A Systematic Survey of Data Value: Models, Metrics, Applications and Research Challenges

Abstract

Data is central to modern decision making and value creation. Society creates, consumes and collects data at an increasing pace. Despite advances in processing power, data is expensive to maintain and curate. So, it is imperative to have methods and tools to distinguish between data based on its value. Yet, there is no consensus on what characterises the value of data or how this data value should be assessed. This results in heterogeneous data value models and inconsistent measurement techniques that are siloed in specific application domains. This limits the formalisation and exploitation of these concepts. We present in this paper a methodical literature analysis that discusses data value models, assessment metrics and current applications. We also highlight challenges hindering the development and exploitation of data value as concept. This leads to the identification of a set of research questions to help researchers contribute to this emerging field. The aim of this article is to stimulate further research and deployment of quantitative data value models and value-driven applications.

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A Systematic Survey of Data Value: Models,
Metrics, Applications and Research
Challenges
MALIKA BENDECHACHE1, JUDIE ATTARD 2, MALICK EBIELE 3and ROB BRENNAN3(Senior Member,
IEEE)
1ADAPT & Lero Research Centres, School of Computer Science, University of Galway, Galway, Ireland (e-mail: malika.bendechache@universityofgalway.ie)
2(e-mail: judieattard@gmail.com)
3ADAPT Research Centre, School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland (e-mail: malick.ebiele@adaptcentre.ie,
rob.brennan@ucd.ie)
Corresponding author: Malika Bendechache (e-mail: malika.bendechache@universityofgalway.ie).
This work was supported in part by the Science Foundation Ireland (SFI) grants 13/RC/2094_P2 (Lero) and 13/RC/2106_P2 (ADAPT)
ABSTRACT Data is central to modern decision making and value creation. Society creates, consumes and
collects data at an increasing pace. Despite advances in processing power, data is expensive to maintain and
curate. So, it is imperative to have methods and tools to distinguish between data based on its value. Yet,
there is no consensus on what characterises the value of data or how this data value should be assessed.
This results in heterogeneous data value models and inconsistent measurement techniques that are siloed in
specific application domains. This limits the formalisation and exploitation of these concepts. We present in
this paper a methodical literature analysis that discusses data value models, assessment metrics and current
applications. We also highlight challenges hindering the development and exploitation of data value as
concept. This leads to the identification of a set of research questions to help researchers contribute to this
emerging field. The aim of this article is to stimulate further research and deployment of quantitative data
value models and value-driven applications.
INDEX TERMS Data Value, Data Value Characterisation, Data Value Quantification, Data Governance,
Data Value Metrics.
I. INTRODUCTION
Data has become an indispensable commodity for society [1].
For example, the European Union has given high importance
to data from strategic, legal and regulatory perspectives1.
Big data, and especially big data analytics, is increasingly
prevalent as a driver of business value [2]. This is part of
the trend where intangible assets have become an important
source of value for businesses [3]. These data assets can range
from market intelligence reports to sensor readings. Such
data assets can be acquired or generated internally, either
directly or as a by-product of offering goods or services [4].
Data lags behind other intangible assets by not being added
to the balance sheet [5] and part of the reason for this is the
challenge in assigning a value to the data [6]. The importance
of valuing intangible assets, however, is frequently pointed
out in relevant research [7, 8, 9, 10, 5]. The EU Open Data
1https://ec.europa.eu/info/strategy/priorities-2019-2024/
europe-fit- digital-age/european- data-strategy_en
Directive2stresses the importance of high-value datasets:
identified as datasets covering a number of domains that
provide important benefits to the society, the environment,
and the economy through their use and reuse. But what is
exactly meant by “data value"?. Many publications explore
this term [11, 12, 13], but there is currently no consensus on
the definition of data value, its component dimensions or on
how this data value can be assessed or quantified.
A major challenge in valuing data assets is the heteroge-
neous nature of value, often broken down into dimensions
of value [14]. This is exacerbated by the observation that
data value, like data quality, is both subjective and highly
context dependent [15, 13, 16]. In the literature numerous
approaches are considered to characterise data value. For
example, Moody and Walsh [17] describe 7 “laws” of infor-
mation which are applicable to data assets:
2https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%
3A32019L1024
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1) Information [data] is infinitely sharable;
2) The value of information [data] increases with use;
3) Information [data] is perishable;
4) The value of information [data] increases with accu-
racy;
5) The value of information [data] increases when com-
bined with other information [data];
6) More is not necessarily better; and
7) Information [data] is not depletable.
These properties contrast strongly with other tangible assets
that have a more easily defined value in use or exchange.
Other early authors consider additional dimensions of the
value of data, including the resulting business impact of new
data [18], usage [6], and monetary costs or benefits [19].
The conceptual heterogeneity of data value leads to numer-
ous approaches to quantify the value of data. For example,
cost-based, market-based, and income-based approaches are
frequently used to monetarily valuate intangible assets and
data assets [9, 20, 21]. Data value measurement has also been
applied in numerous domains as part of decision support or
automated systems. For instance, consider data valuation for
information life cycle management [22, 23]. In this context,
data valuation becomes an indispensable tool for effectively
governing the entire trajectory of data – from its inception
to archival or disposal. By assigning a tangible value to data
at each stage, organisations can make well-informed choices
about data retention, archival frequency, and disposal strate-
gies. This not only optimises data resource allocation but also
ensures that valuable information is appropriately managed
throughout its lifecycle. Another example is data markets and
data pricing [24]. Much like traditional marketplaces where
goods and services are traded, data markets have emerged as
platforms where data can be exchanged, bought, and sold.
The value attached to various datasets forms the basis for
these transactions. Organisations seeking specific datasets
can engage in a market-driven approach to acquire the data
they require, thereby fostering a data-driven economy. This
innovative approach relies heavily on accurate data valua-
tion to facilitate fair and mutually beneficial transactions.
business decision making in agriculture [25] is also another
example. With advancements in precision agriculture and
data-driven decision making, farmers now leverage data to
optimise crop yield, manage resources efficiently, and make
informed business choices. Data value assessment allows
farmers to prioritise which data streams are most critical for
their operations, ensuring that investments in data collection
and analysis align with tangible benefits. This is pivotal
in a sector where data-driven insights can directly impact
productivity and profitability. Spanning this work requires an
interdisciplinary approach as it draws on information systems
deployments in many fields as well as computer science
research.
There are many reasons beyond balance sheet concerns
to quantify the value of the data itself, and to treat data
independently from specific data-driven applications or busi-
nesses. These include: Data can be shared between many
applications or even organisations; It is more efficient to
develop reusable assessment methods, tools and metrics
that treat data in an application-independent fashion, as
seen with data quality assessment methods [26]. Common
models for data value will enable heterogeneous tools and
data value-consuming applications, such as value-driven data
governance systems, to operate seamlessly in terms of data
value quantification [27, 28]. The literature identifies many
purposes for measuring the value of data. For example to
provide knowledge of the value of data/information as an
asset for merger negotiations [12]; to improve organisational
accountability for data by raising awareness [5]; to justify the
costs of creating, maintaining or purchasing data [29, 30]; to
identify relevant data for an application [31, 5]; and to enable
data-driven decision-making about data like file retention
[32]. Most of the time, data’s true value is not recognised
or exploited as an asset by organisations [17, 10, 5]. This is
particularly evident in the latest trend of hoarding data [33]
where organisations blindly capture all possible data with
little means to discriminate between the data being accu-
mulated, despite the fact that data storage still comes with
costs [34]. Robust, easily interpretable data value assessment
techniques will give us the tools to address this problem.
It is notable that despite the width of literature available on
data value, only two publications provide a comprehensive
overview through systematic surveys: (1) Viet et al. [35]
surveyed works published between 2006 and 2017 and fo-
cused on the related concept of value of information (VoI)
in supply chain decisions; (2) Alawad and Kraemer [36]
authored a systematic review focusing on VoI in wireless
sensor networks and Internet of Things. Both of these pa-
pers are limited to their respective application domains. In
addition, these works did not examine other related terms for
VoI, such as data value, information value, data valuation, or
information asset. Other publications provide a more unstruc-
tured approach to reviewing literature. For example, Faroukhi
et al. [37] presented an unstructured survey of the litera-
ture related to data monetisation in big data value chains.
