Exploring Data Value Assessment: A Survey Method and
Investigation of the Perceived Relative Importance of Data Value
Dimensions
Rob Brennan
1 a
, Judie Attard
2 b
, Plamen Petkov
1 c
, Tadhg Nagle
3 d
and Markus Helfert
4 e
1
ADAPT Centre, School of Computing, Dublin City University, Dublin 9, Ireland
2
ADAPT Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland
3
Department of Accounting, Finance and Information Systems, University College Cork, Ireland
4
LERO Centre, School of Computing, Dublin City University, Dublin 9, Ireland
Keywords: Data Value, Business-IT Alignment, Business Value of IT, Data Governance.
Abstract: This paper describes the development and execution of a data value assessment survey of data professionals
and academics. Its purpose was to explore more effective data value assessment techniques and to better
understand the perceived relative importance of data value dimensions for data practitioners. This is important
because despite the current deep interest in data value, there is a lack of data value assessment techniques and
no clear understanding of how individual data value dimensions contribute to a holistic model of data value.
A total of 34 datasets were assessed in a field study of 20 organisations in a range of sectors from finance to
aviation. It was found that in 17 out of 20 of the organisations contacted that no data value assessment had
previously taken place. All the datasets evaluated were considered valuable organisational assets and the
operational impact of data was identified as the most important data value dimension. These results can inform
the community’s search for data value models and assessment techniques. It also assists further development
of capability maturity models for data value assessment and monitoring. This is to our knowledge the first
publication of the underlying data for a multi-organization data value assessment and as such it represents a
new stage in the evolution of evidence-based data valuation.
1 INTRODUCTION
Trends such as Big Data have popularised the need
for enterprises to become more data driven and
increased the need for a better understanding of what
that means (The Economist, 2017). This is in line with
the view that while organizations claim that data is a
strategic asset, they fail to articulate its value,
resulting in missed opportunities, fundamental data
problems (such as data quality), and ultimately
unsuccessful projects (Nagle and Sammon, 2017).
Even defining data value has proved problematic with
many defintions in but no agreed consensus as yet.
Despite this lack of clarity on how to quantify data
value, the literature highlights data value chains as a
a
https://orcid.org/0000-0001-8236-362X
b
https://orcid.org/0000-0001-7507-1864
c
https://orcid.org/0000-0002-1757-7987
d
https://orcid.org/0000-0002-6123-7349
e
https://orcid.org/0000-0001-6546-6408
way to organise enterprises. These echo
manufacturing value chains (Crié and Micheaux,
2006), and depict a process-orientated view of data
(e.g. defining activities from acquisition to
distribution). However, data value chains do not
specify the capabilities needed to manage or optimise
value creation (Rayport and Sviokla, 1995). It has
been observed (Otto, 2015) that measures for
managing data as a strategic resource have focused on
technology aspects such as data architecture or
analytics. However, a technology first attitude
towards data can cause more problems than solutions
(Nagle and Sammon, 2017).
Articulating and communicating the value of data
within organizations in ways that lead to successful
200
Brennan, R., Attard, J., Petkov, P., Nagle, T. and Helfert, M.
Exploring Data Value Assessment: A Survey Method and Investigation of the Perceived Relative Importance of Data Value Dimensions.
DOI: 10.5220/0007723402000207
In Proceedings of the 21st International Conference on Enterprise Information Systems (ICEIS 2019), pages 200-207
ISBN: 978-989-758-372-8
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
projects, depends on an understanding of the context
of use, the value creation process, data value
measures, and hence the nature of data value. The
focus of this paper is on data value assessment or
quantification. It is possible to locate application-
specific data value assessment metrics in the literature
(Higson and Waltho, 2010). However, there is a lack
of understanding of how data value dimensions
combine into data valuations (Viscusi, G., and Batini,
2014) and how they contribute to undoubtedly
complex data value creation processes (Moody and
Walsh, 1999). Effective data value management must
start with practical data value assessment techniques
(Brennan et al., 2018).
