Event Title

Graph database impact on CRM and MDM

Session

Computer Science and Communication Engineering

Description

The NoSQL ('not only SQL') graph database is a technology for data management designed to handle very large sets of structured, semi-structured or unstructured data. Graph database uses graph structures to represent semantic data with nodes, edges and properties. In this paper we study the possibility to use graph technology to better fit a Customer Relationship Management System and a Master Data Management System, using the capabilities of traversing relationships between entities. We explore the benefits of the technology from a scalability point of view (for the so called Big Data), as they are often faster for associative data sets, map more directly and scale more naturally as they don't require expensive join operations. We also approach inherent problems to our choice, namely the relatively difficult aggregation of data. In this paper we finally propose a possible stack in order to create an Operational Big Data CRM, proposing the use of a specific graph database and designing a hybrid solution to overcome the analytical difficulties.

Keywords:

NoSQL, Graph database, CRM, MDM, Big Data, semantic data

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-437-69-1

Location

Pristina, Kosovo

Start Date

27-10-2018 9:00 AM

End Date

27-10-2018 10:30 AM

DOI

10.33107/ubt-ic.2018.93

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Oct 27th, 9:00 AM Oct 27th, 10:30 AM

Graph database impact on CRM and MDM

Pristina, Kosovo

The NoSQL ('not only SQL') graph database is a technology for data management designed to handle very large sets of structured, semi-structured or unstructured data. Graph database uses graph structures to represent semantic data with nodes, edges and properties. In this paper we study the possibility to use graph technology to better fit a Customer Relationship Management System and a Master Data Management System, using the capabilities of traversing relationships between entities. We explore the benefits of the technology from a scalability point of view (for the so called Big Data), as they are often faster for associative data sets, map more directly and scale more naturally as they don't require expensive join operations. We also approach inherent problems to our choice, namely the relatively difficult aggregation of data. In this paper we finally propose a possible stack in order to create an Operational Big Data CRM, proposing the use of a specific graph database and designing a hybrid solution to overcome the analytical difficulties.