Graph in Customer 360 - StampedeCon Big Data Conference 2017
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Enterprises typically have many data silos of partial customer data and a common theme in big data projects to use big data tools and pipelines to unify all siloed customer data into a single, queryable, platform for improving all future customer interactions. This data often comes from billing, website traffic, logistics, and marketing; all in different formats with different properties. Graph provides a way to unify all of the data into a single place for use in tracking the flow of a user through the various silos. Graph can also be used for visualizations and analytics that are difficult in other systems. In this talk we will explore the ways in which Graph can be leveraged in a customer 360 use case. What it can add to a more conventional system and what the approach to developing a graph based Customer 360 system should be.
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Graph in Customer 360 - StampedeCon Big Data Conference 2017
- 2. What we will cover • What is a Customer 360 • Journey to C360 • What is graph? • How Graph can help your C360
- 4. Customer 360 (C360) Gain a singular, contextual view of the customer in real-time, for a seamless customer experience across all touchpoints.
- 5. Challenges • How can I ensure our full customer profile is current and comprehensive? • How can I provide easy access to the current and comprehensive customer profiles to many different consumers? • access to the Customer 360 is always available? • How do I expose the relaEonships within our Customer and Departmental Profiles for flexible exploraEon?
- 7. Next step Finance & Billing Customer Support MarkeEng Website Mobile App Orders API API API API API API ApplicaEon
- 8. Stream processing Finance & billing Customer Support MarkeEng Website Orders Customer 360 API WebSite Mobile App ReporEng Data Hub – ESB – Message queue Mobile App Website Inventory MarkeEng Finance & Billing Customer Support
- 10. What is a Graph? COUNTRY ADDRESS CUSTOMER Rated ORDER PRODUCT Line ItemShipping Address Billing Address Address TAG Resides Purchased Purchased Has Belongs To Ships To COUNTRY ADDRESS CUSTOMER Rated ORDER PRODUCT LineItem ShippingAddress BillingAddress Address TAG
- 11. What is a graph made of? COUNTRY ADDRESS CUSTOMER Rated ORDER PRODUCT Line ItemShipping Address Billing Address Address TAG Resides Purchased Purchased Has Belongs To Ships To COUNTRY ADDRESS CUSTOMER Rated ORDER PRODUCT LineItem ShippingAddress BillingAddress Address TAG Vertex Egde Property
- 19. How is that different then a RDBMS? RDBMS Graph • EnEEes based • Shallow 1:1 foreign key relaEonships • RelaEonship following down mainly by JOINS and UNIONS at query Eme • Rigid data structure • Table based • SQL Query language • All about the relaEonships. • Deep, complex relaEonship structure • EnEEes are almost a second class ciEzen. • Flexible data structure • No tables! • Specialized Graph query language.
- 22. DSE Graph – Data Access | Traversals • Query Access => Apache TinkerPop Gremlin • Traversal style data navigaEon Find all orders purchased by Lisa • g.V().has('customer', 'name', 'Lisa').out('ordered').values('number') • Find all products purchased by Lisa’s Friends • g.V().has('customer', 'name', 'Lisa').outE('related').has('Type','friend').inV().out('ordered').out('purchased').val ues('name’) • RecommendaEon - find all products purchased by Lisa’s Friends, not purchased by Lisa • g.V().has('customer', 'name', 'Lisa').as('customer').out('ordered').out('purchased').aggregate('produc ts').in('purchased').in('ordered').where(neq('customer')).out('ordered'). out('purchased').where(without('products')).values('name') © 2016 DataStax, All Rights Reserved. 22 1 Customer Name:[Lisa] Age:[32] 2 Order Number:[1234] 5 Product Name:[Socks] Size: [XL] 4 Customer Name:[Frank] Age:[28] 6 Product Name:[Shirt] Size: [XL] 7 Address Stree:[123 West Street] Zip Code:[44534] 11 12 13 14 15 16 17 3 Tag Type:[Color] Value: [White] 18 19 orders Date:[1/1/2016] related Type:Friend resides Since:1/1/2000 ships Shipment Date:1/2/2016 purchased Qty: 42 purchased Qty: 1 has Valid: 1/1/2012 has Valid: 1/1/2012
- 23. Where do we see GraphDBs in the wild? • Social networks • logisEcs • Manufacturing • Health care • Fraud DetecEon • RecommendaEons • Networking • IdenEfy & access managment • C360
- 26. Example of C360 – A day in a life of a bank customer 2 6 paid her lunch using ABC bank’s debit card 11am 9am made purchase at local flower shop with bank’s credit card 3pm paid credit card bills with her ABC online banking checking acct 4:15p m 6pm Customer of Bank ABC with mulEple accounts and cards Profile: 68 yrs old, female, Florida resident, no young family members, customer since 1995 A transacEon was made using the customer’s credit card showing purchase of motorcycle in Aruba customer paid her grocery at Safeway with check from the ABC bank real Eme alert was triggered due to unusual item and locaEon purchase. Bank’s Fraud Department sent a text msg to the customer to verify the purchase, and freezed the card customer got text msg from the bank, called customer service rep in her local branch. The rep was already aware of the issue and verified the motorcycle purchase was a fraud. The bank invalidated customer’s credit card and issued a new card. 4:18p m