Advanced

Merging customer relationship management data

Lönnqvist Gustafsson, Fredrik LU (2016) In LU-CS-EX 2016-29 EDA920 20152
Department of Computer Science
Abstract
Working distributed is increasingly important today with technology being
more and more portable while connectivity is still lacking in some areas.
Cellphones and laptops are increasingly in use outside the office with
connectivity to internal office services being unreliable, either due to
lacking speed or inability to have a connection.

Different solutions exist. One is a client-server setup where the data
traffic can be minimized. Another is an internet service where the client
is in the web browser. A third is to use a distributed system where each
system is has full functionality by its own but needs synchronizing with other
copies of the system. Distributed work requires duplication of data and
therefore, also a need to... (More)
Working distributed is increasingly important today with technology being
more and more portable while connectivity is still lacking in some areas.
Cellphones and laptops are increasingly in use outside the office with
connectivity to internal office services being unreliable, either due to
lacking speed or inability to have a connection.

Different solutions exist. One is a client-server setup where the data
traffic can be minimized. Another is an internet service where the client
is in the web browser. A third is to use a distributed system where each
system is has full functionality by its own but needs synchronizing with other
copies of the system. Distributed work requires duplication of data and
therefore, also a need to merge data.

Great efforts have been made to be able to
merge program code in text files and this kind of merge solutions are pretty
mature. However, for merging other types of data the methods are still young and
unproved.

For a CRM system, the data is highly structured and different data fields are
known in advance. This gives good opportunities to merge diverged data. However
the current systems on the market are lacking support for merge algorithm
knowing about the data format.

Even if the data is structured it doesn't mean that a merge is trivial.
Structured data might have dependencies between different data fields, so
that one field depends on the value of another field. That means that a
merge tool needs to be aware of these dependencies. If also taking the
history of changes since the diverging point into account the possibilities
for detecting merge problems is greatly increased.

Different CRM systems have used different approaches to merging data,
however, all found approaches have been without actually merging any data,
but instead either identify a difference or guessing the most correct
version without merging. That means that a result will be either one of two
conflicting changes but not a combination of the two.

This paper has studied different use cases for altering CRM data and how
three different types of CRM programs solve these cases. A custom merge
algorithm that solves all studied cases has been designed and implemented
as a proof of concept.

The proof of concept implementation works satisfying. It uses the
history and the structure of the data, combined with information about
dependencies between different data fields to identify conflicts and perform a
merge. Merge is only done when it can be done without any risk of loosing information.

The conclusion is that current CRM software handles these problems poorly
and that it is possible to merge this data and/or detect conflicts in a good way. (Less)
Popular Abstract (Swedish)
Många företag använder ett kundhanteringssystem för att registrera interaktioner med sina kunder. I en allt mer mobil värld vill användarna av dessa system kunna använda systemen var de än befinner sig men oförändrad prestanda och funktionalitet.
Please use this url to cite or link to this publication:
author
Lönnqvist Gustafsson, Fredrik LU
supervisor
organization
alternative title
Sammanslagning av kundhanteringsdata
course
EDA920 20152
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
CRM, merge, supporting distributed workflow, merging structural data in CRM systems, distributed, distributed CRM, customer relationship management, customer relationship data, merging CRM data
publication/series
LU-CS-EX 2016-29
report number
LU-CS-EX 2016-29
ISSN
1650-2884
language
English
id
8889895
date added to LUP
2016-08-30 13:22:53
date last changed
2016-08-30 13:22:53
@misc{8889895,
  abstract     = {Working distributed is increasingly important today with technology being
more and more portable while connectivity is still lacking in some areas.
Cellphones and laptops are increasingly in use outside the office with
connectivity to internal office services being unreliable, either due to
lacking speed or inability to have a connection.

Different solutions exist. One is a client-server setup where the data
traffic can be minimized. Another is an internet service where the client
is in the web browser. A third is to use a distributed system where each
system is has full functionality by its own but needs synchronizing with other
copies of the system. Distributed work requires duplication of data and
therefore, also a need to merge data.

Great efforts have been made to be able to
merge program code in text files and this kind of merge solutions are pretty
mature. However, for merging other types of data the methods are still young and
unproved.

For a CRM system, the data is highly structured and different data fields are
known in advance. This gives good opportunities to merge diverged data. However
the current systems on the market are lacking support for merge algorithm
knowing about the data format.

Even if the data is structured it doesn't mean that a merge is trivial.
Structured data might have dependencies between different data fields, so
that one field depends on the value of another field. That means that a
merge tool needs to be aware of these dependencies. If also taking the
history of changes since the diverging point into account the possibilities
for detecting merge problems is greatly increased.

Different CRM systems have used different approaches to merging data,
however, all found approaches have been without actually merging any data,
but instead either identify a difference or guessing the most correct
version without merging. That means that a result will be either one of two
conflicting changes but not a combination of the two.

This paper has studied different use cases for altering CRM data and how
three different types of CRM programs solve these cases. A custom merge
algorithm that solves all studied cases has been designed and implemented
as a proof of concept.

The proof of concept implementation works satisfying. It uses the
history and the structure of the data, combined with information about
dependencies between different data fields to identify conflicts and perform a
merge. Merge is only done when it can be done without any risk of loosing information.

The conclusion is that current CRM software handles these problems poorly
and that it is possible to merge this data and/or detect conflicts in a good way.},
  author       = {Lönnqvist Gustafsson, Fredrik},
  issn         = {1650-2884},
  keyword      = {CRM,merge,supporting distributed workflow,merging structural data in CRM systems,distributed,distributed CRM,customer relationship management,customer relationship data,merging CRM data},
  language     = {eng},
  note         = {Student Paper},
  series       = {LU-CS-EX 2016-29},
  title        = {Merging customer relationship management data},
  year         = {2016},
}