In previous work (Brennan et al., 2018) we
identified the data value assessment and monitoring
capability within an organisation as critical to
successfully managing data value. In this paper we
seek to answer the research questions (i) to what
extent do organisations value their data? and (ii) to
what extent can manual data value assessment survey
techniques inform us about the key dimensions of
data value? To address these questions we (i) idenify
a key manual data value assessment survey method
from the literature, (ii) describe our further
development of the assessment method and (iii)
provide initial results from applying the method in 20
academic and business environments.
The contributions of the paper are providing
evidence from a field survey that data value
assessment is needed, development of a manual data
value assessment survey and the first published set of
responses for such an assessment. We have
augmented the survey questions from previous work
with more detailed ones on specific aspects of each
data value dimension and with a set of self-reflective
questions to establish the impact of participating in
the assessment process. This is itself an indicator of
the potential for organizational change.
The rest of this paper is structured as follows:
section 2 provides background on data value with a
focus on assessment and monitoring, section 3
describes the structure and development of our data
value assesment survey, section 4 presents an
evaluation of the relative importance of data value
dimesions using our data value assesment survey for
a set of 34 datasets acrosss multiple organisations and
finally section 5 provides our conclusions.
2 BACKGROUND
Data value assessment should aim to be holistic in
measuring the dimensions of data value for an
organization. Unfortunately, there are a wide range of
known dimensions of data value (Viscusi, G., and
Batini, 2014) and there is not yet a consensus on their
definitions, how they are related, or how data value
metrics in information systems relate to monetary
value (as measured in accounting-based measures of
value). Viscusi and Batini break data value down into
information capacity and information utility (Viscusi,
G., and Batini, 2014). Capacity is then subdivided
into quality, structure, diffusion and infrastructure. In
their scheme, utility is based on financial value,
pertinence and transaction costs. In contrast, the
models of (Moody and Walsh, 1999) and (Tallon,
2013) strongly emphasize usage as a key dimension
of value. It is in usage-based data value that the most
progress has been made for practical data value
monitoring systems. Hence, we give it prominence
below.
Ease of measurement is another important concept
to consider. Some data value dimensions have well
known metrics and may even have recommended data
or metadata formats, for example the W3C’s data
quality vocabulary (Albertoni et al., 2016) and DaVe,
the Data Value Vocabulary (Attard and Brennan,
2018). Other data value dimensions, such as business
utility or impact are very difficult to measure since
they depend on having models and information about
the business processes, outcomes and dependencies to
identify measurable metrics for the contribution of
datasets to profit or operating efficiencies.
Moody et al. (Moody and Walsh, 1999) defined
seven “laws” of information (which we just refer to
as data in the widest sense) that explained
information’s unique behaviour and relation to
business value, whilst also highlighting the
importance of meta-data. Moody identifies three
methods of data (information) valuation utility,
market price and cost (of collection) and concludes
that utility is in theory best, but impractical and thus
cost-based estimation is the most effective method.
Most research on information value merely seeks to
identify dimensions or characteristics without
defining a mathematical theory of data value. Many
of these dimensions overlap with data quality
dimensions. For example, (Ahituv, 1989) suggests:
timeliness (dimensions: recency, response time, and
frequency), contents (dimensions: accuracy,
relevance, level of aggregation and exhaustiveness),
format (dimensions: media, color, structure,
presentation), and cost.
There are documented uses of data value
assessment and monitoring for enhanced control of
elements of the data value chain, especially in the
application areas of file-storage management
Exploring Data Value Assessment: A Survey Method and Investigation of the Perceived Relative Importance of Data Value Dimensions
201
(Wijnhoven et al.., 2014), information lifecycle
management (Chen, 2005), information pricing (Rao
and Ng, 2016), data governance (Tallon, 2010)
(Stander, 2015), and data quality management (Evan
et al., 2010). We used these examples from practice
as the basis for our data value monitoring capability
maturity model (Brennan et al., 2018).
