An Extended Data Object-driven Approach to Data Quality

Evaluation: Contextual Data Quality Analysis

Anastasija Nikiforova

and Janis Bicevskis

Faculty of Computing, University of Latvia, 19 Raina Blvd., Riga, Latvia

Keywords: Data Quality, Contextual Control, Data Object, Executable Models, Domain-specific Modelling Language.

Abstract: This research is an extension of a data object-driven approach to data quality evaluation allowing to analyse

data object quality in scope of multiple data objects. Previously presented approach was used to analyse one

particular data object, mainly focusing on syntactic analysis. It means that the primary data object quality can

be analysed against secondary data objects of unlimited number. This opportunity allows making more

comprehensive, in-depth contextual data object analysis. The given analysis was applied to open data sets,

making comparison between previously obtained results and results of application of the extended approach,

underlying importance and benefits of the given extension.

1 INTRODUCTION

Data quality has been a topical issue for many

decades, and it has a growing importance nowadays

for several reasons: (a) in the 21st century data is

everywhere, according to (Economist, 2017) “the

world’s most valuable resource is no longer oil, but

data”; (b) open data popularity increases extremely

fast – a large number of open data portals and data

sets have become available all over the world.

According to (OpenDataSoft, 2019), there are

more than 2600 open data portals around the world

already and these numbers continuously increase

every day.

The increasing amount of data triggered the data

quality issue. The most significant aspect of data

quality is its impact on decision-making, thus the use

of low-quality data may cause misleading decisions.

Usually the open data are processed without implying

any quality checks. However, statistics on losses

inflicted by data quality problems are frustrating.

According to (Gartner, 2013) and (Gartner, 2018), the

use of the poor data quality results in $15 million to

$3.1 trillion dollars of losses on the US Economy per

year.

In previous research (Bicevskis et al., 2018b) the

initial version of data object-driven approach to data

https://orcid.org/0000-0002-0532-3488

https://orcid.org/0000-0001-5298-9859

quality evaluation was proposed. It consists of 3

components: data object, quality specification,

quality measuring process. Data quality evaluation

for the specific purpose requires description of the

requirements for data values that should be

executable, since the stored data are “scanned” and

checked for compliance with the requirements. The

proposed solution requires the development of a two-

level data quality assurance procedure:

(1) syntactic control – values of data objects are

checked locally within one record (compliance of

input data with the syntax);

(2) semantic/ contextual control – checking

whether the newly entered data is compatible with the

previously entered data that is stored in database. The

semantic control should be repeated every time when

entering or editing the new values of data objects’

attributes.

Syntactic control was already described in

(Bicevskis et al., 2018a, 2018b), (Nikiforova, 2018a).

However, as data quality analysis was made in scope

of one data object and the focus lied on analysis of

syntax, it wasn’t complete and deep enough. In this

paper the presented approach was generalized and

used for semantic data quality analysis. Unlike the

syntactic quality analysis, the semantic quality allows

defining the context of other/external/secondary data

objects used to analyse the primary object.

274

Nikiforova, A. and Bicevskis, J.

An Extended Data Object-driven Approach to Data Quality Evaluation: Contextual Data Quality Analysis.

DOI: 10.5220/0007838602740281

In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 274-281

ISBN: 978-989-758-372-8

The paper deals with the following issues: a short

overview of the related researches (Section 2), a

rationale for the proposed solution (Section 3), a

description of the proposed solution (Section 4), an

application of the proposed solution to open data sets

and comparative evaluation with previous results

(Section 5), conclusions and future work (Section 6).

2 RELATED RESEARCH

Previous papers of the authors (Nikiforova, 2018a)

and (Bicevskis et al., 2018a) provide an overview of

20 different data quality solutions that were proposed

in last decade, emphasizing their pros and cons, so

this research will not explain and discuss them in

detail. In addition, in (Batini et al., 2016) and (Batini

et al., 2009) a comprehensive overview of

methodologies for data quality assessment from

different perspectives is provided.

