DEFINING THE INTRINSIC DATA QUALITY

FOR WEB PORTALS

Carmen Moraga

, Angélica Caro

, Mª Ángeles Moraga

, Rodrigo Romo Muñoz

and Coral Calero

Alarcos Research Group-Institute of Information Technologies & Systems, University of Castilla-La Mancha,

Paseo de la Universidad 4, Ciudad Real, Spain

Department of Computer Science and Information Technologies, University of Bio Bio, Chillán, Chile

Department of Business Management, University of Bio Bio, Chillán, Chile

Keywords: Data Quality Characteristics, Web Portal, Data Quality Model.

Abstract: Web portals facilitate access to data on the Internet. Their use and quantity have increased, which has led to

the need to satisfy user preferences in order for them to continue on the market. One competition-based

strategy is to provide an adequate quality of service, and one of the key factors in this is the quality of the

data provided by the Web portal. Bearing this in mind, we have defined a Data Quality (DQ) Model for

Web portals. This model is composed of 42 DQ characteristics which are organized into two points of view

and four DQ categories. In this paper we present work which is based on the statistical study of users’

opinions of one of the characteristics in the model – the Intrinsic DQ category. Our objective is to determine

which characteristics in the Intrinsic category will be most relevant as regards the users’ gender, age range,

level of studies and knowledge of computing, thus allowing us to verify that, according to the

aforementioned parameters, the importance of some characteristics varies with regard to others. For

example, women place more importance upon Credibility, while men view the Accessibility of the data in

Web Portals as being more important.

1 INTRODUCTION

Web portals have been consolidated as an

appropriate means to organize and facilitate access

to data on the Internet.

As more and more organizations use this means

as a way in which to capture followers or

‘customers’, the competition between them also

grows. One basic competition strategy is to provide

a quality customer service (Yang et al., 2004), and

one of the important factors involved in this is data

quality. In the relevant literature, the concept of

Information Quality or Data Quality (hereafter DQ)

is often defined as ‘‘fitness for use’’, i.e., the ability

of a collection of data to meet user requirements

(Cappiello et al., 2004; Strong et al., 1997). Many

DQ models have been proposed in the last few

years, and a standard was even proposed in 2008

(ISO/IEC-FDIS-25012, 2008). However, very few

works dealing with the DQ in the context of Web

portals have appeared. Examples of the few which

have are: (Caro et al., 2008; Grigoroudis et al., 2008;

Metzger, 2007; Yang et al., 2004).

Our research interest is focused on the creation

of a DQ model for Web portals.

In a previous work, we identified a set of DQ

characteristics that were organized into four

categories (Intrinsic, Operational, Contextual and

Representational), and which were divided into two

points of view (Inherent and System Dependent).

These constitute the SPDQM (SQuaRE-Aligned

Portal Data Quality Model).

In order to make SPDQM usable and applicable,

we have defined two different approaches: Static and

Dynamic. The former is oriented towards

determining those characteristics that are relevant to

certain types of Web portal users according to their

various demographic aspects. That is to say, its

intention is to determine which quality

characteristics are most important with regard to the

users’ gender, age range, level of studies, etc. The

latter is oriented towards obtaining the level of DQ

that exists in each Web portal, at a specific moment,

for a given context.

In this paper we present the results of the

empirical work carried out for the Static approach in

the Intrinsic DQ category of SPDQM.

The remainder of this paper is organized as

374

Moraga C., Caro A., Moraga M., Romo Muñoz R. and Calero C..

DEFINING THE INTRINSIC DATA QUALITY FOR WEB PORTALS.

DOI: 10.5220/0003929603740379

In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 374-379

ISBN: 978-989-8565-08-2

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

follows. Section 2 describes our model SPQDM

which was obtained. Section 3 presents the Static

and Dynamic approaches. Section 4 focuses on the

Static approach towards Intrinsic DQ. In Section 5,

the results are shown, and our conclusions and future

work are presented in Section 6.

2 SPDQM

Web portals are increasingly more important.

However, insufficient attention has been paid to the

data quality in them. A Portal Data Quality model

has therefore been created to deal with this lack

(SPDQM). SPDQM is based on three different

elements: PDQM (Portal Data Quality Model) (Caro

et al., 2008), a set of DQ characteristics obtained

from the relevant literature (Moraga et al., 2009 a)

and the ISO/IEC 25012 standard. SPDQM will allow

the DQ level in a Web Portal to be discovered, and

will guide designers and developers in discovering

the most relevant DQ characteristics according to the

type of user, which will be determined by various

demographic aspects.

