A STUDY OF DATA QUALITY ISSUES
IN MOBILE TELECOM OPERATORS
Naiem Khodabandhloo Yeganeh and Shazia Sadiq
School of Information Technology and Electrical Engineering
The University of Queensland, St Lucia, QLD 4072, Brisbane, Australia
Keywords: Data Quality, Telecommunication Operators, Case Study.
Abstract: Telecommunication operators currently servicing mobile users world-wide have dramatically increased in
the last few years. Although most of the operators use similar technologies and equipment provided by
world leaders in the field such as Ericsson, Nokia-Siemens, Motorola, etc, it can be observed that many
vendors utilize propriety methods and processes for maintaining network status and collecting statistical
data for detailed monitoring of network elements. This data forms their competitive differentiation and
hence is extremely valuable for the organization. However, in this paper we will demonstrate through a case
study based on a GSM operator in Iran, how this mission critical data can be fraught with serious data
quality problems, leading to diminished capacity to take appropriate action and ultimately achieve customer
satisfaction. We will further present a taxonomy of data quality problems derived from the case study. A
breif survey of reported literature on data quality is presented in the context of the taxonomy, which can not
only be utilized as a framework to classify and understand data quality problems in the telecommunication
domain but can also be used for other domains with similar information systems landscapes.
1 INTRODUCTION
A mobile telecom operator (MTO) is a company that
provides mobile telecommunication services for its
end customers and it usually utilizes a variety of
technologies, vendors, and sub-contractors to
provide such services. Although these different
systems and organization are not designed by MTO,
MTO is ultimately responsible for customer
satisfaction and has to monitor its network precisely
and efficiently. This variety inevitably leads to data
quality problems.
In this paper we present a study of data quality
problems derived from the telecom domain. We
firstly present background literature on data quality
and an overview on problems and solutions in
section 2. In section 3, a taxonomy of data quality
problems for MTO is presented and different real-
world data quality issues in a MTO are studied. In
section 4, an analysis of MTO data quality problems
and data quality solutions is presented. Finally, in
section 5, the outlook of future works with a
conclusion is presented.
2 BACKGROUND LITERATURE
Consequents of poor quality of data have been
experienced in almost all domains. From the
research perspective, data quality has been addressed
in different contexts, including statistics,
management science, and computer science
(Scannapieco, 2005). To understand the concept,
various research works have defined a number of
quality dimensions (Scannapieco, 2005), (Wang,
1995), (Fox, 1994).
Data quality dimensions characterize data
properties. Many dimensions are defined for
assessment of quality of data that give us the means
to measure the quality of data as a value. These
dimensions can be grouped in four categories (Fox,
1994), (Scannapieco, 2005): accuracy, currency,
completeness, and consistency.
A variety of solutions are proposed for data quality
problem. These solutions can be categorized into
groups regardless of the domain they are applied on:
Semantic integrity constraints are used for
maintaining data integrity. Integrity constraints can
be used for preventing database from loosing quality
as well as repairing quality problems.
Record linkage solutions are used to detect
duplicate record which are not always identical in
441
Khodabandhloo Yeganeh N. and Sadiq S. (2008).
A STUDY OF DATA QUALITY ISSUES IN MOBILE TELECOM OPERATORS.
In Proceedings of the Tenth International Conference on Enterprise Information Systems - DISI, pages 441-444
DOI: 10.5220/0001689904410444
Copyright
c
 SciTePress

value, but represent the same entity. For example,
same name can be written in two different formats,
but might identify the same person or a SMS can be
modified during the distribution from one to one, but
conveying the same meaning.
Data lineage or provenance solutions are
classified as annotation and non-annotation based
approaches where back tracing is suggested to
address auditory or reliability problems.
Schema mapping solutions are designed to map
schemas between different data sources or views.
See (Rahm, 2000) where issues and approaches of
database integration are studied taking schema
mapping requirements into consideration.
Data uncertainty and completeness are
important unsolved problems in databases and it is
important to efficiently integrate models of
uncertainty in database systems (Silberschatz, 1991).
3 TAXONOMY OF DATA
QUALITY ISSUES FOR MTO
MTO data has specific requirements due to its
nature, but many data quality dimensions as
described in the previous section apply to this field
generally. Below, we provide some examples of how
various data quality dimensions can be found in
MTO data.
Figure 1 shows the weekly average of the daily
peak hours of the difference in the number of traffic
channel assignments reported by the two major
components of a GSM network: radio and core, at
the same time from the same vendor. We see an
average 2% to 5% difference. This difference will
decrease call setup success rate and answer seizure
rate which will have bad effects on customer
satisfaction and the operator’s income.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
18/07/2007 20/07/2007 22/07/2007 24/07/2007 26/07/2007 28/07/2007 30/07/2007
1/08/2007 3/08/2007 5/08/2007 7/08/2007 9/08/2007
11/08/2007 13/08/2007 15/08/2007 17/08/2007 19/08/2007 21/08/2007 23/08/2007 25/08/2007
Diffbe tweenCAin INand MSC(%)
Figure 1: Difference between call attempts reported by IN
versus MSC.
Different parts of a telecom network are
independently designed and managed, but they co-
operate as a single system. This independence
causes inconsistency problems in database. For
example, it is likely to have a name mismatch
problem between different elements of the network.
For example, you need to know that Trunk Group
with name TGRP_BSC28 is going to the Base
Station Controller named BSC_QAZVIN for the city
of Qazvin and these names should be linked
together.
Data lineage is also an important problem in
such databases; for example, analysis of statistics
may show a sharp change in some indicators at a
past time where engineers or managers may need to
know what was changed, who changed it, and why it
was changed.
Below, we categorize MTO data quality
problems into four major categories to constitute
taxonomy for MTO specific data quality issues:
Lack of Consistency. Inconsistency emerges
when same measurements from different parts of the
network don’t mach. This difference can have
several reasons including; different vendors,
unknown errors, inexact time synchronization
between different elements or other technical
reasons.
Lack of Sufficient Error Recognition.
Sometimes all of the errors are not calculated and
thus, the values do not match. For example, there is
a rule that the number of incoming calls to a switch
should be equal to the number of outgoing calls plus
the number of rejected, congested or overloaded
calls. However our study shows marked differences
due to uncounted rejected calls. This difference is an
effect of data incompleteness.
Lack of Standardization. There is still no
global standard for data interaction by different
networks and it is not always possible to map
counters and measurements from different vendors,
e.g. to calculate the number of Failed Random
Access Channels in a network consisting of both
Alcatel and Siemens radio equipments).
Lack of Synchronization. Time related issues
frequently occur in MTO data, e.g. when the
microwave link is disconnected temporarily, the
number of active traffic channels might report a fake
traffic. This issue is a direct effect of using out-of-
date data in the system.
4 RELATIONSHIP WITH
REPORTED LITERATURE
Although, all data quality solutions provided may
not be directly or practically applicable for industry
ICEIS 2008 - International Conference on Enterprise Information Systems
442