One of the most traditional concepts of data

quality analysis that is used by the most part of the

existing researches is data quality dimension. The

most popular and commonly used among existing sets

of data quality dimensions are (a) Wang’s and

Strong’s 15 data quality dimensions representing four

categories: intrinsic, contextual, representational,

accessibility; (Wang and Strong, 1996), (b) Redman’s

3 categories of data quality dimensions such as

conceptual schema, data values and data format

(Redman, 1997), (Batini et al., 2016), and (c) one of

the most modern - the 6 data quality dimensions

defined by Data Management Association

International UK Working Group (DAMA, 2019).

However, there is no consensus on data quality

dimensions and their usability. According to (Batini

et al., 2016) “dimensions are not defined in a

measurable and formal way”, other authors are of the

same opinion (DAMA, 2019), (Huang et al., 1999),

(Eppler, 2006). According to (DAMA, 2019), “even

amongst data quality professionals the key data

quality dimensions are not universally agreed. This

state of affairs has led to much confusion within the

data quality community and is even more bewildering

for those who are new to the discipline and more

importantly to business stakeholders”.

Despite the fact that many data quality

dimensions and their groupings have already been

proposed by well-known researchers, the most of

nowadays data quality researches are focused on

finding of new, “more comprehensive” dimensions.

One of such examples is the PoDQA project (Caro et

al., 2007) - a quite promising data quality model for

Web portals that uses 30 data quality attributes.

Moreover, many solutions propose using data

quality dimensions of the same name but with

different semantics and vice versa – different names

for the same dimensions (Batini et al., 2016).

Existing researches intend that the data quality

requirements (defined by a user or already existing

ones) are assigned to appropriate dimensions to be

further applied for data sets. But it leads to the

necessity to involve data quality experts at every

stage of data quality analysis process as very deep and

specific knowledges of data quality and data quality

dimensions are required. However, the presented data

object-driven approach eliminates this very specific,

time-consuming and in most cases confusing step,

using more comprehensive concept “data quality

requirement”. Quality requirements are defined for

the specific data object without their assigning to the

specific data quality dimension and group, as the data

quality is a relative concept that depends on the use-

case. For instance, if a user exploits the data set

“Company Register” identifying company by its

name, registration number and incorporation date, he

might not be interested in information about

representative persons of the company. However,

another user may use the same data set to contact the

company through its representative person, in this

case the data of the company’s representants could be

of big importance but incorporation date could be

completely useless. It means the same data set can be

of high quality for one user and completely useless

(of low quality) for another. The proposed approach

recommends defining the data object which quality

will be analysed first. The data quality requirements

are defined depending on the specific use-case for the

previously defined data object.

To sum up, there are no well-known data quality

solutions which would allow non-data quality staff to

analyse “foreign”/ “external” data sets without any

information about how data was initially collected

and processed. The proposed solution is expected to

provide possibility to analyse data sets according to

the needs of users, i.e., to specific use-cases.

3 RATIONALE FOR THE

RESEARCH

With the popularity of open data, the need for quality

testing is also increasing. In accordance with the

nature of open data, its data quality analysis should be

simple enough for users without advanced IT and data

quality knowledges. In order to ensure users with

such possibility, very intuitive data object-driven

An Extended Data Object-driven Approach to Data Quality Evaluation: Contextual Data Quality Analysis

275

approach to the data quality evaluation was

previously presented in (Bicevskis et al., 2018a). It

was designed to analyse parameters of one data

object, mainly focusing on syntactic analysis.

According to the experience of its application to

multiple data sets (Bicevskis et al., 2018b),

(Nikiforova, 2018a, 2018b), the most common checks

were focused on: (1) existence of values, (2)

relevance to the specified data type, (3) relevance to

the list of enumerable values, (4) validity of value (for

example, credible date).

This paper considers the analysis of data object’s

quality in the context of multiple data objects out of

scope. The necessity for data quality model in the

context of multiple data objects is obvious as

provided by the data quality analysis in (Bicevskis et

al., 2018a). In previous research the data quality of

Company Registers of four European countries was

analysed. Two the most obvious use-cases were

chosen: (a) identifying company by name,

registration number and incorporation date; (b)

contacting company via post using its address and

postal code. Despite the very simple use-case the

results of the data quality analysis were surprising –

many data quality problems were detected. However,

in many cases the single data object analysis indicated

the mere existence of the data quality problem

without detecting all the defective records.