Again this work was limited in scope and we eliminate
data monetisation from our focus to minimise the overlap.
Yanlin and Haijun [38] provide a timely but non-systematic
survey of data value concepts focusing only on the Chinese
literature. Fleckenstein et al. [39] provide a useful framework
of approaches to data valuation models that we reuse but their
work is not supported by a systemic search as presented here,
lacks details on assessment metrics useful for automation and
instead focuses on qualitative approaches to assessment.
This shows that there is both a need and a gap in the
literature for a wider structured survey aimed at unifying the
field and identifying a wide-ranging research agenda. In this
paper we provide a systematic survey that comprehensively
analyses the existing literature covering the domain of data
value in terms of data value models, assessment metrics, and
applications.
The rest of this paper is structured as follows: Section II
defines some terminology and concepts used throughout the
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paper. In Section III, we describe our research method for
the systematic survey. Section IV provides an analysis of the
primary studies resulting out of the systematic survey with
a focus on data value models, metrics and applications. We
discuss our research agenda and the highlighted relevant re-
search questions in Section V and we provide our conclusions
and future work in Section VI.
II. TERMINOLOGY AND CONCEPTS
In this section we provide a number of definitions of terms
related to the topic of data value, and used throughout this
article. This is to provide consistency to the discussion and
more broadly to the domain of data value research.
Data - The Oxford dictionary defines data to be “[f]acts
and statistics collected together for reference or analysis"3.
Data is therefore facts about the world. Data can be collected
(e.g. using sensors or surveys) or calculated (e.g. age from
date of birth). Data can be represented in different formats,
including text, images, spreadsheets, JSON, etc. Here we do
not discuss the distinction between data, information, and
knowledge, and for the sake of incorporating all relevant
literature, we include all three terms in the scope of our
search.
Value - In the generic sense of the term, the Oxford dictio-
nary defines value to be “[t]he regard that something is held to
deserve; the importance, worth, or usefulness of something"
4. While this applies in most, if not all, contexts, different
disciplines have more specific definitions. For example, in
economics, the definition and measurement of value would
be in terms of currency. Stern [40] identified two kinds of
value in the context of natural resource scarcity indicators:
use value and exchange value. Prices and rents are common
measures of exchange value and unit costs as a measure of
use value [40]. Other definitions, such as sentimental value,
would be in terms of personal or emotional associations
rather than material worth. It is evident that these varying
definitions are tied to the subjective and contextual nature
of value. With the aim of characterising the latter concept,
we define value to be a number of different data value
dimensions (attributes) that in an aggregate manner represent
the worth return of the thing in question.
Data Value - There is a wide range of definitions of data
value across different domains and for specific use cases in
the literature. For instance in ML, data value is the weight or
contribution of each training sample or feature in improving
a model performance [41, 42, 43]. In applied energy, data
value is defined as the quantitative relationship among the
data, uncertainty reduction, and profit enhancement [44]. In
business, data value is typically estimated in terms of cost
(e.g. collection cost, storage cost, or cost related to the loss
of the data) and revenue by selling or exploiting the data
[39]. This is exactly what Laney tried to do with his financial
3https://en.oxforddictionaries.com/definition/data (Accessed 1st October
2019)
4https://en.oxforddictionaries.com/definition/value (Accessed 1st October
2019)
valuation models of information [data] [5]. There are also
some definitions of data value which are more general and
can be applied to multiple domains. For example, Khokhlov
and Reznik [45] defined data value as data usefulness. An-
other such definition of data value is: data value is the future
importance of data, it expresses a probability of further use
[46, 47].
Data Value Model - Representations of the value of data,
either as explanatory, descriptive or predictive models. These
representations define the relevant data value dimensions and
relationships between the dimensions that characterise the
value of value to an individual, application or organisation
in a specific context.
Data Value Dimensions - Attributes of the value of data
assets that are relevant to data consumers, maintainers or
owners. Sometimes called data value aspects. Due to the
subjective and contextual nature of value, some dimensions
may be considered to be more characteristic of value than
others, depending on the use case, data asset, or consumer.
Metrics - Metrics are specified quantitative measures of
data or its context that can be used to measure the data
value dimensions of a specific data item. For instance, if we
consider "Usage" as a dimension in a relational database,
a metric that measure this dimension could be "Number of
writes in a day". Metrics can be subjective or objective and
qualitative or quantitative. All metrics can be mapped to one
or more data value dimensions in a descriptive or predictive
data value model. A set of observations of data value metrics
for a specific data item quantify or measure the mapped data
value dimensions for those metrics.
Data Value Quantification - The quantification, assess-
ment or measurement of data value is the explicit calculation
of the the value of a specific data item based on a descriptive
or predictive data value model. It is usually based on a set
of observations of a set of data value metrics. Data value
metrics could be the specific measurable elements that are
used within the process of data value qualification. Data
value qualification involves using these metrics to qualify,
assess, and make judgements about the value of data. Expert
judgement can also be used in subjective and less formal
quantification methods. The focus of this survey is on more
formal methods based on observations which may them-
selves be objective or subjective.
III. REVIEW TECHNIQUE
In this survey, we follow a methodical literature survey tech-
nique with three phases of activities - (i) actively-planning,
(ii) conducting and reporting the review results, and (iii)
exploration of research challenges as per of the widely ac-
cepted guidelines and process outlined in Pai et al. [48] and
Kitchenham et al. [49, 50]. The remainder of this section
details the research question, the process for the identification
of research, and the data extraction process.
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A. RESEARCH QUESTIONS
The goal of this survey is to provide a comprehensive
overview of the literature that provides discussions on the
value of data, and its models, dimensions, assessment tech-
niques, metrics and applications. We therefore define the
following as research questions:
•Q1: What are the existing models and dimensions used
to characterise the value of data?
•Q2: What metrics and measurement approaches have
been created for the quantification of the value of data?
•Q3: In what application domains have data value models
or assessment techniques been applied?
•Q4: What are the issues and challenges facing the data
value research?
The research questions are designed to cover a wide range
of aspects related to data value, from theoretical models and
measurement techniques to practical applications and chal-
lenges, aligning with our goal of providing a comprehensive
overview of the existing literature in this area.
B. IDENTIFICATION OF RESEARCH
Research was identified from the following four electronic
databases: (i) Springerlink, (ii) ScienceDirect, (iii) IEEEeX-
plore, and (iv) ACM Digital Library.
Figure 1 shows the search string used to query these
databases based on metadata (title, abstract, keywords). In the
case of IEEEexplore, the query did not produce sufficiently
accurate results. As such, the query for IEEEexplore was
rerun to both metadata and full texts. In addition to the papers
returned by the above databases, we also included some
handpicked papers [51, 52, 53, 54, 6, 55, 56, 57, 32, 29] that
were recommended by the domain experts which were not
returned by the search string.
(‘Data value’ OR ‘data valuation’ OR ‘infonomics’ OR ‘data asset’
OR ‘value of data’ OR ‘data governance’ OR ‘information value’
OR ‘value of information’ OR ‘information valuation’ OR ‘knowledge
asset’)
AND
(‘assess’ OR ‘measure’ OR ‘evaluation’ OR ‘estimate’ )
AND
(‘model’ OR ‘framework’ OR ‘system’ OR ‘theory’ OR ‘dimension’
OR ‘metric’).
FIGURE 1: Search String
The initial search returned a total of 434 research papers
related to the research topic. A set of inclusion and exclu-
sion criteria, shown in Table 1, were defined to enable the
selection of papers to include in this study to be carried out
in a systematic and replicable manner. In line with Kitchen-
ham [49, 50], three researchers independently screened titles.
The title-based exclusion reduced the number of papers to
121. Then, the papers’ abstracts were read. The abstract-
based exclusion reduced the number of papers to 73. In the
next phase, the full text of the papers was read. In all three
phases, in case of disagreements on whether a paper was
to be included or discarded, discussions were held until an
agreement was reached. Following the application of these
criteria, 63 research articles were included in the final review.