In 2006 Sajko et al. defined a structured manual
data value assessment method for security risk
assessment (Sajko et al., 2006) and unlike the
previous methods that focus on automated or
theoretical data value assessment, a structured
questionnaire is used to drive a stakeholder
assessment of the importance (value) of
organisational data assets as part of a workshop to
determine which assets should receive the most
attention in the creation of a data security solution.
The five questions provided are each aligned with a
single data value dimension: operational impact
(utility), replacement costs, competitive advantage,
regulatory risk and timeliness. They are framed in
terms that are suitable for business stakeholders to
easily relate to. Compared to the general formulations
of data value dimensions discussed above, there are
two significant omissions in the data value
dimensions selected: data utilisation and data quality
which is generally included by all holistic models of
data value. Sajko et al. also provide a suggested
scoring system for the Likert-style responses to the
questions and establish a threshold to determine
whether or not a given data asset is “organisationally
valuable”. The simplicity and engaging nature of this
method is very attractive for deploying a first level
data assessment method in an organisation to (i)
establish baselines for the evaluation of automated
methods, (ii) act as a first assessment of data value
from local domain experts that are aware of the
business use of data assets but who may struggle with
linking value either to more abstract data value
dimensions or choosing appropriate data value
metrics and (iii) to stimulate organisational awareness
of data value. Although Sajko et al. report that the
method has been applied many times unfortunately it
provides no example data on responses.
3 DATA VALUE ASSESSMENT
SURVEY DESIGN
This section discusses the development of an
enhanced form of Sajko et al.’s data value assessment
survey to investigate our research question and
support the wider use case of data value-driven digital
transformation rather than security risk assessment.
Three enhanced questionnaires were iteratively
developed ranging from 11 to 28 questions per
dataset. The survey prototypes were created using
Google Forms. In order to study data value and its
dimensions, the questionnaires were divided into five
major dimensions of value:
1. Operational Impact (Utility);
2. Dataset Replacement Costs;
3. Competitive Advantage;
4. Regulatory Risk;
5. Timeliness.
These are based on the structured manual data
value assessment method by Sajko et al. (Sajko et al.,
2006). All three questionnaires covered these
dimensions. In addition to evaluating data value itself,
every survey included a section on self-reflection on
the manual assessment process itself. This is where
the impact of performing the assessment on the
organization was self-evaluated. In two of the three
forms of the survey, participants could add
evaluations of multiple datasets or data assets, but this
was dispensed with for the final survey as it was
found most respondents (66%) only entered data for
a single dataset and it was hoped that a shorter survey
would increase the response rate.
Questions were mainly multiple choice; however,
some open-ended questions were also included. In
toyal 23 new questions developed and these were
formulated based on (a) the desired addition of data
quality and utilization dimensions and (b) the
approach of the data value map (Nagle and Sammon,
2017). Thus, the objective with these questions was
to go beyond the passive collection of data and to act
as spur to insight and discussion with the participants.
The addition of data quality and utilization
dimensions of value is grounded in our ongoing
survey of the data value literature to support the data
value vocabulary initiative (Attard and Brennan,
2018). Most questions asked the participant to rate the
importance of an event with respect to their dataset in
terms of business impact in an increasing level of
severity that may be converted to a Likert-like scale.
During the first iteration, the description of the
survey and its purposes was presented to a test group
of data science postgraduate students with a range of
backgrounds to improve the understandability of the
study. Information related the ethics and the impact
of the study were included based on their feedback.
The first versions of the questionnaire consisted
of a high number of questions (28). After discussing
ICEIS 2019 - 21st International Conference on Enterprise Information Systems
202
the complexity and the number of questions, the
authors decided to also produce a short form survey
in order to reduce completion and minimise
inaccurate responses (e.g. by participant unable to
understand a question due to its complexity or by
answering a question without taking the necessary
time to understand it). The authors also decided to
keep and use two versions of the questionnaire (long
and short form), as the long one may generate more
insight into data value assessment and analysis of the
meta-questions could provide feedback on which
form respondents preferred or found more effective.