For instance, analysis of Companies House of the

United Kingdom (Companies House, 2018) showed

significant number of data quality deficits in the fields

storing information on country. More in-depth

analysis was required involving the context of another

data object containing information on country

(conforming standards).

The proposed extension is also valuable analysing

data sets of .json and .xml formats, as these formats

can be treated as many interconnected tables, where

number of tables depends on the document structure.

Contextual checks are often implemented in

relational databases and IS (for instance, if the record

already exists). They usually are checked after or

together with syntactic checks. However, in other

cases such as non-relational databases the quality

checks are difficult to test and to manage. Users

without having access to data storing, accruing and

processing procedures, cannot check the data quality.

To solve this problem, these checks (same as

syntactic checks in the initial version of the presented

approach) are separated from program code.

The creation of data quality models within the

context of multiple data objects allows making more

comprehensive in-depth data quality analysis.

4 DATA QUALITY MODEL

The proposed data object-oriented approach covers 3

of 4 data quality control phases proposed by Total

Data Quality Management (TDQM, 2019): (1) data

quality definition, (2) data quality measuring, and (3)

data quality analysis. The 4

phase - data quality

improvement – is left to users or data publishers as

there exist many useful and user-friendly solutions for

this purpose. As stated before (Bicevskis et al.,

2018b), there are three main components forming the

proposed data quality model: (a) data object that

specifies the data which quality will be evaluated, (b)

quality requirements that define conditions that must

be met to admit data as qualitative, and (c) quality

evaluation process that defines procedures should be

performed to evaluate the data object’s quality.

All three components are defined by using a

graphical domain specific language (DSL). Three

DSL families were developed as graphic languages

(Nikiforova, 2018a) based on the possibilities of the

modelling platform DIMOD (Bicevska et al., 2017).

DIMOD is advised to be used instead of specific data

objects definition language as it offers a possibility to

define DSLs with different data objects structures.

Requirements for data quality syntaxis are logical

conditions on data fields of the primary data objects.

Requirements for data quality semantics are defined

by logical conditions on data fields of the secondary

data object. Therefore, both syntactical and

semantical data quality can be analysed according to

unified description principles.

One of the advantages of the proposed approach

is that diagrams are easy to create, edit and read.

Graphical diagrams (a) make the data quality analysis

process easier and well-understandable to non-IT and

non-data quality experts, (b) support interaction

between users (between business- and technical-level

units, different persons or even teams within an

organization or many organizations if needed) as

more than one person can be involved in the process.

According to the proposed approach, the language

describing the quality evaluation process involves

verification activities for individual data objects that

can be defined in several ways: (a) informally as a

natural language text, (b) using UML activity

diagrams, or (c) in the own DSL.

More detailed definition of each component is

provided in the next Section. Two examples explain

main concepts, changes and improvements in

comparison with previously defined (Bicevskis et al.,

2018b): (a) data quality analysis in scope of one data

set for The Register of Enterprises of the Republic of

Latvia (2019); (b) data quality analysis in context of

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

276

multiple data objects and data sets for Companies

House (Company House, 2018).

4.1 Primary and Secondary Data

Objects

The first and probably one of the most important

concepts of the presented approach is a “data object”.

As previously stated in (Bicevskis et al., 2018b), “a

data object is the set of values of the parameters that

characterize a real-life object”. It means that (a) data

object stores only the data the particular user is

interested in, (b) every analysis can have different

attribute names and types. Figure 1 shows a single

data object “Enterprise_LV” with its attributes that

are necessary in specific use-cases (with just few

additional attributes) to get an overview of Enterprise

of enterprise, Name – name of enterprise, Type – type

of enterprise etc. The description of data object at this

stage is informal - no rules for attribute values syntax

are given.

Figure 1: Single data object “Enterprise_LV”.

Figure 2 demonstrates a data object in the context

of multiple data objects. In this case, one object

becomes the primary and other objects are secondary.

The primary data object is the initial data object

which quality is analysed. The secondary data object

determines the context for analysis of the primary

data object. The primary data object traditionally is

one (data quality analysis for one data set), however

the number of the secondary data objects is not

limited, as it on the nature of the primary data object

and its parameters. The number of secondary data

objects depends on the primary data object and its

parameters. A secondary data object is introduced if

it is possible and necessary to analyse the data quality

of the primary data object against another data object.