These are listed in Table 2.
C. DATA EXTRACTION
The papers were manually reviewed by the three authors
independently. For each one of the 63 papers, the follow-
ing data was extracted: bibliographic data, the contribution
towards the domain of data value (e.g., data value model,
data value assessment, or data value use case/domain), im-
plemented details, the data value dimensions and metrics (if
applicable) under study, and the type of validation used (if
applicable). The data was then compared and aligned, with
discussions taking place if any inconsistencies were found.
IV. REVIEW RESULTS
In this section, we analyse the papers obtained from our sys-
tematic review. We start by reviewing their bibliographic data
such as number of publications per year and also publication
venues. Then we delve into their content: data value models,
data value assessment metrics, and applications.
A. PRELIMINARY RESULTS
We see in Figure 2 (extracted from Table 2) that most of
the publications (34 papers) are conference papers, 21 are
journal papers, 3 are symposium papers, 3 are workshop
papers and 2 are books. We can also see that the search
in the domain of data value goes all the way back to the
year of 1980. However, most of the selected articles were
published in the period starting from 2003 5. We notice an
increased number of works from a year to another especially
in the last decade. This reflects on the increase of interest in
researching the data value field. The same pattern can be seen
in terms of type of publications where more diversity in type
of publication can be seen in recent years including journals,
conferences, workshops and symposiums. An analysis of
publication venue (in Table 2) suggests that there is no
common venue for publication in this field. In fact, almost
all the selected papers are published in different venues. This
indicates that there is no common consensus in term of venue
for publication in this field. We notice also that the values are
diverse in terms of discipline. Some of them are in computer
science, information systems and engineering (e.g., Interna-
tional Conference on System Sciences, Iberian Conference
on Information Systems and Technologies, Conference on
Management of Engineering and Technology, Conference
on management Science and Engineering, etc.), other are
in science and environment such as Journal of Livestock
Science and Journal of Science of The Total Environment.
One paper was published in the interdisciplinary journal of
space policy.
B. DATA VALUE MODELS
Fleckenstein et al. [39] identified three categories of ap-
proaches to data valuation: market-based valuation, eco-
5Data was only collected until second quarter of 2022
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TABLE 1: Inclusion and Exclusion Criteria
Inclusion Criteria Exclusion Criteria
Full-text Uncompleted studies
Published at any time Non English
Published in the above selected databases Duplicated studies
Published in workshops (W), symposiums (W), conferences (C), books (B),
and journals (J) across all disciplines
Studies that discuss value outside the context of data, information or
knowledge
Papers written in English on the topic of defining, describing, characteris-
ing, modelling, measuring, or quantifying data value
Studies on the value of Information Technology
FIGURE 2: Number of Publications Per Year
nomic models, and dimension-based models. They define
market based as "estimates the value of data in terms of cost
and revenue when buying and selling data or data-intensive
businesses"; Economic models as "estimating economic ben-
efit as a result of making data available [measuring impact]".
The dimension-based approach "examines valuation points
of a specific data set both inherent to data, like data quality ...
and contextual to value [of] data [e.g. data usage]".
This work fits within the dimension-based approach of
Fleckenstein et al.. It focuses on new metrics and dimensions
that are specific to data value and not already known from the
data quality metrics literature. For example, 26 data quality
dimensions and over 80 associated metrics are described
by Zaveri et al. [26] and 21 data quality dimensions are
identified in the recent review of data quality dimensions
by Wang et al. [102]. The most commonly referenced data
quality dimensions in our data value survey sample papers
were accuracy [35] [17], timeliness [35] [51], completeness
[95] [35], latency [71], volume [99] [32] [80] and provenance
[71] [95]. Note that volume in many models has an inverse or
convex value curve in relation to value [17].
The research approach in this paper yielded 19 primary
studies that focus on models including one or more dimen-
sions which characterise data value, as shown in Table 3.
All previously known data quality dimensions [102] [26]
are grouped in the table into "data quality". Four new di-
mensions are identified: Content/Uniqueness, Usage, Utility
and Financial. The most popular dimension cited is related
to examining the content or uniqueness of the data and its
relevance for a task. The Content dimension of data value
was first identified by Even and Shankaranarayanan [32] in
2005. Usage is a well established dimension of value since
[17] defined it as a distinguishing feature of data value and
classed as part of the context of value by Fleckenstein et
al.. Utility is perhaps the original dimension of data value
[31]. We extend the dimensional approach to include one
market-based model aspect of Fleckenstein et al., a financial
dimension that represents measurable aspects of data within
an organisation like cost or price.
As can be seen from the table, most current models of data
value are limited in their perspective since they only focus on
a subset of the data value dimensions, such as the financial
dimension, and they do not provide a comprehensive view of
how dimensions are related. One feature of Table 3 is that the
majority of models address three or less dimensions of data
value.
It may be asked are any of these dimensions antecedents
of (factors that influence) value rather than dimensions of
value itself. Examining Wang’s original antecedents (man-
agement responsibility, operation and assurance costs, re-
search and development, production, distribution, personal
management, and legal) it seems there is some overlap, e.g.
in production costs. However this neglects the contention of
Fleckenstein et al. and Laney[5] that data value, even in a
dimensional approach, is a wider concept than data quality.
Additionally, metrics have been identified in this search for
all of the dimensions, and this suggests that they are directly
useful for data value calculations.
In the following subsections we provide an overview of
the models covered in the mentioned primary studies, based
on the main dimension that, according to the authors, the
data value would contribute towards. For each dimension we
provide a table of data value assessment metrics identified in
the search to facilitate reuse in new applications or further
research. As has been long established for data quality, to
assess specific data assets, it is necessary to define metrics to
quantify or measure data value dimensions [103]. Only about
half of the selected papers (31 out of 63) provide specific
data value metrics and this shows the relative immaturity of
the field. In the tables below the metrics are classified as
being suitable for subjective or objective measurement in the
“Type" column. In total 44 metrics for data value assessment
beyond those typically used for data quality assessment have
been identified and allocated to data value dimensions. The
Utility, Content-Uniqueness, Usage and Financial dimen-
sions have 14, 10, 10 and 9 metrics identified respectively.
These are candidates for the biggest departure from tradi-
tional data quality dimensions for data value assessment.
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TABLE 2: Final Selection of Publications. J:Journal, C:Conference, S:Symposium, W:Workshop, B:Book, BC:Book Chapter
No Title Authors Type Year Venue
1 A Systematic Approach toward Assessing the Value of an Information System Ahituv J 1980 MIS quarterly
2 Measuring the Value Of Information-An Asset Valuation Approach Moody and Walsh C 1999 European Conf. on Information Systems
3 Information value mapping for fusion architectures Peterson and
Sundareshan
C 2000 Int. Conf. on Information Fusion
4 Improving Data Quality in Practice: A Case Study in the Italian Public Administration Missier et al. 2003 Distributed and Parallel Databases
5 Situation-based message rating in information logistics and its applicability in collabora-
tion scenarios
Meissen et al. C 2004 Euromicro Conf.