The next review criteria were related to the types
of questions included. For example, to ensure that the
survey includes questions which cover all possible
data value dimensions, open form answer options
were added in addition to predefined lists of potential
answers. The authors also agreed to place simpler
questions (e.g. questions related to capture
technology-centric metrics such as data volume, and
access rate) at the beginning of the questionnaire and
more difficult at the end (e.g. questions that capture
business user satisfaction or require understanding of
the value creation process). The rationale behind this
was to avoid people become flustered and quit
answering questions at very early stage of the survey.
4 VALIDATION OF DATA VALUE
DIMESIONS
This section describes the use of our survey to
investigate the hypothesis that given a set of data
value assessments (responses) targeted at specific
data value dimensions that we could gather evidence
for which dimensions are seen as most important for
contributing to data value in an operational setting.
4.1 Method
A wide-scale, multi-organization data value
assessment survey was conducted to gather further
evidence about the relative importance of different
data value dimensions to an organization. The
primary means of data collection for our research was
a questionnaire. The structure of this questionnaire is
outlined in the previous section, and information on
participation criteria and sampling is provided below.
The Likert-type scoring scale provided for the
questionnaire results by Sajko et al. is used to convert
the survey results into numerical scores to enable easy
comparison of the results.
6
http://2018.datavalue.adaptcentre.ie/cfp.html
By allowing participants to evaluate their own
datasets we recorded the overall responses per data
value dimension (as each question targets a specific
dimension). Lower scores in these cases indicate less
important dimensions of value for specific datasets.
When the survey results are taken as a whole these are
an indicator of the relative importance of each
dimension for the operational datasets evaluated.
Following (Sajko et al.., 2006) this gives some insight
into the relative importance of datasets within an
organization and may even indicate trends in the
relative importance of the dimensions themselves in
a business setting. In addition, the reflective questions
were analyzed to indicate the organizational impact
of participating in the data value assessment exercise.
The participants were a mix of enterprise data
professionals (16) drawn from a wide range of
industries (finance, aviation, publishing, legal, ICT)
and computer science postgraduate students (4) used
for initial testing. Recruitment was through the
network of past professional association with the
Cork University Business School Master’s degrees
for practitioners, participation in the Data Value
Workshop at Semantics 2018 in Vienna
6
, Austria and
the clients and partners of Castlebridge data
governance consultancy
7
. This was a broad range of
participants with data governance backgrounds.
Non-probabilistic sampling methods were used to
recruit participants. Key decision makers were
contacted in the participating organisations and asked
to complete the questionnaire or forward it to relevant
staff. An open call for participation in the
questionnaire was made in the Data Value Workshop
(3 responses).
The questionnaires received 20 responses, all of
whom had completed at least one dataset data value
assessment. In total 34 datasets were assessed. This
was made up of 12 short-form questionnaires that
assessed a total of 20 datasets and 8 long-form
questionnaires that assessed 14 datasets.
4.2 Results
The results of this multi-organization data value
assessment activity are presented in the following
paragraphs and associated tables and are discussed
and interpreted in the next subsection. In all cases the
value score columns are based on the methodology of
Sajko et al. but the raw data from user responses is
also presented in the tables to enable other
interpretations.
7
https://www.castlebridge.ie/
Exploring Data Value Assessment: A Survey Method and Investigation of the Perceived Relative Importance of Data Value Dimensions
203
Operational Impact Data Value Dimension
(Utility): In table 1 the results of the common
question for operational impact are summarized. The
most popular impact selected across all data assets
(59%) is that there would be a major impact on
operations. The mean score calculated is also the
highest value for any dimension examined.