Moreover, (a) more than one secondary data object

can be related to any parameter of the primary data

object, (b) every secondary data object can be related

to multiple parameters of the primary data object.

In the example, the quality of the primary data

object “Company_UK” is analysed against the

secondary data object “Country” that store codes for

the representation of countries’ names and their

subdivisions (according to ISO 3166, including such

popular standards as ISO2, ISO, Short name, Official

name, UNDP). In the Figure 2, two parameters of the

primary data object “Company_UK” (“Companies

House of United Kingdom”) storing country names

(“RegAddress Country” and “CountryOfOrigin”) are

connected with all parameters of the secondary data

object “Country”.

Figure 2: Data object in context of multiple data objects.

Every secondary data object has its own colour

that is also assigned to arrows connecting the primary

and the secondary data objects. As the description of

data object at this stage is informal, an arrow also

doesn’t represent any semantics – it just depicts an

interconnection of primary and secondary data

objects.

It is also possible to add supplementary data

objects to connect the primary and the secondary data

objects if needed. However, this paper discusses the

most common situation when data objects can be

directly connected by one or more parameter’s

values.

4.2 Data Object Quality Specification

The aim of data quality specification is to allow a user

to define conditions that must be met to admit the data

object (that was defined on the previous stage) as of

high quality. There are two possible ways to define

quality conditions: (a) informal descriptions of

An Extended Data Object-driven Approach to Data Quality Evaluation: Contextual Data Quality Analysis

277

conditions, for example, in a natural language, or (b)

formalized descriptions that are implementation

independent. According to the approach (Bicevskis et

al., 2018b), “data quality specification of a data object

is defined by logical expressions, where names of

data object’s attributes/ fields serve as operands in

logical expressions”.

Figure 3: Data quality specification.

In case of multiple data objects, additional two

steps at this stage should be taken: (1) supplying data

quality specification diagram (Figure 3) with

secondary data object(-s), and (2) specifying how the

primary and the secondary data objects are linked/

connected (Figure 4).

Quality conditions are defined only for the

primary data object (“Company_UK” in Figure 4).

Data quality of the secondary data object (“Country”)

isn’t analysed. Only quality of the primary data

objects is analysed in case of contextual data quality

analysis, as the secondary data objects are auxiliary,

they just supplement analysis. It is more convenient

to ensure data quality of the secondary data object

before it is involved in data quality analysis of another

data object. If the secondary data object quality isn’t

checked before, resulting records will contain

records, where part of them will point out quality

problems of the second data object but detection of

the quality defects of the primary data object will be

more complicated and resource consuming.

Moreover, some quality problems won’t be found at

all, if the quality of the secondary data object is not

high enough. Since the secondary data object’s

quality isn’t analysed, no quality conditions are

defined for the data object “Country” (Figure 4). The

initial structure of the data quality specification

diagram is just supplemented with the secondary data

object as shown in the Figure 4.

When the secondary data object is specified, links

between primary and secondary data objects must be

depicted. At this stage, informal rules could be added

to describe the connections. They are depicted by

adding arrows linking both (a) parameter of the

primary data object which quality is checked against

the secondary data object, and (b) parameters of the

secondary data object involved in this check. The

number of attributes of the secondary data object

involved in the specific quality check determines the

number of dash lines connecting the arrow and the

secondary data object.

Figure 4: Data quality specification in context of multiple

data objects.

In the given example (Figure 4), the quality of two

parameters (“RegAddress_Country” and

“CountryOfOrigin”) is analysed against all

parameters of the secondary data object. It means all

parameters of the secondary data object “Country”

are connected by the arrow containing conditions and

the dashed lines. The colour of the secondary data

object input element corresponds to the colour

assigned to this data object at the 1

stage. Informal

conditions added to arrows are formulated in the same

way as the conditions for each specific parameter.

The arrow’s colour corresponds to the box containing

requirements while the border’s colour corresponds

to the secondary data object’s colour.