6 Information Valuation for Information Lifecycle Management Chen C 2005 2nd Int. Conf. on Autonomic Computing
7 Value-Driven Data Quality Assessment Even and Shankara-
narayanan
B 2005 ICIQ
8 The value of information: Measuring the contribution of space-derived earth science data
to resource management
Macauley 2006 Space Policy
9 How to calculate information value for effective security risk assessment Sajko et al. 2006 J. of Information and Organizational Sciences
10 Information Assets and their Value Engelsman C 2007 Not a Peer Reviewed Paper
11 File valuation in information lifecycle management Turczyk et al. C 2007 Americas Conf. on Information Systems
12 Therole and value of information for supply loop management: Framework and application
for the end-of-life cell phone industry
Doctori-Blass and
Geyer
S 2008 IEEE Int. Symp. on Electronics and the Environment
13 A Hierarchical Storage Strategy Based on Block-Level Data Valuation Zhao et al. C 2008 Int. Conf. on Networked Computing and Advanced Infor-
mation Management
14 An approach for quantifying enterprise value of information (EVI) Lu and Zhu C 2009 Int. Conf. on Management Science and Engineering
15 The value research on information resources management of supply chains Dang et al. C 2009 Int. Conf. on Management Science and Engineering
16 Block-Level Data Migration in Tiered Storage System Zhao et al. C 2010 Int. Conf. on Computer and Network Technology
17 Chapter 28 - Measuring EIM and the Value of Information Ladley BC 2010 Making EIM Enterprise Information Management Work
for Business
18 Understanding the dynamics of information management costs Tallon 2010 Communications of the ACM
19 A Framework to Assess Value of Information in Future Vehicular Networks Giordani et al. W 2010 Workshop on Technologies, models, and Protocols for
Cooperative Connected Cars
20 Asset Analysis of Risk Assessment for IEC 61850-Based Power Control Systems—Part I:
Methodology
Liu et al. 2011 IEEE Trans. on Power Delivery
21 Disentangling the Value of Information and Analytics through Componentised Business
Architecture
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6VOLUME 4, 2016
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Three metrics are used in more than one dimension (rival
access loss, camera resolution and market price). Camera
resolution is an example of a common sensor-based metric
that can quantify the likely information content of data and
therefore its value.
Moreover, we also provide a distinct section for market-
based VoI models (12 papers), which we follow Fleckenstein
et al. to be a separate type of data model (economic) since
they focus on cost/benefit difference in outcome as opposed
to specific dimensions of data value.
TABLE 3: Data Value Dimensions included in Data Value
Models Studied
Publication Economic Content Quality Usage Utility
Shimazu et al. [73] ✓
Zhang et al. [92] ✓
Hanratty et al. [71] ✓ ✓
Schuh et al. [78] ✓ ✓
Yao and Atkins [87] ✓ ✓
Hofman et al. [100] ✓ ✓
Mayle et al. [77] ✓ ✓
Zhao et al. [54] ✓ ✓
Sonobe et al. [82] ✓ ✓
Yanlin and Haijun [38] ✓ ✓
Jia et al. [52] ✓ ✓
Tan et al. [81] ✓ ✓
Sajko et al. [56] ✓ ✓ ✓
Li et al. [83] ✓ ✓ ✓
Qiu et al. [80] ✓ ✓ ✓
Viscusi and Batini [14] ✓ ✓ ✓ ✓
Lu and Zhu [63] ✓ ✓ ✓ ✓
Laney [5] ✓ ✓ ✓ ✓ ✓
Ahituv [31] ✓ ✓ ✓ ✓ ✓
1) Economic or Financial-based Models
The financial or economic dimension is one of the most pop-
ular dimensions used to determine the value of a data asset.
This is probably due to the tangible nature of this aspect.
This dimension is based on the metrics included in the “Ac-
countancy Valuation Models” for data (information) value
of Moody and Walsh [17] or the financial models of Laney
[5] which includes the realised or potential cost of data, the
market value of data, and the present financial value of data.
Zhang et al. [92] propose a theoretical capitalisation of data
assets, based on the historical cost method, the fair value, and
the current value. Li et al. [83] focus on data pricing, where
an entropy-based method is proposed to measure the value
of a dataset based on size and information content. A pricing
function is then provided to convert from entropy-based value
to price. Mayle et al. [77] propose a game-centric model of a
private data exchange in return for a service. The model takes
into account the priority given to data items by users and the
monetary value given to users in return for their data. Schuh
et al. [78] model an extended, economic value definition
which is proportional to the benefits and costs associated
with the product or service. Based on “technological value
contributions”, the model supports manufacturing companies
in evaluating if a generic set of field data generated by a smart
product provides value to the user.
2) Content Uniqueness-based Models
This dimension was identified by Even and Shankara-
narayanan [32] and Viscusi and Batini [14]. Measurement
can be derived by assessing the content and its applicability
for business use cases. The research identified here proposes
measurement methods that link impartial characteristics and
contextual perception to measure the potential business value
associated with the data. This is related to but distinct from
utility metrics which measure the value of the data in in use.
Uniqueness is an important aspect of content for value and
is a characterisation of the value of data based on rarity [13]
or scarceness [14] of the information contained in it. Yao and
Atkins [87] propose Smart Black Box (SBB) data compres-
sion decision making based on data value, calculated on data
novelty and events. Shimazu et al. [73] on the other hand take
a value-based approach towards setting data confidentiality.
Their paper defines a method for setting data confidentiality
based on risk, taking into account data value, protection level,
and threat level.
3) Quality-based Models
Data data quality dimensions are tied with Data Content
Uniqueness as the most common way identified to quantify
the value of data assets, and can include aspects in the Data
Quality Model defined in the ISO/IEC 25012 Standard6. To a
certain extent this is because the quality concept of "fitness
for use" is closely aligned with the concept of value, for
example possessing the same strong dependence on context
of use. Measurement can be derived by assessing the dataset
directly and its conformance to standards or applicability
for business use cases. Fleckenstein et al. [39] describe the
dimension-based approach to data value as an of extension
of data quality methods and tools. Given the wide range of
data quality metrics well known to the community we do not
provide a table of them here but instead refer readers to other
surveys of data quality metrics such as [26]. In Hanratty et al.
[71] base their definition of data value on data quality related
aspects, namely content reliability, trust of source, and time-
liness. Intended for the military decision-making domain, the
authors propose a fuzzy-logic-based approach that valuates
data.Hofman et al. [100] propose an evaluation framework
for data quality and value assessments. The authors explore
data quality categories and dimensions for assessing the po-
tential value of linking different customs data sets and linking
a business dataset to a customs data set. The categories
of data quality used were contextual (including relevancy,
value-added, timeliness, completeness, and amount of data as
dimensions) and representational (including interpretability,
ease of understanding, concise representation and consistent
representation).
4) Usage-based Models
Authors such as Moody and Walsh [17] argue that data
has no intrinsic value, yet it only becomes valuable when
6http://iso25000.com/index.php/en/iso-25000-standards/iso-25012
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TABLE 4: Data Value Metrics for Financial Dimension. S:Subjective. O: Objective
Metric Description Type
Compliance cost Estimated financial cost to business of not keeping data for compliance/regulation purposes [56]. S, O
Fixed record value A count of records (see data volume metrics) with a fixed value per record can be used [32]. S, O
IP value For intellectual property data assets whose value is directly proportional to its financial value (e.g. licences or
patents) [56].
S, O
Maintenance cost Estimated financial cost to business of maintaining, adapting or curating data [96] . O
Market price Price that a user will accept for data, sometimes in return for a specific service [77, 62] O
Replacement cost Estimated financial cost to create, replace or reproduce this data [56, 22, 17]. S, O
Revenue loss Estimated financial cost to business of not having access to data [56, 62] or a service based on observations in
the data [101].
S, O
Rival access loss Estimated financial cost to business of competitors having access to data [56]. S, O
Storage cost Estimated financial cost to business of storing data [96, 30]. O
TABLE 5: Data Value Metrics for Content-Uniqueness Dimension. S:Subjective. O: Objective
Metric Description Type
Camera resolution For visual sensors a measure of the potential accuracy of sensor readings from a specific source [51]. O
Customer lifetime value Data records that describe or refer to specific customers, for example business transactions with that customer,
may be weighted. Typically used with count of records [32].
S, O
Field value The financial amount of a transaction recorded in a field of a data record may be used as a weight for the value
of that record. Typically used with count of records [32].
O
Image quality An estimate of the quality of an image based on camera type (smartphone type), brightness, contrast, resolution,
noise, and exposition [95].
S, O
Data information entropy A measure of the uniqueness of data in the dataset [83]. O
Event information measure By classifying the contents of an image or sensor reading to a set of events with known probabilities, can
calculate the information measure of the image containing a specific event [87]. Alternatively [69] specify a
Fisher Information Matrix (FIM) based metric.
O
KNN Shapley value For K-nearest neighbours classifier algorithms Jia et al. provide an efficient Monte Carlo method to calculate
the Shapley value of the KNN utility of each data point [52].