Each of the elements used to create our model

can be found in (Moraga et al., 2009 b).

2.1 DQ Characteristics for SPDQM

SPDQM contains a set of 42 DQ characteristics that

are appropriate to Web portals. The first level

corresponds with the two points of view adopted

from the ISO/IEC 25012 standard (see Figure 1a).

The second level corresponds with the DQ

categories adopted from the PDQM model (Caro et

al., 2008) (see Figure 1b):

(a) Intrinsic, which denotes that data have quality

in their own right; (b) Operational, which emphasizes

the importance of the role of systems, that is, the

system must be accessible but secure; (c)

Contextual, which highlights the requirement which

states that DQ must be considered within the context

of the task in hand; and (d) Representational, which

denotes that the system must present data in such a way

that they are interpretable, easy to understand, and

concisely and consistently represented, in a specific

context.

The third level corresponds with the set of

characteristics in each category (see Figure 1c). Based

on the definitions of the PDQM’s categories and the

ISO/IEC 25012’s points of view (see Figure 1a and

Figure 1b). Finally, the fourth level contains the sub-

characteristics of the characteristics in the previous

level (see Figure 1d).

Once SPDQM was defined, the next step was to

define how this model could be applied in practice,

such that it would be possible to determine the set of

DQ characteristics that would be most relevant as

regards the user perspective.

This would also assist us in the evaluation and/or

improvement of the level of DQ in a Web portal.

The following section describes the two

approaches that we were defined in order to apply

SPDQM.

3 TWO APPROACHES WITH

WHICH TO ADDRESS THE DQ

IN WEB PORTALS

This section shows the two approaches used to apply

Figure 1: The structure of SPDQM model.

(a) (b) (c) (d)

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375

our model.

The Static approach will be carried out only once

for each category (Intrinsic and Contextual). This

approach consisted of determining the set of

characteristics that are most relevant as regards the

different types of Web portal users. These types of

users will be determined according to different

demographic aspects. These demographic aspects

are: gender, age range, level of studies, etc,

according to the category that will be analysed. The

results obtained will be used to create a tool that will

allow to guide designers and developers in what the

relevant characteristics are for each type of Web

portal user. These guidelines will also indicate

criteria with which to satisfy user preferences.

A set of measures for each of the characteristics

in the Representational and Operational categories

will be defined in the Dynamic approach. The

objective of this approach is to obtain the level of

DQ of each Web portal at a specific moment. The

results will vary depending on the Web portal that is

being analyzed, and it will not be possible to

generalize the results of the analysis. A tool which

automates the measures will be also available for

this approach. This tool will use the URL in a Web

portal to obtain the level of DQ for the characteristics

in these categories. This evaluation will then be used

as a basis to provide its designers and developers

with recommendations for its improvement.

The steps followed in each approach are shown

as follows (see Figure 2).

The following steps will be carried out in the

Static approach:

• An initial set of questions will be created in

relation to the definitions of the characteristics in

each category, and to the demographic aspects.

• These questions will be reviewed by a group of

experts in statistical analysis.

• The questions will be modified with the feedback

obtained from the experts.

• A pilot survey will be sent to a trial group who

will fill in the questionnaire. This group will be

composed of Web portal users, and will inform us

whether the questions are understandable, or

whether they will have to be improved.

• The questions in the pilot survey will be reviewed,

and the feedback from the trial group will be

taken into consideration.

• A questionnaire containing the final set of

questions will be created. The questions must be

written in such a way that they are understandable

to any Web portal user.

• The questionnaires will be distributed, either in

printed format or via e-mail, to a heterogeneous

group of Web portal users.

• The questionnaires will be collected either by

hand or by e-mail.

• The results obtained will be analysed, and any

questionnaires containing erroneous data or with

questions that had not been answered will be

discarded.

• A statistical analysis will be carried out to

determine the most relevant DQ characteristics

according to the types of users. This will be done by

o First: The mean value of each characteristic

is obtained in order to verify whether all the

characteristics are important from the point

of view of user preference.

o Second: A correlation analysis is carried out

to verify whether there is a relationship

Figure 2: The structure of SPDQM model.

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between the category itself and the

characteristics associated with that category.