Because of data quality’s relative nature, data

quality requirements depend on users’ needs, i.e. on

the defined use-cases. In the given example, several

data quality conditions are possible: (1) all country

names available in the primary data object must

conform to at least one standard; (2) all country

names available in the primary data object must

conform to the one particular standard. In the second

case, following options are also possible: (a)

conformity to one specific standard defined by a

specific user, (b) conformity to one of widely-used

standards. In later case, data object’s pre-processing

is required to determine the standard which the most

records correspond to. The determined standard is

used in quality requirements.

The type of requirement depends on user’s

definition of the “data quality” concept. The first

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

278

option can be used to check whether the stored values

are valid (representing an object of the real world),

whilst the second option is to check whether all data

conforms to the same standards (sometimes called

“homogeneity”).

4.3 Quality Evaluation Process

Quality evaluation process consists of two steps: (1)

the first step describes activities to be taken to select

data object values from data sources (data object class

instances are selected from the data sources and

written into a collection), (2) one or more steps are to

be taken to evaluate quality of primary data objects

according to quality specifications. As stated in

(Bicevskis et al., 2018b), all instances are processed

cyclically by examining the fulfilment of a quality

specification for each individual instance.

Analysing data object in context of multiple

objects, the number of data objects involved in the

analysis determines the number of data sources where

data object values must be selected from. Data object

values of the secondary data objects are gathered from

the data source if the parameter indicating the secon-

dary data object’s value in scope of defined quality

requirement/condition is true (Figure 5). The following

amendments should be done: (a) depiction of data read/

write operations from data source into database, (b)

connection via appropriate parameters, (c) appropriate

quality requirements that are depicted in the primary

data object’s box together with other conditions.

At this stage diagram should contain separate field

checks for the primary data object where each

individual operation evaluates the data quality of the

field by using a SQL statement, programming code

routines or similar executable artefacts (depending on

person’s knowledges and experience).

In this example, SQL statements are used as the

most intuitive option. Initially only the SQL (a)

SELECT statement that specifies the target data

object, and (b) WHERE clause that specifies the

quality specification, were needed. However, the

secondary data objects involved in this process

require additional clause that would link/ combine the

primary and the secondary objects based on one or

more related parameters between them. This task in

SQL is solved by JOIN clause. Type of the JOIN used

to combine data objects depends on the

implementation (the sequence in which they appear in

the statement): LEFT is used if the secondary data

object appears the first (Figure 5), and RIGHT if vice

versa. It means that only three basic SQL statement’s

components form a data quality requirement’s/

condition’s structure for every single analysed

parameter. However, from case to case more complex

constructions and SQL operators are needed for

translating informally defined data quality

requirements into executable statements.

Figure 5: Data quality evaluation process (in context of

multiple data objects).

Figure 5 shows how the parameters requiring

contextual data quality checks against the secondary

data objects are highlighted using border colour

corresponding to the colour was assigned to the

appropriate secondary data object. Each secondary

data object is represented by using the same colour in

all quality evaluation stages (in all three diagrams).

5 ANALYSIS RESULTS

The extended approach was examined and

approbated through applying it to 14 data sets. In this

paper we explain step-by-step analysis of the

previously mentioned data set “Companies House of

the United Kingdom” in order to compare result of

this analysis with previous results (Bicevskis et al.,

2018a). Two parameters containing name of the

country “CountryOfOrigin” and “RegAddress

Country” are validated against the secondary data

object “Country” storing the name of the country

according to the international standards.

These parameters were chosen based on the

results of the approbated initial version of the

proposed approach (Bicevskis et al., 2018a) where the

following data quality problems were detected: (1)

various names indicating the same country (UK and

United Kingdom; USA, United States and United

An Extended Data Object-driven Approach to Data Quality Evaluation: Contextual Data Quality Analysis

279

States of America etc.); (2) names of dissolved

countries such as Czechoslovakia, Yugoslavia,

USSR; (3) values indicating administrative division

or region (Wales, Scotland, England & Wales,

England); (4) 4 values are not countries at all: “SW7”

- South Kensington and part of Knightsbridge postal

code, “EAST SUSSEX” - a county in South East

England, “BWI” - Baltimore/ Washington

International Thurgood Marshall Airport code, “DE

19901” - postal code of Dover.