Replacement Cost Data Value Dimension
(Cost): In table 2 we see the results of this common
question across all three questionnaires. This features
a much more even spread of answers, so this implies
that replacement costs for data are more variable than
the operational impact of losing data. However, the
fact that the highest impact answer is the most popular
(35% of respondents) ensures that the weighted mean
score for this dimension is still high at 2.88.
Competitive Advantage (Market Value) Data
Value Dimension: Once again (table 3) the strongest
(most valuable) response it’s the most popular one at
35% of respondents but it is notable that a large
fraction of the respondents (18%) estimate that their
data is of no use to their competitors. This depresses
the mean score for this dimension to 2.35.
Competitive Advantage (Market Value) Data
Value Dimension: This dimension (table 4) captures
the likelihood that an organization is keeping data for
auditing purposes that have a potential penalty
associated with non-compliance. It is a kind of
inverse value as if not properly maintained then these
datasets will become a liability for the organization.
For 50% of the datasets assessed there were potential
sanctions or strict sanctions if the data was not
maintained. However, the large number of lower
category responses see that the mean score continues
to drop slightly and is at 2.32 for this dimension.
Timeliness Data Value Dimension:
Unfortunately, the sample size (20) for this question
(table 5) is smaller than the others as the longer
variant questionnaire had a cluster of related
questions about the effect of time on data that do not
easily map onto the question presented in the short
survey based on Sajko et al. hence only the short
survey results are presented here. Nonetheless it can
be seen that many datasets (45%) do not exhibit the
property of data value decreasing over time. One
omission from Sajko et al.’s methodology (Sajko et
al.., 2006) was the ability to account for datasets that
rise in value over time. Hence in the longer version of
the questionnaire we asked this and 50% (N=14) of
the datasets surveyed were recorded as increasing in
value over time. It is possible to combine the results
in table 5 with this result to get an overall value of
47% (N=34) of datasets are seen to either retain their
value or increase in value over time.
Self-Reflection on the Data Value Assessment
Process: The survey was accompanied by open
questions leaving the ability for the respondent to
provide additional context or rationale for their
answers (Table 6). One participant did not complete
this part of the survey and hence the sample size drops
to 19. The vast majority of respondents had never
taken part in a data valuation exercise before (89%)
and found the process simple (95%).
Overall Valuation: Using Sajko et al.’s method
the 34 data valuation surveys can be scored using the
Likert-type scale and weights of 0, 1, 2, 3 and 4 for
the possible answers (figure 1).
Figure 1: Histogram of the Sajko et al data value scores
calculated for the 34 datasets assessed in the field study.
4.3 Discussion
This is to our knowledge the first publication of the
data behind a multi-organization data value
assessment and as such it represents a new stage in
the evolution of evidence-based data valuation. It is
notable that 82% of the datasets which were assessed
would score 7 or more on the valuation scale of Sajko.
et al. and hence be assessed as “business private
information” and thus is valuable.
It is an interesting feature of this survey that the
aggregate results can be interpreted as an indication
of the relative importance (figure 2). From the figure
the operational impact of data for an organization (for
the datasets evaluated) is the most important
dimension and after that it is the combination of
timeliness and replacement costs that dominate.
Given the reported importance of timeliness it is
perhaps significant that under the assessment scheme
of Sajko et al. (Sajko et al.., 2006) there was no
concept of data value rising over time, in comparison
to the results reported by our respondents.
This may be a feature of the differences between
2018 and 2006 when Sajko et al. developed their
assessment scheme. It is also important to recognize
that Sajko et al. constructed the survey for the use
case of security threat assessment, i.e. to understand
ICEIS 2019 - 21st International Conference on Enterprise Information Systems
204
which data assets most needed protection, whereas
we are investigating data value for its general use in
data management. The specific use cases are laid out
in our definition of a data value ontology (Attard and
Brennan, 2018) and include value monitoring value-
driven data governance, data quality, data curation.
Table 1: Operational Impact (Utility) What happens if you do not have this data anymore?
