Metadata Quality Dimensions for Big Data Use Cases

Widad Elouataoui

, Imane El Alaoui

and Youssef Gahi

Laboratoire des Sciences de l’Ingénieur, University of Ibn Tofail, Kénitra, Morocco

Laboratoire des Systèmes de Télécommunications et Ingénierie de la Décision, University of Ibn Tofail, Kénitra, Morocco

Keywords: Metadata Quality, Big Data, Metadata Management, Metadata Quality Dimensions, Machine Learning.

Abstract: Even though more than two decades have elapsed since the Big data big bang, there are still big data problems

that are not addressed. Indeed, the emergence of big data has raised several challenges related to data analysis,

data quality, and all activities involving data processing. These challenges have also affected metadata

management as a crucial component of big data ecosystems. Thus, many metadata management approaches

for big data environments have been suggested recently. However, a metadata management policy could not

be effective if the metadata is of low quality. Metadata quality directly impacts data quality and, thus, the

reliability of data analysis. Moreover, enhancing metadata quality in big data environments allows optimizing

data processing time and cost, one of the biggest concerns of data managers. Thus, this paper aims to

contribute to the ongoing discussion regarding metadata quality enhancement. Therefore, we highlight the

metadata quality issues raised by the particular characteristics of big data. We then provide recommendations

to overcome the presented quality issues and conclude with some possible future work directions.

1 INTRODUCTION

Nowadays, data is considered one of the most

valuable resources companies could rely on to

improve their business processes. Data analytics

allows companies to collect practical knowledge

about their customers and their business strategy and

supports their decision-making. Achieving such

objectives requires a robust data management policy.

However, a data management policy could not be

effective if it does not include metadata management

since metadata is crucial. Indeed, managing metadata

is highly required for a complete understanding of

data content. Metadata could be defined as data about

data (Immonen et al., 2015). It summarizes all

relevant information about data such as quality,

resource type, provenance, and other technical

details. However, metadata could not be used

efficiently if it is not well structured and of high

quality. Indeed, the gathered metadata may contain

errors such as empty fields, duplicated records, or

inconsistent values. Therefore, ensuring metadata

quality has received much attention from companies

https://orcid.org/0000-0002-2968-2389

https://orcid.org/0000-0003-4428-0000

https://orcid.org/0000-0001-8010-9206

and researchers. Also, multiple approaches have been

suggested in the literature to assess and improve

metadata quality (Bruce and Hillmann, 2004),

(Király, 2017).

With the emergence of big data, new challenges

related to metadata and data management have been

raised. Managing big data involves handling many

unstructured and heterogeneous data that traditional

data management tools could not process. Thus, new

data analytics techniques and processing approaches

have been introduced to manage big data (El Alaoui et

al., 2019a), (Alaoui and Gahi, 2019). Also, big data

requires a special treatment related to its particular

characteristics, also known as big data 7V's (Kapil et

al., 2016), (Alaoui et al., 2019b) (Table 1).

488

Elouataoui, W., El Alaoui, I. and Gahi, Y.

Metadata Quality Dimensions for Big Data Use Cases.

DOI: 10.5220/0010737400003101

In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning (BML 2021), pages 488-495

ISBN: 978-989-758-559-3

Table 1: Big Data Characteristics.

Characteristic

Meaning

Volume

It is an essential characteristic that refers

to the vast amount of data generated and

processed in big data systems.

Variety

Big data incorporates heterogeneous

data sources and includes different data

types that could be structured, semi-

structured or unstructured.

Velocity

Refers to the high speed at which data is

generated and processed.

Veracity

Refers to the accuracy and the

truthfulness of the generated data.

Value

Refers to the insights and the business

value that data provide.

Variability

Sheds light on the dynamic aspect of

data. It refers to the frequency at which

the meaning of data changes.

Visualization

It refers to the process of presenting the

extracted information in a readable

form, more comfortable to understand.

The collected metadata in big data environments is

usually not well structured, inconsistent, and low

quality because of big data characteristics. Such

metadata is useless and may even bias data analytics

results. Hence, enhancing metadata quality in big data

environments is of a high priority and should be

considered by data owners.

Therefore, this paper aims to shed light on the

metadata quality challenges raised by the emergence

of big data, a less discussed issue in the existing

literature. Also, some solutions to overcome these

challenges will be suggested.