O
Lexical semantic uniqueness A measure of the fraction of most unique words in a file compared to the contents of the whole collection [80]. O
Mutual information A qualification of the inferential gain by increasing the size or features of data when comparing two datasets
[99].
O
Scarcity The probability that other organisations (particularly competitors and partners) have the same data [5]. S
people use it. Following this argument, the more the data
is used, the more valuable it becomes. This is based on the
economic concept of “value in use”. This dimension is a
characterisation of how often or by whom [72] a dataset is
used. It was developed by Chen [22] into a set of concrete
metrics. Qiu et al. [80] propose a data value measuring
algorithm for data migration applications based on the usage
of the data, such as the access time, and the data read and
write frequency and access, and other content related aspects
such as data size and file content. Zhao et al. [54] propose a
model that values data blocks based on the timeliness of the
data, data distribution and usage, and the association between
blocks, as well as other usage related characteristics such as
read and write frequency and granularity. Yanlin and Haijun
[38] derive a data asset valuation framework based on data
asset production cost and data asset spillover value, where
the data is considered to be more valuable the more it its used,
particularly if it is used in a multidimensional manner.
5) Utility-based Models
Utility characterises data value in terms of value in use and
the benefits, usually business oriented, that can be derived
from it [17, 14, 104]. This dimension is often used to clas-
sify data value metrics which are very specifically tied to
a particular application, service or business process and in
many cases a specific dataset. Sonobe et al. [82] define a
data value model based on a utility function derived from
seller/producer estimates, with the aim of enabling rapid flow
of the most valuable data in a disaster recovery situation. Jia
et al. [52] also use a utility function in an attempt to answer
how much each data point is worth for machine learning
models based on a model of the relative value of data derived
from its Shapley Value from game theory. Tan et al. [81]
derive an analytic formula for information value assessment
based on utility functions, data type classification, informa-
tion uncertainty, and the willingness of different actors to pay
for more information based on their risk preferences.
6) Multi-dimensional Models
Laney [5] discusses a number of “Information Asset Valu-
ation Models” i.e. a set of approaches for calculating the
value of data assets. Compared to the previous models,
Laney’s models are more comprehensive as they comprise
a larger number of dimensions. Laney [5] divides the models
into fundamental models and financial models. Fundamental
models are mostly based on the content of the data, including
the utility, business usage, impact of information, and quality
aspects, such as validity, completeness, integrity, and con-
sistency, and are claimed to be better for information asset
management applications. The financial models are based
on measures of money and accountancy and claimed to be
better for assessing an information asset’s business benefits.
Based on the model definitions provided, Laney [5] uses
more of an expert-based approach, as opposed to automated
data valuation. Whilst a great basis for further research, these
models in many cases lack the definition of concrete metrics.
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TABLE 6: Data Value Metrics for Usage Dimension. S:Subjective. O: Objective
Metric Description Type
Access interval A time series measure weighted towards data most recently created. [80]. O
Frequency An access time series measure based on how often data is read and written to [80]. O
Number of accesses A measure of the degree of usage [22]. Moody et al. suggest this metric [17]. O
Potential user count An estimate of the number of similar users in the system compared to the ones that have accessed the data, that
are hence likely to also access the data [80].
S, O
Purpose A measure of the significance of the type of usage [22]. S
Read write profile Data valuation based on read and write granularity, data distribution, and data association [54]. O
Recency A specialised time-based metric to capture when data was last used, defined by [22]. O
User count Called the number of visits by Qiu et al., the number of unique users that access the data [80]. O
User role [72] indicates user role is a measure of the significance of the usage. S, O
Usage over time A quantification of value based on the usage of the data over time [22]. O
TABLE 7: Data Value Metrics for Utility Dimension. S:Subjective. O: Objective
Metric Description Type
Fidelity Usefulness of sample data for a given inferential task [99]. O
Goods condition For end of life processing (recycle, parts reuse, product reuse) need estimate of goods condition [62]. S, O
Market price Price that a user will accept for data, sometimes in return for a specific service [77, 62] O
nKudos Number of approvals (“kudos”) given to a message by the community [74]. O
Personal data differential price A measure that calculates the relative utility of a dataset by comparing the differences between original data of
a personal nature, and an obfuscated version of the same data [55].
O
Provider assessment An assessment of the value of a data item by the provider of that data [82]. S
Relevancy The number of business processes that use, or could benefit from this data [5]. S, O
Revenue Revenue increases or decreases with or without access to the data [5, 97]. S, O
Rival access loss Estimated financial cost to business of competitors having access to data [56]. S, O
Service outcome Reduced or improved service outcomes with or without access to the data [62]. S, O
Safety distance For vehicular sensors a measure of the distance where sensor readings become increasingly important. [51]. O
User assessment An assessment of the value of a data item by the user of that data after consuming it [82]. S
User priority Priority given to a data item by a specific user [77]. S, O
Similar to Laney, Viscusi and Batini [14] propose a more
comprehensive model for digital information asset evalua-
tion. The authors consider information value to be based on:
information capacity (comprising information quality, infor-
mation structure, and information infrastructure) and utility
based on information diffusion. The authors base their model
on the assumption that information value can be quantified
either on the basis of information utility of the IT capabilities
enabled by a data asset, or otherwise on the basis of the
overall capabilities the data asset may provide in the initiative
in question.
Lu and Zhu [63] also consider a number of dimensions in
the evaluation of Enterprise Value of Information (EVI), and
construct an evaluation model of EVI based on a combination
of a qualitative and quantitative approach. The authors pro-
pose an EVI evaluation index system that takes into consid-
eration information authenticity, timeliness, degree of cover-
age, degree of relevancy, degree of superposition, manager’s
subjective consciousness, information flux, and information
cost. Whilst only the latter two are quantitative indexes,
all the previous are categorised as qualitative. The authors
therefore use the cloud model evaluation to translate the
qualitative indicators into the quantitative target.
Ahituv [31] defines a joint utility function that includes a
set of dimensions for information value as follows: timeliness
(including response time and frequency); contents (including
similarity and aggregation level); format (including medium,
ordering, and graphic design); and cost. Albeit the author
provides this list of attributes to demonstrate an approach,
the author points out that he does not intend this list to be
exhaustive.
In summary, a wide range of data value dimensions are
defined by existing models but only the models in [5] , [14],
[63] and [31] define a broad multi-dimensional approach to
data value and in each case the dimensions selected differ.
7) Value of Information (VoI) Models
Keisler et al. [105] define the value of information (VoI)
as “a decision analytic method for quantifying the potential
benefit of additional information in the face of uncertainty".
It is often used in decision support systems and automated
sensor data fusion applications. Thus it can be seen that
VoI is a term used for a specific type of data value in the
literature. Since VoI calculation is always specialised to a
specific decision and application domain, there are a wide
range of VoI models and assessment methods in the literature.
Its application depends on having an objective function to be
maximised and a choice between courses of action leading to
uncertain payoffs [106].
A popular approach is to define VoI in terms of monetary
values related to the costs and benefits of the use case in
question. The monetary definition of VoI corresponds to the
difference between the cost for acquiring/collecting new data
and the benefit that this would create in terms of reducing
uncertainty in decision making and improved business gains.
For instance, Koski et al. [53], Macauley [61], and Rojo-
Gimeno et al. [88] define and estimate VoI as a monetary
value in three different application domains. Doctori-Blass
and Geyer [62], and Dang et al. [64] also define VoI as
monetary/economic value for the specific domain of supply
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chain. While the aforementioned papers aim to provide a
single/global assessment of VoI, Giordani et al. [51] and
Fauriat and Zio [86] aim to assess the VoI of different data
sources separately, in an attempt to identify the most relevant
information and prioritising their acquisition. However, in the
former, the definition of VoI in is not based on monetary
value. Instead, it is based on an aggregation of three domain
specific attributes (i.e., a weighted-sum of source proximity,
timeliness, and quality). Santos et al. [84] and Hanratty et al.
[71] aim at improving the accuracy and applicability of the
defined VoI for their respective use cases (i.e., reservoir de-
velopment and military operations management). [89], [94],
and [75] discuss the large computational complexity that is
associated with computing and optimising VoI in various
domains and propose techniques to improve the efficiency
and the time complexity of these processes.