This is to reinforce the idea that these

characteristics are truly correctly associated

with their.

o Third: The set of characteristics is used to

create homogeneous groups of

characteristics (denominated as factors).

These groups are formed of those

characteristics which have a considerable

amount of correlation with each other. We

also attempt to ensure that each group is

independent of the others.

o Finally: User groups (denominated as

clusters) are created, which are related to the

factors. Each user group is formed of

different types of users, according to the

demographic aspects. The DQ characteristics

which are most important to different types

of users are therefore grouped together.

• Guidelines containing recommendations for Web

portal designers and developers will be created,

which would allow them to discover user

preferences.

The process used in the case of the Dynamic

approach will be as follows:

• A set of measures will be defined for each

characteristic.

• The level of quality desired for each characteristic

will be determined.

• Recommendations by which to improve the level

of data quality in said Web portal will be

generated for each DQ characteristic.

• The measures will be automated through the

creation of a computerised tool.

• The tool will be made available for direct use by

any interested parties in specific Web portals.

In this paper we shall show the application of the

Static approach to the Intrinsic DQ category.

4 THE INTRINSIC DQ IN WEB

PORTALS

In this section we shall use the Static approach in the

Intrinsic DQ category (see Figure 1). The questionnaire

in this survey was made up of a total of 20

questions, 16 of which were related to the DQ

characteristics in the category (also including a

question concerning the definition of the term

‘Intrinsic’), and the other 4 of which were related to

demographic aspects. We used closed questions, in

which only one response was possible.

The 16 questions were created using a 5-point

Likert scale, where 1 is “Not at all important” and 5

is “Very important”.

The questions related to demographic aspects, on

the other hand, allowed us to define the different

types of users. These types of users were obtained

according to their gender, age range, level of studies

and knowledge of computing.

The questionnaire was of the unsupervised type,

and was distributed to a heterogeneous group of 150

Web portal users. The results of the questionnaires

are analysed in the following section.

5 ANALYSIS OF THE RESULTS

This section shows the study of the results obtained

from the questionnaires. Of a total of the 137

questionnaires received, the data from 136 were

analysed, since the questions relating to the

demographic aspects had not been answered in one

of them. This study was carried out by using an

SPSS statistical analysis tool.

The objective of our study is, on the one hand to

determine whether all the DQ characteristics in the

Intrinsic DQ category are important for Web portal

users and, on the other, to analyse whether some

characteristics are more relevant than others

according to the different types of users.

As a starting point, it was necessary to estimate

the reliability of the results. This was done by

calculating the Cronbach’s alpha. The result

obtained from this was a value of 0.856, which

indicated that the results had good internal

consistence. The information is therefore reliable.

A descriptive statistical analysis was then carried

out in order to determine whether all the DQ

characteristics are important to Web portal users.

This analysis allowed us to obtain the central tendency

(mean) and the dispersion (typical deviation) for all the

variables in the study. The variables correspond with

the characteristics that are included in the Intrinsic

category, along with the Intrinsic DQ category itself.

As a result of this, we observed that the mean value,

of all the characteristics, is approximately four. As

additional data, we obtained that the characteristics

Credibility, Accessibility, Reputation, Consistency

and Currentness have a higher mean value. The only

characteristic that was below the mean value of four

was that of Traceability. Although the value for this

category was below four, it had a mean of 3.96,

which is very close to four, and it is therefore also

considered to be important.

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This proximity to the value of four indicates that

all the characteristics in the Intrinsic DQ category

are, in effect, important to Web portal users.

In order to reinforce the previous idea, we

carried out a correlation analysis between the DQ

characteristics (which were considered as dependent

variables) and the Intrinsic DQ (independent

variable). The method used was the Spearman

correlation analysis. The results showed that the

characteristics are related to the Intrinsic DQ

category at an extremely high level.

Having proved that all the characteristics are

important and that there is a correlation with the

Intrinsic DQ, the next step was to analyse whether

some characteristics are more important than others

with regard to the different types of users. This was

done by carrying out a factorial analysis and a cluster

analysis, which are detailed as follows.

5.1 Factorial Analysis

At this point, we created groups of characteristics.

Each group was independent of the others and was

denominated as a factor. This would allow us to later

relate each of these factors to the different types of

users.

Table 1 shows the characteristics that correspond

with each of the factors obtained.