This paper is divided into four parts: Section 2

describes the metadata quality dimensions introduced

in the literature. Section 3 reviews all related works

that have tackled metadata quality issues and metadata

management in the big data era. Section 4 highlights

the metadata quality issues raised by the emergence of

big data and proposes some recommendations deal

with the problems presented. Finally, conclusions will

be made, and future research directions will be

discussed.

2 METADATA QUALITY

DIMENSIONS

Metadata quality could not be addressed without

considering its main characteristics, also known as

Metadata Quality Dimensions. Metadata quality

dimensions were first defined by Bruce and Hillmann

that have defined seven quality dimensions: accuracy,

consistency, completeness, conformance to

expectations, timeliness, accessibility, and

provenance. Later, other studies have extended these

basic dimensions to include more metrics such as

shareability, extendibility, and versionability [8] [9]

[10]. Table II describes the most common metadata

quality dimensions discussed in the literature that we

grouped into four aspects: Usability, Reliability,

Availability, and Interoperability.

Table 2: Metadata Quality Dimensions.

Quality Aspect

Metadata Quality

Dimension

Meaning

Usability

Completeness

Assures that there are

no missing attributes

and all the expected

attributes have values.

Consistency

The extent to which

metadata is coherent

and aligned with

metadata standards

and schemas.

Usefulness

The extent to which

metadata is valuable

and relevant for its

intended use.

Reliability

Accuracy

The degree to which

metadata is correct and

reliable.

Timeliness

Refers to how recent

and up-to-date the

metadata is.

Availability

Accessibility

The extent to which

metadata is available

and easily accessible.

Security

It consists of ensuring

that access to metadata

is appropriately

restricted.

Interoperability

Shareability

The extent to which

metadata could be

effectively used out of

its local environment.

Discoverability

The extent to which

metadata is visible and

can be easily found.

Extendibility

The extent to which the

metadata may be easily

extended [9].

Versionability

The extent to which a

new version may be

easily created [9].

Metadata Quality Dimensions for Big Data Use Cases

489

One of the most common questions that come up

is: "Which of these metadata quality dimensions is the

most important? ". We believe that a definitive answer

to this question could not be given since the most

crucial quality dimension could differ from one

context to another. Indeed, the prioritization of these

dimensions mainly depends on the intended use of

data, the context, and the organizational policy. Thus,

several approaches to assess and improve metadata

quality have been suggested in the literature. Each has

focused on the dimensions deemed to be most relevant

to the study's context and requirements.

In the next section, we survey the available

approaches and works that have tackled metadata

quality and metadata management in the Big data era.

3 RELATED WORK

Enhancing metadata quality has long been addressed

by the literature. Indeed, the first paper that has

introduced metadata quality dates back to 2004 [11],

when Bruce and Hillmann have defined seven quality

dimensions related to metadata: accuracy,

consistency, completeness, conformance to

expectations, timeliness, accessibility, and

provenance. The authors have also defined three levels

to address metadata quality: the semantic level, the

syntactic level, and the data values themselves.

Likewise, Steve et al. in [12] have defined a taxonomy

of 38 information quality dimensions divided into

three categories (Intrinsic, Relational and

Reputational). Moreover, they have suggested an

approach to conceptualize and measure metadata

quality while considering data use.

Later, there have been many efforts to implement

metadata quality dimensions. Ochoa and Duval in [13]

have proposed metrics and measurement methods that

assess the seven metadata quality dimensions

previously defined in [11]. For the same purpose, the

authors in [14] have described the formulas to measure

and assess five metadata quality metrics

(completeness, weighted completeness, accuracy, the

richness of information, and accessibility). The

proposed metrics were implemented and applied to

three public government data repositories. Also,

Király and Buchler, in [15], have suggested an open-

source implementation to measure some metadata

quality metrics such as completeness, uniqueness, and

multilingualism.

Other authors have even suggested relevant

frameworks to assess and improve metadata quality in

different domains. The authors in [9] have defined

new metadata quality dimensions such as extendibility

and versionability. They have also suggested a quality

framework that allows assessing biomedical metadata

quality. In research and open science, Kubler et al. in

[16] have developed a quality framework that will

enable users to evaluate the metadata quality of open

data portals using the Analytic Hierarchy Process

(AHP). In the same area, Király in [17] have proposed

a flexible metadata quality assessment framework that

works with several metadata schemas and can also

support new schemas. Always in the field of digital

libraries, other metadata quality assessment

frameworks were suggested in [18] [19] [20].