In summary, the literature on VoI is relevant for discussing
more general concept of data value but VoI is limited to
creating application-specific data value models where it is
possible to efficiently calculate or measure the expected
value of perfect information (EVPI), the expected value of
sample information (EVSI) and the expected net gain of
sampling (ENGS) and this is not possible in many real world
deployments.
C. APPLICATION DOMAINS AND USE CASES
Approximately two thirds of the papers (45) had a specific,
detailed application domain or use case for data value. A
thematic analysis of these papers is provided in Table 8.
This helps to better understand the current application areas
for data value and it identifies gaps for further work or
opportunities for cross domain exploitation of existing re-
sults. The most common application areas identified were, in
order of frequency of occurrence: information management,
sensors and monitoring, security and privacy, information
(data) pricing for data markets, and business decision making
support. Each of these areas is discussed in detail below.
1) Information Management
This is the largest application area identified with 15 papers
on this topic. Within this theme the most common sub-topic
is a grouping of 10 papers in the discipline of information
lifecycle management (ILM) looking at data migration, data
storage, and file management. This theme also includes the
related fields of enterprise information management and data
quality management (3 papers).
ILM is an industry term for managed dynamic and efficient
storage resource management for the increased digital data
being managed given the availability of multi-tier storage that
trades access time for cost and the increasing prevalence of
data legislation forcing compliance [30]. Central to the ILM
approach is the idea that “not all corporate information [data]
has the same value and values change over time”[6]. Chen
[6] identifies the three key ILM tasks as information valua-
tion, information characterisation & classification, and task
prioritisation & optimisation. Given the relatively early date
of Chen’s work (2005), the ongoing expansion of corporate
data and the key role played by data valuation in ILM, it is
not surprising that there is a significant body of work in this
field.
Automating ILM is dependent on defining data value met-
rics that can be executed either in realtime or periodically to
enable files [72], blocks [65] or other data [30] to be shifted
between storage types or deleted. The relatively high avail-
ability of file metadata [6] and access information [57] char-
acterising the numbers of data accesses means that both of
these have been exploited by authors. Some authors caution
that that metadata-driven approaches can require great effort
to collect and maintain appropriate metadata and instead
propose probabilistic approaches to metrics [57]. Despite the
progress on data value assessment for ILC, Wijnhoven et al.
[72] conducted a large scale comparison of subjective data
value assessments conducted by experts using Sajko et al.
[56] questionnaire and automated assessments and found a
poor correlation. However, when identifying “wastage", the
least valuable files, they found that automated methods had
an 80% accuracy, far better than when identifying the most
valuable files.
Given the commercial importance of data retention, the
works often include extensive evaluations in real-world set-
tings. For example, Turczyk et al. [57] describe a case study
that generates file migration rules for 150,000 files, whilst
Wijnhoven et al. [72] conducted a case study with 77 em-
ployees of Capgemini Netherlands. Many of the studies pro-
pose domain specific file or block-based data value metrics
that would be difficult to re-purpose for other use cases or
domains [54]. However the work has matured over the last
decade and fully automated ILM systems are now a reality
in data centres and so the focus has switched from data value
metrics to decision algorithms based on the metrics enabling
fully automated ILM [80].
Enterprise information management [66] has largely
grown out of the discipline of records management which
focuses on managing information for legal compliance and
supporting efficient operations of the organisation (gover-
nance). There is a growing number of records or data sources
in organisations and a prioritisation mechanism is needed
to deal with them most effectively, given limited resources.
However this work is less mature than ILM use cases. Ladley
[66] presents a list of loosely defined metrics and ideas on
how to measure the value of information assets for Enter-
prise information management. Tallon [30] proposes a tiered
information framework that, by considering the value of in-
formation allows CIOs to comprehend the interplay of market
forces that shape information costs. Laney [5] devotes a chap-
ter to EIM driven by thinking of data as an asset. He states
that most EIM metrics are for justifying, funding, prioritising
and gauging the success of initiatives for either managing
data and business initiatives that use the data. This provides
extensive use cases for data valuation in EIM. Unfortunately
the metrics presented by Laney are less formally specified
and thus hard to operationalise. Finally there research on
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TABLE 8: Thematic Areas for Data Value Application Domains.
Information Management Sensors and Monitoring Security and Privacy
Information lifecycle management (4) Environmental monitoring (1) Security risk assessment (3)
Data migration/storage (4) Sensing for earth sciences(1) Security, crowd sensing, privacy (1)
File management (3) Livestock production (2) Privacy, data markets (2)
Enterprise Information Management (2) Sensor fusion (1) Privacy, IoT markets (2)
Data Quality Management (1) Smart Vehicles (2) Privacy, data brokers (1)
Data Markets and Information Pricing Decision Making Other Applications
Privacy, data markets (2) Petroleum Reservoir development Web Resource identification
Information pricing (2) Military decision-making (1) Information overload (1)
Data markets, disaster relief (2) Supply chain management (3) Multi-agent communication (2)
Machine learning (1) Social media (1)
value-driven data quality management (a sub-topic of EIM)
which, has strong metrics since it aims to enable automated
processing of data [32]. Given the importance of data quality
to modern machine learning and analytics pipelines, this is a
promising topic for further research.
2) Sensors and Monitoring
Eight papers were identified as applying data value to sen-
sor deployment or communication and monitoring systems.
Topics discussed include a sensing system for earth sciences
like satellites [61], environmental monitoring such as lake
water quality[53], livestock production [88], sensor fusion
architectures [58] and vehicular networks [51].
Many of the papers make use of the Value of Information
(VoI) measure discussed above. In most cases probabilistic
VoI approximation techniques are used, e.g. Bayesian [61]
or Monte Carlo [53], due to the computational complexity
of determining VoI exactly in realistic situations. Macauley
[61] summarises that the value of data is based on “(1) How
uncertain decision makers are; (2) What is at stake as an
outcome of their decisions; (3) How much it will cost to use
the information [data] to make decisions; and (4) The price of
the next-best substitute for the information [data]". It can be
seen that answering these questions for specific domains can
inform decision-making about data acquisition or use. These
domain-specific answers limit the re-usability of VoI methods
in other domains. For example Cornou and Kristensen [25]
assign a value to knowledge of pig drinking behaviour that is
very closely tied to their use case.
The key applications of data value estimates for these
papers are to assess the worth of paying for additional
monitoring capacity or information sources (justifying ICT
system expense), optimising deployments of mobile sensors,
minimising inter-sensor communications network load, and
optimising data storage in low resource devices. In most of
these applications the data value estimate can be used to
prioritise data collection or communication activities. Thus,
utility-based VoI models dominate. However, for monitoring
system deployment scenarios [61], [25], [88] economic ben-
efits are also important as the decision is a longer term one
about investment rather than short-term automated decision
about mobile sensor movement [69], data stream selection
[58], message transmission [51], or data storage [87].
3) Security and Privacy
This section discusses the eight papers that were classified as
dealing with data value applications for security and privacy.
There is a strong distinction between the two application
areas: for security data value was used as a part of the
risk assessment process [73] [56] [95] [67]; for privacy the
emphasis was on placing an economic value on the private
data that users exchange in return for “free" services [24] [70]
[76] [77]. Within the privacy papers the topic of IoT (Internet
of things) was seen as an important sub-application area for
two of the papers. One paper was a cross-over that considered
both security and privacy aspects in security system design
influenced by data value [95].
In security risk assessment, for example as defined by the
ISO 27705 standard, a key question is prioritisation of data
assets to be protected by security systems. Identifying the
most valuable assets has long been understood as part of
this process [56]. This encourages consideration of a multi-
dimensional view of data value. For example the early work
of Sajko et al. [56] examined assessing timeliness, utility,
replacement cost, legislative risk, and competitive advantage
through a qualitative, questionnaire-based approach. Not all
assessments are qualitative though, as Shimazu et al. [73]
propose a quantitative assessment of risk using the value of
information [data value], protection level, and threat level.
