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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.
Customer 360 (C360)
Gain a singular, contextual view of the
customer in real-time, for a seamless
customer experience across all
• 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
• access to the Customer 360 is always available?
• How do I expose the relaEonships within our
Customer and Departmental Proﬁles for ﬂexible
API API API
API API API
MarkeEng Website Orders
WebSite Mobile App ReporEng
Data Hub – ESB – Message queue
Website Inventory MarkeEng
Data sEll silo’d
What is a Graph?
Line ItemShipping Address
What is a graph made of?
Line ItemShipping Address
Vertex is roughly equal to an enEty in a tradiEonal world
Added to list
How is that diﬀerent then a RDBMS?
• EnEEes based
• Shallow 1:1 foreign key
• 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
• EnEEes are almost a second
• Flexible data structure
• No tables!
• Specialized Graph query
Comparing to SQL
DataStax is a registered trademark of
DataStax, Inc. and its subsidiaries in the
United States and/or other countries.
Where do we see GraphDBs in the
• Social networks
• Health care
• Fraud DetecEon
• IdenEfy & access managment
Sign up for
Example of C360 – A day in a life of a bank customer
paid her lunch using ABC
bank’s debit card
made purchase at local
ﬂower shop with bank’s
paid credit card bills with her ABC
online banking checking acct
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
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
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.
Betweenness, Wondering, Social inﬂuence.
How are two people
What reviews are most
What path through the
buying journey lead to
the most conversions?
What path through the buying
journey lead to the most
What manufacturing lines lead
to the most defects?
Franchise Product Name: StarWars
Logo: Jar Jar