All the research mentioned above has made a

significant contribution to metadata quality

enhancement. However, they have not addressed the

metadata quality issues that could be raised in big data

environments. Indeed, given the particular

characteristics of big data, ensuring metadata quality

in a big data project is more challenging and requires

a more specific and in-depth study. Data in big data

environments also goes through multiple processing

steps known as the big data value chain (BDVC),

leading to a loss and degradation of metadata quality.

Moreover, because of big data characteristics, the

collected metadata in big data environments is usually

not well structured, inconsistent, and low quality.

Thus, enhancing metadata quality is highly required

for effective and reliable big data analysis. However,

very few initiatives have been conducted to address

metadata quality in a big data context.

In [21], the authors have identified some of the top

quality issues related to metadata used in the Dryad

data repository. In this study, the authors have also

shown how low metadata quality could have a

negative impact on the results of data analytics. This

work was focused on enhancing metadata quality in

research metadata repositories. Likewise, authors in

[22] have suggested an approach to assess metadata

quality dimensions such as completeness,

multilingualism, and reusability. The proposed

method was implemented in Europeana, a digital

platform for cultural heritage qualified as Big Data. In

[23], a novel big data quality approach encompassing

all big data processing phases (input, throughput,

output) has been suggested. In this paper, the authors

have defined the quality dimensions that should be

considered for the data source, data, and metadata.

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Table 3: Metadata Quality Approaches

Ref Main Findings

For Big

Data?

Metadata quality Dimensions

Application

Domain

[11] New Metadata Quality Dimensions No

accuracy, consistency,

completeness, conformance to

expectations, timeliness,

accessibility, provenance

Generic

[12]

A taxonomy of 38 information quality

dimensions divided into three categories:

(Intrinsic, Relational, and Reputational)

completeness, redundancy,

consistency, accuracy,

completeness

Generic

[13]

Metrics and measurement methods to

assess the metadata quality dimensions

accuracy, consistency,

completeness, conformance to

expectations, timeliness,

accessibility, provenance

Generic

[14]

Formulas to measure the metadata quality

dimensions

completeness, weighted

completeness, accuracy, the

richness of information,

accessibility

Generic

[15]

An open-source implementation to

measure metadata quality

completeness, uniqueness, and

multilingualism

Generic

[9]

A quality framework to assess biomedical

metadata quality

No extendibility and versionability Health

[16]

A quality framework to assess metadata

quality in open data portals

existence, conformance,

retrievability, accuracy,

accessibility

Digital libraries

[17]

An extensible metadata quality assessment

framework that supports multiple metadata

schemas

No measurement flexibility Digital libraries

[18]

A study to show the correlation between

the use of geospatial datasets and the

quality of metadata

existence, conformance,

accessibility

Digital libraries

[19]

A metadata analyzer that measures

metadata quality based on ontology

concepts and the terms used in the metadata

term coverage, semantic

specificity

Digital libraries

[21]

Illustrating some of the primary data

quality issues associated with the use of

metadata in the Dryad Repository

Yes completeness, consistency Digital libraries

[22]

A novel approach to assess metadata

quality in Europeana

Yes

completeness, multilingualism,

and reusability

Digital libraries

[23]

A global big data quality approach to

enhance the quality of data source, data,

and metadata as well

Yes

complexity, completeness,

usability, linkability,

consistency, validity

Generic

Other issues related to metadata management in

big data contexts, such as metadata storage and

processing, have been addressed in [24] [25] [26].

However, this research has not tackled metadata's

quality aspect and has focused on the metadata

system's functional architecture. A summary of the

reviewed studies is described in Table 3.

To the best of our knowledge, the few available

studies that have tackled metadata quality in a big data

context are limited to a particular big data application

domain (e.g., research repositories, Europeana) and do

not deal with the subject whole. Thus, to address

metadata quality issues in big data projects, we

highlight in the next section how the particular

characteristics of big data could impact the quality of

metadata. Also, some recommendations to overcome

these quality issues will be suggested.

Metadata Quality Dimensions for Big Data Use Cases

491

4 METADATA QUALITY ISSUES

AND SOLUTIONS IN BIG DATA

SYSTEMS

This section highlights how big data could impact the

most common metadata quality dimensions:

Usefulness, Completeness, Accuracy, Consistency,

Shareability, and Timeliness. Moreover, we propose

some solutions to overcome the presented issues.