Khokhlov and Reznik [95] provide mappings between data
quality metrics and data value as a further extension of the
quantitative approach. They also show how the computed
data value can be used to influence security system design
for a crowd-sensing application and other one in TOR (The
Onion Router) network including privacy protection. Liu
et al. [67] extend ISO27005-style risk assessment of data
assets to assets in power control systems (smart grids).
The privacy related studies use economic (monetary) data
value assessments (sometimes called “privacy valuation") to
drive competitive data trading [24] [70], or to raise users’
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awareness of the value of personal data by providing trans-
parent access to the value realised by service providers and
data brokers [76] [77]. Game theoretic models play an impor-
tant role in these markets whether trying to drive the market
[24] or provide more transparency for users [77]. The data
value model of Yassine et al. [70] is notable because instead
of more usual data value dimensions it uses “risk of sharing
private data" as a dimension of data value. The privacy data
value papers are collectively characterised by a lack of strong
implementation or evaluation. This may be due to the relative
cost of integrating with many data broker platforms [76] or
the immaturity of deployments of the IoT technology being
discussed [24], [77].
4) Data Markets and Information Pricing
This topic was addressed by only seven of the papers anal-
ysed. This is interesting as data markets and pricing, i.e. a
direct measure of the economic value of data, have received a
lot of commercial attention in recent years but comparatively
little seems to have been published on this topic, at least in the
technical venues that we examined. Pricing for sale was seen
as an end in itself by only two of the papers [83] [55] whereas
three papers deal with data pricing in the context of privacy
as a way of raising the awareness of users or increasing
transparency of the transaction taking place between users
and platform or service providers [24] [70] [76]. We also
group in this theme methods that place an emphasis on data
trading or data markets, even when they are not evaluated
financially. This could be considered to include many of the
“Value of Information" papers that are based on a games
theoretic approach to the concept of a market for information.
However there are also more concrete market mechanisms
specified for encouraging data transfer in disaster situations
[82] and markets for personal data [24]. Finally there is one
paper discussing training data pricing for machine learning
models [52].
Li et al. [83] describe a data pricing strategy based on
information entropy and give a useful overview of different
existing pricing strategies: subscription, query-based pricing,
and bundling/discrimination-based pricing. It claims none of
these are actually based on assessing the information value or
contents of a dataset and presents a new information entropy-
based way to measure the value of a dataset based on two
value dimensions: size and information content. It provides
an interesting evaluation of information content based on the
ability of a dataset (or subset) to train a classifier and this is
tested on six research datasets. A set of three example pricing
functions to convert from entropy-based value to price are
also provided and validated in use cases. Rao and Ng [55]
instead define a utility-based pricing mechanism, however
unlike most utility measures they define a method to estimate
the utility of data before and after obfuscation for privacy
purposes using Kolmorgorov statistics. This is applicable to
personal information but it is unclear how other applications
of the approach would work.
Jia et al. [52] seek to answer how much each (additional)
input training data point is worth for machine learning mod-
els based on a model of the relative value of data derived from
its Shapley value from game theory. The Shapley value de-
fines an optimal distribution of value to resources in a multi-
actor system such as a market. Since this is very expensive
to calculate (O2N) the paper develops an approximation for
k-nearest neighbours (KNN) machine learning models and a
more practical Monte Carlo approximation algorithm. This
analysis is based on having a utility function for the use of
the data, which is often hard to estimate. They provide a
classification of types of data valuation problems. The paper
is significant as they explore the issues of data valuation at
scale as they seek methods to assign value to each individual
data point rather than an entire data-set or information source.
Given the importance of data for ML methods, it is to be
expected that there will be more work in this direction in the
future.
5) Decision Making and Other Applications
This is a catch-all application domain category with 11
papers. In practice data value techniques are usually applied
as an input to some form of decision making: should I use this
data? do I need more data to make this decision? and so forth.
The only sub-topic that contains multiple papers are supply
chain management with 3 papers [62] [64] [35] but other
areas include military decision making [71], insurance in-
dustry case studies [68] electricity markets [91], information
overload [60], petroleum reservoir development [84], smart
grids [81] and identifying relevant content in social media
[74]. Several of these papers are based on VoI measures
discussed above and do not contribute anything unique their
domain application.
Supply chain management is significant in having a num-
ber of papers and the fact that two of them are relatively
early (2000s). Dang et al. [64] say “Information resources
management is the basis of supply chain" and that this is a
source of competitive advantage. They propose the concept
of a value chain of information resources that parallels their
own focus on physical supply chain management. Viet et al.
[35] provide a structured review of the value of informa-
tion in different supply chain decisions focusing on articles
between 2006 and 2017. They find that the focus has been
on data availability rather than data characteristics (quality
dimensions). They lay out a research agenda. The two most
relevant questions for our study are: more research is needed
on information characteristics (data quality dimensions) and
a need for new methods to assess the value of data. They
highlight the importance of enabling multi-method modelling
approaches and so semantic models of data value such as
DaVe [27] could have an important contribution there.
Other significant papers include Glissmann et al. [68] who
create a model to dissect data value from the value generated
by analytics in business decision-making in the context of
an insurance industry example. The approach is based on
models of business operations and the way information (raw
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and analytical) is connected to the organisation and its busi-
ness priorities (business architecture/enterprise architecture).
This is a tempting model for data governance operations
but it is very unlikely that businesses will have an up to
date catalog of their data assets or comprehensive business
process models in a machine-readable format due the relative
immaturity of data governance in most industries [5]. Scaffidi
[74] discussed the use of data value measures to support the
automatic identification of valuable Web resources in social
media. This use of data value as a proxy for “importance"
or “interest" is surely an area of overlap with other fields
such as information retrieval and recommender systems that
seek to satisfy a user’s desires by supplying their data needs.
More work is required to examine this overlap and identify
paths to cross-fertilise the research in both areas. Scaffidi’s
approach is interesting in that it depends on collecting both
positive and negative examples of value and highlights the
importance of authorship of content for value (which is
linked to Wijndhoven et al.’s idea of incorporating the role
or seniority of the people accessing the data in their usage-
based value model for files [72].
Laney [5] discusses techniques for applying data value
measurements in a business context in chapter 11. First he
introduces data value improvement through the idea that
there are three degrees of value: i) realised value (current
economic benefits), ii) probable value (based on intended
uses) and iii) potential value (if optimally applied). The
“information performance gap" is then defined as the dif-
ference between realised and probable value. It is suggested
that his Market Value of Information (MVI) and Economic
Value of Information (EVI) models can be used to calculate
realised and probable value. The “information vision gap"
is then defined as the difference between probable and po-
tential values. Laney indicates that is is harder to estimate.
He suggests using Business Value of Information (BVI)-
based actual versus potential valuations as a prioritisation
technique. Unfortunately, the estimation of BVI is based
on a crude estimate for relevance, which limits its applica-
bility. A set of use cases for business decision-making are
identified and a decision method based on the application
and comparison of multiple of Laney’s data value models is
defined. The use cases and methods are as follows: priori-
tising information asset management initiative investments,
proving benefits of information governance, innovation and
digitisation, monetisation and analytics, to help building a
business case for monetising information assets, reducing
information lifecycle expenses.
All of these methods are currently limited by the subjec-
tive models available, yet they represent significant insight
into the types of decision-making possible and the relevant
aspects of data value that we need be able to understand and
quantify in order to maximise its utility.
V. RESEARCH AGENDA
The review results showed that despite [107] identifying
“Data value measurement" as a key challenge for the future
scope of data governance in their well received review [107],
there are still many challenges that are hindering the efficient
and effective exploitation of the concept of data value. We
have identified the following three main challenges for data
value research: a common conceptual framework, reusable
assessment methods and tools, and future applications. Table
9 summarises the open research questions and topics of
interest.
A. COMMON CONCEPTUAL FRAMEWORK
The first major challenge observed in the literature is the
absence of a common conceptual framework for data value
that sets a common basis for communication, research, and
tool interoperability in terms of data value applications,
context, stakeholders, models, terminology, and data value
dimensions [35]. Currently, most data value research is use-
case or application focused (see Table 8) and identifies its
own terminologies, models, and even the type of value they
seek to derive from data. There are opportunities to develop
data value ontologies [27], catalogues and other online re-
sources to make terminologies, metrics, use cases and data
value test data easier to exchange and reuse –similar to the
efforts that have been done in the data quality engineering
field [26] [102].