In total, 128 different values, that possibly can

contain data quality problems, were indicated. The

detection of these 128 values was quite sophisticated

requiring multiple additional queries (grouping and

sorting) in order to detect data anomalies indicating

potential data quality problems. Therefore, the further

analysis of these particular results would be very

resource consuming. Moreover, there was lack of

confidence that all possible data quality problems

were detected and can be detected in this list at all.

These results indicated the necessity to make an

additional analysis, which would (a) facilitate

detection of these data quality problems, and (b)

would provide only defective records which quality

should be improved. It was achieved by presented

extension.

Approbation of the proposed extended version of

the approach shows that 33 of 109 (30%) country

name records of the primary data object does not

conform to any standard. This data quality problem

was detected in 1 045 records (0.14%). 40 of 114

(35%) country name records of “RegAddress

Country” parameter have data quality problems, i.e.,

don’t conform to any standard. This problem was

detected in 206 994 records (27.44%).

Making an additional check whether the primary

data object’s values conform to number of standards,

it was concluded that: (a) all values of

“CountryOfOrigin” conform to one standard, i.e., the

short name, (b) 73 of 74 values of “RegAddress

Country” conform to the same standard, however 1

value – “USA” corresponds to ISO3/UNDP.

Although this problem was observed in 208 039

(27.6%) records, it could be solved by making just 33

corrections in the first analysed parameter and 40

corrections in the second

parameter. The number of

necessary corrections was rather small as invalid

names of both parameters overlap. Therefore, 48

values of country name should be corrected to reduce

the detected problem. Correction of these 48 values

would significantly improve the overall data object’s

quality, reducing data quality problem in 208 039

records. The correction of 48 values does not require

much of the resources (in comparison with 208 039).

In comparison, the extended version of the

proposed approach yielded results that detected the

records with the certain data quality problem.

Comparing both lists it was concluded that the initial

analysis detected 13 values with data quality problem

instead of 48. 115 values didn’t have data quality

problems. In this specific case, the proposed extended

approach was able to improve the results of analysis

by 72.9%. Therefore, the time needed to analyse the

results of the initial approach now could be spent on

the data quality improvement because the number of

corrections is significantly lower than the number of

resulted records where only 10.2% of values had data

quality problems.

This example demonstrated the potential of the

extended data object-driven approach. The number of

possible controls where the proposed extended

approach can yield valuable results is very high. The

results of the contextual control can be used for data

quality improvement.

Moreover, in case of open data, user’s

participation in its quality analysis brings benefits not

only the users themselves, but also data holders if

users share their feedback. It can significantly

improve not only users experience in its use but also

the overall quality of open data.

6 CONCLUSIONS

The paper is a continuation and refinement of the

authors’ previous researches in data quality area

(Bicevskis, 2018b), (Nikiforova, 2018a). The paper

focuses on the proposed data object-driven approach

to data quality evaluation improving it with the

possibility to analyse real data object’s quality within

the context of multiple data objects. The extended

approach yielded significantly improved results of the

data quality analysis.

Moreover, although the extended approach was

complemented with more in-depth analysis, the

diagram’s structure remained easy to read, create,

understand and edit even by non-IT and non-Data

Quality professionals. It means that even deeper data

quality analysis within the context of multiple data

objects requires minimal involvement of IT-experts.

The presented extended approach is very intuitive.

It significantly facilitates use of the data quality

analysis by a regular user. The proposed structure

eliminated the necessity of additional in-depth quality

analysis, as well as writing complex queries and

individual analysis of the results. The initial version

of approach indicated records potentially containing

data quality problems, however, this was very

ICEIS 2019 - 21st International Conference on Enterprise Information Systems

280

resources consuming process. The proposed

extension of the approach detects only the records

with the certain data quality problem by analysing the

primary data object against other data objects that

probably was collected by other data publishers. It

allows to make more comprehensive and deep data

quality analysis thus improving decision-making.

Although this extended approach was exemplified

by examining open data sets, it is not specifically

tailored to open data. It can also be applied to assess

the quality of any type of structured or semi-

structured data which is clear benefit of this extended

approach. The authors expect that the proposed

extended approach will provide possibility to analyse

“foreign”/ “external” data sets without any

information about initial data collection and

processing. This will enable users to analyse any

available data set according to their needs in the

specific use-case.