4.1 Usefulness

Managing big data consists of handling a large volume

of data and metadata, which generally goes through

several processing steps, such as data acquisition, data

pre-processing, data storage, data analysis, and data

visualization, known as Big Data Value Chain

(BDVC). Indeed, in a big data era, metadata is used to

store basic descriptions of data and save information

related to data processing, such as data analysis results

and data quality assessment measures. Thus, when

data is collected, the gathered metadata attributes may

be associated with the original context of data use and,

thus, not relevant to the current environment.

Therefore, the accumulated metadata attributes must

be cleaned and filtered to keep only useful and

valuable information. Once the outside metadata

elements are determined, metadata can be cleansed

using outlier detection techniques, parsing, statistical

clustering, etc. Besides, in a big data context, data are

usually gathered from multiple data sources. This

could lead to metadata redundancy since data sources

use different standards and terminologies to rename

the metadata attributes. Thus, a redundancy check

must be performed on the selected parameters before

embarking on data analysis. There are two types of

metadata redundancy:

• Semantic Metadata Redundancy: Refers to

information redundancy that can be found in metadata

attributes. To avoid this kind of redundancy, metadata

mapping techniques can align metadata models of two

different systems. Also, it is highly recommended to

use metadata standards and file naming convention

frameworks to automate metadata mapping.

• Syntactic Metadata Redundancy: Refers to

string similarities that could be found within metadata

values. To address syntactic redundancy, similarity

measures can be applied. Moreover, other

methodologies suggested in [8] [27] to assess

metadata records' similarity can be used.

4.2 Completeness

In big data environments, the collected raw data are

usually cleaned, reduced, and transformed into a

helpful format before being analyzed. This process is

also known as data ingestion. During this phase,

metadata must be completed with the appropriate

contextual information. The gathered metadata is

usually insufficient for the intended purpose and lacks

contextual information related to the data source and

the collection process. Moreover, some missing

information may not be specified in the original

context because they are considered assumed or not

helpful [20]. There are two layers of metadata

completeness. The first layer consists of ensuring that

the required attributes are not missing. For this

purpose, we suggest defining a metadata model that

describes all the essential information that needs to be

incorporated throughout the metadata. It is beyond this

paper's scope to provide a detailed description of this

model since it mainly depends on the intended use of

the data and the organizational policy. However, we

can introduce some general guiding questions as

follows:

• Does the available metadata provide all the

required descriptive information of the data?

• Does the available metadata provide all the

information required by the different processing

phases that the data goes through?

• Does the available metadata provide all the

information required by the data quality approach

adopted?

• Does the available metadata provide all the

information required by the organizational policy?

The second layer of metadata completeness ensures

that metadata values describe the first layer's attributes

completely and exhaustively. For this purpose, the

authors in [23] have suggested attributing a clarity and

completeness score to each parameter. (e.g., 0

description missing, 1 Insufficient description, 2

Complete description).

4.3 Accuracy

Big data comes from multiple data sources that are not

always credible and may contain inaccurate and not

factual values (e.g., social media data). This calls into

question the reliability of the information provided in

the metadata. Thus, knowing the provenance of

metadata could already offer an excellent basis to

make a quality judgment. Therefore, information like

which organization the metadata comes from, how

well they are an expert on metadata standards and

classifications, what transformations were applied to

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metadata should be considered when assessing

metadata accuracy [11]. Accuracy is usually evaluated

by measuring how values match with an accurate

model. Thus, a metadata set model could be defined

from an open metadata portal or similar corroborative

metadata set values. The metadata set model could

then be trained and compared to a metadata collection

using machine learning and record linkage algorithms.

Given the enormous volume of the gathered metadata

in big data environments, metadata accuracy could not

be directly assessed. Thus, some data inspection

procedures could be performed, such as sampling and

profiling techniques. As mentioned earlier, metadata

is also used to store information related to data

processing and data quality assessment. This

information is usually quantified using approximate

values rather than exact real ones. Indeed, processing

a large volume of data is costly in time, money, and

effort. Therefore, several quality approaches are

confined to analyze a representative sample rather

than the entirety of data. The results of the sample

quality assessment are then generalized to the whole

data. The type of sampling method used to select the

data to evaluate can impact the assessment results.

Thus, to ensure high accuracy, metadata that contains

such results should also include an uncertainty score

based on the conditions under which the data analysis

and assessment were made.

4.4 Consistency

One of the most common characteristics of big data is

variety. This characteristic refers to the heterogeneous

aspect of big data from various data sources and

incorporates different data types. This aspect also

applies to the gathered metadata since the data sources

use different metadata standards and terminologies

depending on the data and the organizational policy.