The few unified, multidimensional data value models like
[14] are incomplete when compared with scope of data
value identified in this review. At present the infonomics
models of Laney [5] come closest to this ideal but many of
their underlying metrics rely on subjective judgement and
are not suitable for scalable, automated decision making.
Fleckenstein et al. [39] explains that despite the utility of
their framework of three types of data value models (market-
based, economic and dimensional) that the "three models
overlap with each other" and issues like regulation and pri-
vacy affect all models. All of the models are immature and
lack a consistent way to represent the data value context
which is external to the data and yet part of the data value
assessment. This hinders the development and deployment
of tool chains or frameworks for data value assessment and
management or novel applications of data value to driving
decisions in new domains like data governance [28]. A key
problem for any unified models is how to reconcile, combine,
and convert between financial models of data value measured
in currency and scalar metrics typically measured in the range
0-1 [108]. Lastly, since there are several dimensions that
could be included as part of a data value model, it is cru-
cial that models provide machine-readable methods to score
importance and provide means to aggregating the relevant
dimensions and explaining their impact on individual use
cases. Fleckenstein et al. [39] describes one model but it is
human-oriented rather than addressing automation directly.
B. REUSABLE ASSESSMENT METHODS AND TOOLS
It is critical that the data value community has access to
known, reusable techniques and tools for data value assess-
ment.
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Although this paper has presented the most comprehensive
collection of data value metrics to date (see Tables 5, 4, 6, 7,),
the coverage of individual data value dimensions has high
variance. Many metrics are extremely specific to particular
applications, especially in VoI models (see Sec. IV-B7).
The relative frequency of subjective as opposed to objective
metrics in the literature [56, 5, 71, 32, 82, 95, 80, 62] and the
significant number of data value metrics based on abstract
and idealised utility functions [82, 52, 81, 75, 94] that are
hard to connect to real applications, shows that data value
assessment is still immature. Objective metrics are also more
suitable for automation and the creation of reusable assess-
ment tools, as has long been common in the data quality
domain [26]. As Viet et al. [35] says there is “a need for new
methods to assess the value of data".
As well as lowering costs, common tools and metrics
foster reproducibility of research findings and will facilitate
the creation of open competitions and technical challenges
by the research community. At present, the proliferation of
individual assessment tools and models ensures divergence
of results and duplication of effort.
Furthermore, although several metrics already exist for the
assessment of data value metrics on static data, we live in
the big data era with constantly updated data streams, yet
there is little research to date on data value metrics for this
domain. It is key to accelerate stream data value research to
both analyse the applicability of existing works and propose
novel assessment techniques that will dynamically capture
the evolution of data value over time.
C. APPLICATIONS
The potential scope of applications for robust data value
assessment techniques is currently unknown. We have seen
that a very wide range of application types and domains
are already represented (see section IV-C) with automated
data value-driven decisions is most mature in the field of
Enterprise Information Management Fleckenstein et al. [39]
explains. However with modern, increasingly data-centric
businesses the potential for data value must be significant.
Machine learning applications are heavily data dependent
and although data quality has started to be addressed in
this field there are very few data value papers published on
machine learning to date.
It is notable from this survey that many data value models
and metrics are still only tested in simulations [53, 61, 51] or
case studies, and there is a lack of longitudinal case studies
on the experiences and benefits of data value-driven decision
making. Enterprise Information Management is an exception
with several significant deployments reported but the privacy
and security application papers [24, 76, 77, 95] do have
not a single deployment of automated, objective data value
assessment reported.
However, the diversity of current approaches demands
some synthesis for more widespread applications. For exam-
ple, the lack of an established pipeline, lifecycle, or workflow
for the application of data value impedes its adoption. This
could span data value assessment, reporting, improvement,
value-driven decisions, and other stages. Early work on data
value monitoring capability maturity models exists [109] but
this must be expanded to all lifecycle stages.
Common data value representations, perhaps based on
semantic models [27], are key to both application and domain
interoperability. At the business level, they will facilitate the
comparison and integration of reported data value between
multiple units in the same organisation, eliminating silos and
enabling management of organisation-wide data value chains
[1]. Beyond the organisation, this will provide better insights
and collaboration opportunities to other stakeholders, e.g.
governments, policy-makers, economists, non-governmental
agencies, press.
While there are already attempts to link data value with
the proliferation of machine learning [90] techniques, it is
surprising how little is being done. We are witnessing several
breakthroughs in machine learning, but only a tiny fraction
of them are related to data value (e.g., assessment of data
quality metrics [110]). On the other hand, a large proportion
of machine learning outcomes relies on data (collected, syn-
thetic, or augmented) with many data quality issues [111].
Therefore, data value could play an important role in provid-
ing guidance and decision support mechanisms for machine
learning. This collaboration would no doubt also produce
more machine-learning based techniques for assessing data
value.
VI. CONCLUSION
In this paper, we surveyed 63 existing works defining, char-
acterising, modelling, and applying data value as a concept
and for driving decision making (Table 2). We have identified
that despite data value having conceptual origins back to at
least 1980 in [31], the field is still immature with a lack of
commonly agreed terminologies, models, or approaches to
data value.
Our analysis found that there is a lack of generalised
data value models and commonly understood dimensions
to properly quantify the value of data, see Section IV-B.
This contrasts strongly with the more mature but related
domain of data quality. This leads to an absence of common
validation platforms and tools, which limits comparison of
work, reproducibility, rate of progress, and industrial de-
ployment. Nonetheless this survey has collected the most
comprehensive list of data value metrics to date (Table 8)
where it can be seen that the Usage dimension is the most
often measured dimension of data value that is not already a
known dimension of data quality.
Despite the increase in number of works in the area in
recent years, it is clear that there is still many important
research questions be be resolved and a set have been col-
lected in Table 9. Addressing these challenges will help
organisations better understand and exploit their data more
effectively. More mature data value techniques will enable
them to quantify the value of their data assets more accu-
rately and efficiently. This will not only help mitigate any
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TABLE 9: Research areas for data value and potential research questions for future research.
Topics Challenges and Research questions
Common Conceptual Framework Challenge
Terminology What would a unified terminology for data value consist of and how should
it best be expressed and shared?
Data value models Is there a set of data value dimensions that can be used across many use
cases?
Can application and stakeholder-independent predictive models of data value
be developed?
How should financial and scalar data value metrics be combined in data value
models?
Multi-models What is the most appropriate basis for combining multiple model results?
How can common concerns like privacy be consistently included in multiple
models?
Reusable Assessment Methods Challenge
New metrics What new methods or metrics are needed to comprehensively and effectively
assess the value of data?
To what extent can utility function-based metrics be linked to observable
properties of data sets or services?
Open source tools What is the potential for open source or widely deployed data value assess-
ment tools?
New types of data What interfaces, formats, standards, and protocols are needed to enable data
value management tool-chains?
How can stream processing techniques assess dynamic data value?
Applications Challenge
Lifecycles Would a standard data value lifecycle foster tool development and adoption?
Deployment What are the differences between lab-based data value applications and real
world deployments?
Decision making What are the effects of data value mechanisms on decision-making perfor-
mance?
How can organisations use data value to enhance decision-making and break
down siloes?
How does data value evolve over time for different applications?
Machine learning Can data value approaches be applied to data collection and curation for
machine learning applications?
How can machine learning approaches be best applied to data value assess-
ment?
data-related risks and enhance any data governance efforts,
but also enable data-based decision making, including data
acquisition and investment decisions, data maintenance deci-
sions, innovation decisions, and also business decisions (e.g.
in merger and acquisition scenarios).
ACKNOWLEDGEMENT
This work was supported in part by the Science Foundation
Ireland grants 13/RC/2094_P2 (Lero) and 13/RC/2106_P2
(ADAPT) and is co-funded under the European Regional
Development Fund (ERDF). For the purpose of Open Access,
the authors have applied a CC-BY public copyright license
to any author accepted manuscript version arising from this
submission.
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