Further research will be focused on (a) applying

and evaluating the extended approach in the cases of

complex data object’s structure, including

supplementing data objects in cases when direct

connection between the primary and the secondary

data objects is not possible, (b) detecting possible

limitations of the proposed extended approach, (c)

ensuring possibility to evaluate data sets’ evolution,

(d) assessment of possibility to provide users with

suggestions for data improvement derived from

information on the defective values.

ACKNOWLEDGMENTS

This work has been supported by University of Latvia

project AAP2016/B032 “Innovative information

technologies".

REFERENCES

Batini, C., Cappiello, C., Francalanci, C., Maurino, A.

(2009). Methodologies for data quality assessment and

improvement. ACM computing surveys (CSUR), 41(3),

16.

Batini, C., & Scannapieco, M. (2016). Data and

information quality. Cham, Switzerland: Springer

International Publishing. Google Scholar.

Bicevska, Z., Bicevskis, J., Oditis, I. (2017). Models of

Data Quality. In Information Technology for

Management. Ongoing Research and Development (pp.

194-211). Springer, Cham.

Bicevskis, J., Bicevska, Z., Nikiforova, A., Oditis, I.

(2018a). Data quality evaluation: a comparative

analysis of company registers’ open data in four

European countries. In Communication Papers of the

Federated Conference on Computer Science and

Information Systems (FedCSIS) (pp. 197-204).

Bicevskis, J., Bicevska, Z., Nikiforova, A., Oditis, I.

(2018b). An Approach to Data Quality Evaluation. In

2018 Fifth International Conference on Social

Networks Analysis, Management and Security

(SNAMS) (pp. 196-201). IEEE.

Caro, A., Calero, C., Piattini, M. (2007). A Portal Data

Quality Model for Users and Developers. In ICIQ (pp.

462-476).

Economist, T. (2017). The world’s most valuable resource

is no longer oil, but data. The Economist: New York,

NY, USA.

Eppler, M. J. (2006). Managing information quality:

Increasing the value of information in knowledge-

intensive products and processes. Springer Science &

Business Media.

Fisher, C. W., Kingma, B. R. (2001). Criticality of data

quality as exemplified in two disasters. Information &

Management, 39(2), 109-116.

Gartner (2018). How to Create a Business Case for Data

Quality Improvement. Available at: https://www.

gartner.com/smarterwithgartner/how-to-create-a-

business-case-for-data-quality-improvement/

Gartner (2013). The State of Data Quality: Current

Practices and Evolving Trends. Available at:

https://www.gartner.com/doc/2636315/state-data-

quality-current-practices

Huang, K. T., Lee, Y. W., Wang, R. Y. (1999). Quality

information and knowledge management. Publisher:

Prentice Hall.

Nikiforova, A. (2018a). Open Data Quality Evaluation: A

Comparative Analysis of Open Data in Latvia. Baltic

Journal of Modern Computing, 6(4), 363-386.

Nikiforova, A. (2018b). Open Data Quality. In Baltic

DB&IS 2018 Joint Proceedings of the Conference

Forum and Doctoral Consortium, Trakai, Lithuania

(Vol. 2158, pp. 00742158-1).

Redman, T. C., Blanton, A. (1997). Data quality for the

information age. Artech House, Inc.

Wang, R. Y., Strong, D. M. (1996). Beyond accuracy: What

data quality means to data consumers. Journal of

management information systems, 12(4), 5-33.

The Register of Enterprises of the Republic of Latvia

(2019). Available at: http://dati.ur.gov.lv/register/

(Accessed: 20 March 2019)

Company House (2018). Available at: https://www.

gov.uk/government/organisations/companies-house

(Accessed: 20 March 2019)

TDQM, (2019). The MIT Total Data Quality Management

Program. Available at: http://web.mit.edu/tdqm/

(Accessed: 20 March 2019)

DAMA, (2019). The six primary dimensions for data quality

assessment. Defining Data Quality Dimensions.

DAMA UK Working Group.

OpenDataSoft (2019). Available at: https://www.open

datasoft.com/a-comprehensive-list-of-all-open-data-

portals-around-the-world/ (Accessed: 20 March 2019).

An Extended Data Object-driven Approach to Data Quality Evaluation: Contextual Data Quality Analysis

281