussion can be found here: https://youtu.be/7vBdlXzhs_4

Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connected-data-is-more-useful/

Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com

Published in: Data & Analytics
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Knowledge Graphs - Journey to the Connected Enterprise - Data Strategy and Analytics

  1. 1. Knowledge  Graphs Journey  of  the  Connected  Enterprise   Benjamin  Nussbaum   @bennussbaum  |  ben@atomrain.com   www.atomrain.com  |  www.graphgrid.com  
  2. 2. In  the  Past…  Leonhard  Euler  1707-­‐1783
  3. 3. These  are  graphs…
  4. 4. More  Recently… •  The  understanding  and  discovery  that  comes  through  connec@ons  is   something  that  organiza@ons  like  Google  (Knowledge  Graph)  and   Facebook  (Social  Graph)  have  leveraged  very  well  for  over  a  decade.   •  We  owe  the  rising  popularity  and  availability  of  the  general  purpose   graph  databases  today  to  the  pioneers  of  the  space,  Neo  Technology,   the  makers  of  Neo4j  the  one  truly  produc@on  ready  na@ve  graph   database  with  over  15  years  of  history.   •  Now  every  organiza@on  can  have  a  knowledge  graph.  
  5. 5. IntuiDve   Speed   Agility
  6. 6. Data  used  to  be  stored  like  this…
  7. 7. Then  we  started  storing  it  like  this…
  8. 8. Now  we  can  store  data  like  this…
  9. 9. But  why  does  this  make  sense?
  10. 10. Because  your  data  really  IS  connected  like  this
  11. 11. Graph  Thinking:  IdenDty  &  Access  Management
  12. 12. Graph  Thinking:  Graph  Based  Search
  13. 13. Graph  Thinking:  Master  Data  Management
  14. 14. Graph  Thinking:  Fraud  DetecDon
  15. 15. Graph  Thinking:  Network  &  IT  OperaDons
  16. 16. Graph  Thinking:  Cyber  Threat  DetecDon
  17. 17. Graph  Thinking:  Unlocking  Understanding Your  Data  Connected  is  Your  Knowledge  Graph  
  18. 18. What  are  the  benefits  of  a  knowledge  graph? •  You’re  interac@ng  with  your  data  in  its  true  form   •  Everyone  can  understand  the  data  design  and  organiza@on   •  Developers  get  more  done  in  less  @me   •  Your  organiza@on’s  data  is  connected  across  all  silos   •  Understanding  the  connec@ons  is  now  possible  
  19. 19. Improved  Data  Understanding  and  InteracDon JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN JOIN
  20. 20. Improved  Data  Understanding  and  InteracDon
  21. 21. Improved  Cross-­‐FuncDonal  CollaboraDon Graphs  Connect  Not  Only  Your  Data  But  Your  Whole  Organiza=on  
  22. 22. Improved  Developer  ProducDvity “Complex  Join”  in  SQL   opencypher.org  –  Na@ve  Query  Language  for  Graphs   SQL  Query  vs  Na@ve  Graph  Query  (Cypher)     Equivalent  queries  for  finding  the  repor@ng   chain  within  an  organiza@on  
  23. 23. Key  Components  of  a  NaDve  Graph  Database •  Nodes  –  The  “things”  in  your  data.   •  Rela@onships  –  The  context  of  how  two  “things”  are  related.    They   are  treated  as  first  class  en@@es.   •  Labels  –  Think  of  these  as  tags  used  to  organize  Nodes.  A  Node  can   have  mul@ple  labels  applied  to  it.   •  Proper@es  –  Both  Nodes  and  Rela=onships  have  Proper@es.  These   store  a^ributes  of  the  Node/Rela@onship.  
  24. 24. Key  Components  of  a  NaDve  Graph  Database
  25. 25. Key  Components  of  a  NaDve  Graph  Database
  26. 26. Key  Components  of  a  NaDve  Graph  Database
  27. 27. How  do  we  go  from  Big  Data  to  Smart  Data?
  28. 28. Graph  Thinking  is  a  Paradigm  ShiY
  29. 29. And  no  paradigm  has  ever  made  more  sense •  This  is  how  the  brain  works  –  dealing  with  “things”  and  how  they’re   related  is  already  how  we’re  wired  to  func@on   •  Solve  more  complex  problems  with  less  effort   •  Improved  collabora@on  between  technical  teams  and  everyone  else   •  Flexibility  to  evolve  your  data  naturally  as  your  business  changes  
  30. 30. But  a  New  Paradigm  Requires  a  New  Engine Na=ve  Graph  Database   •  Op@mized  for  graph  traversal   •  Rela@onships  are  first  class   •  Referen@al  integrity  guaranteed   •  ACID  Compliant  &  Transac@onal   Non-­‐Na=ve  Graph  Database   •  SQL,  Document,  Tabular,  Key   Value,  etc  database  engine  with   an  abstrac@on  layer  that   provides  “graphy”  interac@ons.   •  Not  sympathe@c  to  the  nature  of   reading  and  wri@ng  connected   data.  
  31. 31. Using  a  non-­‐naDve  graph  is  like  keeping  your   old  dirt  bike  engine  for  your  new  race  car
  32. 32. Choose  your  Engine  carefully Na=ve  Graph  Database   •  Neo4j   Non-­‐Na=ve  &  Not  Graph  Databases   •  DataStax   •  Elas@c   •  IBM   •  FlockDB  –  edge  cache   •  Tinkerpop  –  compute  framework   •  RDF  –  specifica@on  
  33. 33. The  leading  naDve  graph  database  engine •  Neo4j  is  the  world’s  leading  na@ve  graph  database   •  Neo4j  is  op@mized  for  connected  data  opera@ons   •  Neo4j  is  my  go  to  for  connected  data  solu@ons  in  the  enterprise   •  Neo4j  is  used  by  the  world’s  leading  enterprises   •  Neo4j  will  accelerate  your  knowledge  graph  ini@a@ves  
  34. 34. IntegraDng  with  Your  ExisDng  Architecture •  Very  low-­‐risk,  non-­‐invasive  opera@on   •  Create  connectors  for  exis@ng  data  bases   •  Flow  data  into  your  knowledge  graph   •  Real-­‐@me,  analy@cs,  learning,  understanding,  etc  applica@ons  interact   with  the  na@ve  graph  database  directly   •  Start  flowing  new  data  directly  into  your  knowledge  graph  (assuming   you  chose  one  that  is  ACID  and  guarantees  referen@al  integrity)  
  35. 35. Non-­‐Invasive  Architecture  OpDon Data Storage and Business Rules Execu5on Data Mining and Aggrega5on Applica'on Graph Database Cluster Neo4j Neo4j Neo4j Ad Hoc Analysis Bulk Analy'c Infrastructure Hadoop, EDW … Data Scien'st End User Databases Rela5onal NoSQL Hadoop
  36. 36. Where  do  I  go  from  here? •  Embrace  the  paradigm  shih  –  go  na@ve.   •  Embrace  the  paradigm  shih  –  get  connected.   •  Embrace  the  paradigm  shih  –  work  intui@vely.   •  Embrace  the  paradigm  shih  –  transform  your  organiza@on.  
  37. 37. Thank  You! Knowledge  Graphs:  Journey  of  the  Connected  Enterprise   Benjamin  Nussbaum   @bennussbaum  |  ben@atomrain.com   www.atomrain.com  |  www.graphgrid.com  

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