Table 1: Characteristics in each factor

Factor Characteristics

Factor 1

Compliance, Confidentiality, Currentness,

Understandability, Efficiency, Completeness,

Precision

Factor 2

Reputation, Objectivity, Credibility, Accuracy,

Consistency

Factor 3 Traceability, Accessibility and Expiration

These three factors combined explain 52.9% of

the total variability (which is confirmed by KMO,

which has a value of 0.804). Factor 1 accounted for

35.1% of the total variance. Factor 2 accounted for

10.4% of the total variance, and Factor 3 accounted

for 7.4%.

All of the DQ characteristics have now been

placed in groups. However, our eventual intention

was to discover whether there is more relevance

between some characteristics and others according

to the different types of users. The next step,

therefore, was to group these factors by carrying out

a cluster analysis whose purpose is shown as

follows.

5.2 Cluster Analysis

The results obtained in the factorial analysis were

then used to carry out a cluster analysis in order to

group the factors by resemblance or similitude. The

factors were organised into two clusters, since this is

the minimum number of clusters that will allow each

factor to be differentiated in a single cluster. In the

first cluster, those characteristics located in Factors 1

and 2 are positively valued, whilst cluster 2

positively values the characteristics in Factor 3.

Finally, the clusters were related to the

demographic aspects. In this way, it was obtained

that Cluster 1 contains users who are principally

female, under 25 or over 45, with a basic level of

studies or with vocational training, and with a basic or

intermediate knowledge of computing. The designers

and developers aimed at this type of users should,

therefore, bear in mind the characteristics contained

in Factors 1 and 2.

The users in Cluster 2 are principally male,

between 25 and 45 years of age with vocational

training or university studies and with an advanced

knowledge of computing. The designers and

developers of Web portal aimed at this type of user

should therefore bear in mind the characteristics in

Factor 3.

The results obtained have been summarized in a

table (Table 2) in which it will be observed that all

the users consider the characteristics in the Intrinsic

DQ category to be relevant (R). It will also be noted

that some of these characteristics have been allotted

a higher value (R+) and others have been allotted a

lower value (R-). Each of the clusters, later gave a

greater value to different subsets of characteristics in

the Intrinsic DQ category (R+).

Table 2: Relevant characteristics.

Intrinsic

Characteristics

Users in

General

Cluster 1 Cluster 2

Compliance R R+

Traceability R- R+

Reputation R+ R+

Objectivity R R+

Credibility R+ R+

Accuracy R R+

Consistency R+ R+

Accessibility R+ R+

Confidentiality R R+

Currentness R+ R+

Expiration R R+

Understandability R R+

Efficiency R R+

Completeness R R+

Precision R R+

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6 CONCLUSIONS AND FUTURE

WORK

This paper presents a data quality model for Web

portals, denominated as SPDQM.

The theoretical definition of the model served as

a basis for the definition of two approaches which

can be used in the evaluation of the DQ in Web

portals. One was a static approach whose objective

is to determine those DQ characteristics which are

most important for Web portal users. The other was

a dynamic approach whose objective is to obtain the

DQ level of the data contained in a specific portal at

a determined moment. All of this should enable

designers and developers to be guided in the

construction of portals, in order to make them more

appropriate for the users at which they are aimed.

In this paper, we have presented our proposal for

the development of the static approach in the

Intrinsic DQ category. To do this, we carried out a

survey and then analysed the results obtained from

the questionnaire by using the SPSS statistical tool.

The results obtained have allowed us to determine

that all the characteristics are important, and to

demonstrate that some characteristics are, in effect,

more relevant than others according to the different

types of users, who are determined by the following

demographic aspects: gender, age range, level of

studies and knowledge of computing. Finally, we

have indicated the set of characteristics to which

more attention should be paid in the DQ of a Web

portal according to the user towards whom it is aimed.

Our next step will be to create support guidelines

which will be available via a computing tool.

Our short-term future work will be to carry out

the static approach for the Contextual characteristics.

In the long term we shall develop the dynamic

approach for the remaining categories. Our eventual

intention is to make our model available to users and

developers through a free tool.

ACKNOWLEDGEMENTS

This research has been funded by the following

projects: ORIGIN (CDTI-MICINN and FEDER IDI-

2010043(1-5)), PEGASO/MAGO project

(Ministerio de Ciencia e Innovacion and Fondo

Europeo de Desarrollo Regional, TIN2009-13718-

C02-01), EECCOO (MICINN TRA2009_0074), and

VILMA (JCCM PEII 11-0316-2878).

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