use, in the discussion below we extract relevant
techniques from literature and establish their
applicability for MTO data quality requirements as
developed through our case study.
Table 1 and Table 2 show relations between data
quality problems in a MTO and traditional data
quality domain in brief. Table 2 infers that data
quality issues discussed for MTOs are present in
other domains and solutions proposed therein can be
applied in the context of MTOs. The numbers in
cells correspond to references as below:
1. (Bohannon, 2007), (Cong, 2007)
2. (Elmagarmid, 2007), (Gravano, 2001), (Tejada,
2001) (Scanappieco, 2007), (Dasu, 2002),
(Rahm, 2000), (Miller, 2000)
3. (Buneman, 2002), (Bhagwat, 2004)
(Srivastava, 2007)
4. (Scanappieco, 2007), (Dasu, 2002), (Batani,
1986), (Rahm, 2000), (Miller, 2000)
5. (Silberschatz, 1991), (Thone, 1995)
6. (Ballou, 2003), (Chomicki, 2005) ,(Churces,
2004), (Al-lawati, 2005), (Freedman, 2004)
Lack of Consistency. (Ballou, 2003) provides a
model to calculate trade off between consistency and
completeness of data. Works such as join estimation
are also useful for improving the consistency of data.
(Chomicki, 2005) (Elmagarmid, 2007) , (Churces,
2004) , (Al-lawati, 2005) and (Freedman, 2004)
proposes some methods for record-linkage. For a
complete survey of record linkage see (Elmgarmid,
2007). These techniques can be used to find
inconsistencies in statistical data. (Bohannon,
2007), propose and utilize a concept called
conditional functional dependencies that can be
useful to maintain MTO database consistency.
Lack of Sufficient Error Recognition. A
closely associated aspect of error recognition is error
tracking. Backtracking for data lineage discussed in
(Buneman, 2002), (Bhagwat, 2004), and (Srivastava,
2007) can be applied to determine if a changed
record is truly dirty or the change is deliberate.
Lack of Standardization. Some automatic
standardization can be defined within the database
system, e.g. methodology presented in (Dasu, 2002)
which automatically collects schema model of the
database and extracts dependencies can be useful.
Techniques for similarity analysis (Elmagarmid,
2007), approximate joins (Gravano, 2001) and
mapping tables (Tejada, 2001) can assist here.
Lack of Synchronization. Currency, age and
timeliness of data are key characteristics of the data.
(Bullou, 1985) provides a model for data quality
which supports data expiration. (Pernici, 2002)
presents a methodological framework for
information systems where currentness of data is
important.
5 OUTLOOK
The number and diversity of mobile
telecommunication operators world-wide has led to
a highly competitive industry. A major contributing
factor towards achieving the above goal is the
quality of the data upon which both operations as
well as the business decisions rely on.
We have conducted a case study of a GSM
operator and studied in detail how data quality issues
emerge in the various activities of the organization.
Through this study we have derived a taxonomy of
data quality problems for MTOs. The taxonomy
provides a forum to analyse and relate the domain
specific problem for MTOs to the wider data quality
problem and solution space.
We would like to remark that the data quality
problems mentioned in the previous sections do not
represent the entire spectrum of data quality issues
for MTOs. The nature of these organizations and
their sensitivity to the quality problems was
Table1: Relation between traditional DQ problems and
MTO data quality problems.
MTO
DQ Problem
Dimension
(i) (ii) (iii) (iv)
Currency/
Timeliness
2
Accuracy/
Reliability
1 3 2
Completeness
5
Consistency
6 4
Table 2: Relation between DQ solutions and MTO data
quality problems.
MTO
DQ Problem
Solution
(i) (ii) (iii) (iv)
Semantic
Integrity
1 1 1
Record
Linkage
6 2
Data
Lineage
3
Schema mapping
4
Certainty
Completeness
5
(i) Lack of Consistency; (ii) Lack of Error Recognition;
(iii) Lack of Standardization; (iv) Lack of Synchronization.
A STUDY OF DATA QUALITY ISSUES IN MOBILE TELECOM OPERATORS
443

demonstrated by examples which usually occur in
the radio, core and statistics databases. The
operations of MTOs have several other aspects
which were not considered in our study.
Through this study, we hope that data quality
problems for MTOs in specific and for the
telecommunication industry in general can be better
understood. We also envisage that this study can
provide the roadmap for industry relevant research
on data quality.
ACKNOWLEDGEMENTS
The authors wish to acknowledge the help of the
unnamed MTO through which this case study was
developed
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