Thus, one of the most critical issues to address when

handling big data is to convert the gathered metadata

that is usually unstructured and heterogeneous into

uniform and consistent metadata. Also, not all the

collected metadata attributes are aligned with

metadata standards and schemas.

Moreover, inconsistency may be related to

metadata values that do not use the same formats and

range of values (e.g., inconsistencies with the structure

of date and time attributes), creating some anomalies

and bias data analysis. Therefore, it is highly

recommended before collecting data to define the

standards and schemas used, especially for critical

variables. Also, the impact of such anomalies on the

potential use of data should be determined.

4.5 Shareability

In [10], Shreeves et al. have observed that even if

metadata is of high quality within a local database, this

quality may not be maintained when metadata is

combined in a federated environment. Later, in [28],

Shreeves et al. have introduced "shareability" as a

crucial metadata quality dimension to cross-domain

resource discovery and promote search

interoperability. Thus, shareable metadata is metadata

that does not lose its quality and could be effectively

used outside its local environment. Ensuring metadata

shareability would significantly contribute, especially

in a big data era that consists mainly of aggregating

data from multiple resources. Given that metadata is

also used to store information related to data

processing, reusing the obtained results will save time

and cost. For this purpose, metadata should use a

controlled vocabulary and should be conformed to

metadata standards. To enhance metadata shareability,

it is highly recommended to use ontologies while

structuring metadata. Indeed, ontologies provide a

formal representation of metadata attributes with their

properties and the relationships that hold between

them. Thus, using ontologies allows presenting

metadata in a more readable and machine-processible

format, promoting metadata discoverability and

enabling automatic metadata processing. Also, shared

metadata must be well documented and must provide

the appropriate context of use. Moreover, content

should be kept as short as possible and optimized to

keep only the pertinent information.

4.6 Timeliness

One of the essential characteristics of big data is

variability. This characteristic sheds light on the

dynamic aspect of continuously updated data as new

information becomes available such as IoT and social

media. Therefore, metadata should also be regularly

updated on every change of data. Indeed, outdated

metadata is useless and may even confuse the obtained

results. Also, some metadata are static and do not

change over time. Therefore, metadata that needs to be

updated should be determined when establishing the

metadata model. Another aspect of metadata

timeliness could occur when the collection of data and

metadata are not synchronized. This problem becomes

serious in the case of real-time data when time-

sensitive analysis should be performed. Thus, an

assessment of the risks associated with metadata

unavailability and the use of outdated metadata should

be performed.

Metadata Quality Dimensions for Big Data Use Cases

493

It is worth noting that metadata quality dimensions

are strongly related to each other. For example, adding

additional information to improve completeness may

lead to increase metadata redundancy. It is

recommended to find the right balance to ensure that

any dimension is impacted heavily in such a case.

Thus, focusing only on one dimension without

considering its correlation with the others may not be

a practical approach for supporting metadata quality.

Therefore, for a successful metadata quality

assessment and improvement, data managers should

also consider the existing dependencies between the

metadata quality dimensions while prioritizing

metadata quality dimensions. This would also help

data managers to define the causes that may degrade a

specific dimension. Thus, improving metadata quality

is not limited to enhance the quality of metadata

dimensions. It is a whole process that involves other

components such as the intended use of data, the

business requirements, the organizational policy, and

the data owners.

5 CONCLUSIONS

Ensuring metadata quality is of great importance since

it directly impacts data quality and, thus, the extracted

insights' reliability. Therefore, several approaches

have been suggested in the literature to assess and

improve metadata quality. With the emergence of big

data, new challenges related to metadata quality have

been raised by big data's particular characteristics,

known as 7 V's. To the best of our knowledge, no

studies have been conducted to address the impact of

big data 7V is on the different metadata quality

dimensions. Thus, this work's purpose was to

highlight the quality issues related to metadata in big

data environments. This study analyzed each big data

characteristic's impact on the most common metadata

quality dimensions. Thus, six metadata quality

dimensions have been addressed: accuracy,

usefulness, completeness, consistency, shareability,

and timeliness. Also, some recommendations to

address the raised issues have been suggested. As

future work, we aim to propose a novel quality

framework for big data that addresses the metadata

quality issues raised in this paper and implements the

suggested solutions while considering the different

factors that could impact metadata quality, including

the organizational policy project context and the

business requirements.

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