Answer
Responses
Value
Score
(N = 34)
% Datasets
Nothing special
2
0
6%
Some non-essential processes are late
1
1
3%
Imperfections are noticeable, but fixable
4
2
12%
New costs appear
7
3
21%
There is a bigger halt to operations and wrong decisions are likely- new urgent
action is necessary
20
4
59%
Mean:
3.24
Table 2: Replacement Cost - What is the cost of replacing this data or production of the new equivalent data?
Answer
Responses
Value
Score
(N = 34)
% Datasets
Negligibly small
2
0
6%
Cost exists but it is low
0
1
0%
Higher costs appear
10
2
29%
Cost is hardly tolerable
10
3
29%
Intolerably high costs
12
4
35%
Mean:
2.88
Table 3: Competitive Advantage (Market Value) - What happens if your competitor has the same data?
Answer
Responses
Value
Score
(N = 34)
% Datasets
Nothing
6
0
18%
Competitor has all unimportant data about our company available
3
1
9%
Competitor has insight in our important business processes
10
2
29%
Competitor can reach the company
3
3
9%
Competitor gets competitive advantage
12
4
35%
Mean:
2.35
Table 4: Regulatory Risk - Is there any obligation to keep this data and any consequences for the organization if it loses it?
Answer
Responses
Value
Score
(N = 34)
% Datasets
There are none
8
0
24%
It is necessary to keep the data for a brief period
2
1
6%
The organizations should keep the data but without consequences
7
2
21%
Keeping the data is obligatory and the company can suffer sanctions
5
3
15%
Keeping the data is obligatory and the sanctions are strict
12
4
35%
Mean:
2.32
Table 5: Timeliness - Does the data value fall in the course of time?
Answer
Responses
Value
Score
(N = 34)
% Datasets
Very quickly
1
0
5%
Quickly
5
1
25%
After 1 year
0
2
0%
After a few years
5
3
25%
Does not fall at all
9
4
45%
Mean:
2.8
Exploring Data Value Assessment: A Survey Method and Investigation of the Perceived Relative Importance of Data Value Dimensions
205
Table 6: Self-reflection Questions (per participant rather than per-dataset).
Question
Answer
Responses
(N = 34)
% Datasets
1. Have you been previously asked to value your data?
Yes
2
11%
No
17
89%
2. Do you think the Data Value Questionnaire has changed your perception on
data value?
Yes
8
42%
No
11
58%
3. In the future, will you change how your data is stored, maintained, or
secured?
Yes
6
32%
No
12
63%
4. Was the Data Value Questionnaire easy to answer?
Yes
18
95%
No
1
5%
Figure 2: Radar plot of the Highest Mean Scores for the
Data Value Dimensions Assessed by the Survey.
A new use case that is gaining attention is the use
of data value assessments in corporate merger and
acquisition processes.
Given that 18 of the respondents were
practitioners, it was surprising to see change in
perception the survey generated. Given the simplicity
of the survey and the fact that it changed the
perception of 42% of respondents, points to fragility
or uncertainty in how practitioners perceive data
value. This may be partially explained by the low
number of data valuations carried out by the
respondents, but it is still surprising given the
backdrop of current data trends like AI, machine
learning and big data, all of which portraying the
potential to unlock the value in organizational data.
However, if practitioners do not understand this value
in the first place, initiating data projects becomes a
random exercise and delivering a successful one
becomes problematic. How can data projects be on a
successful trajectory if the value of data is not
understood upfront or throughout the project.
5 CONCLUSIONS
Our key conclusion, from this initial field study, is
that while organisations acknowledge that they hold
significant value in data (82% of the datasets assessed
were classified as valuable) but interestingly very
rarely are asked to place a value their data (89% of
respondents had never previously been asked to
perform a data value assessment). It also seems that
their understanding of data value is fragile as 42% of
respondents suggested that engaging in our simple
assessment process changed their opinions on data
value. This indicates that the answer to our first
research question is that organisations do value data
in theory but not often in practice. More data needs to
be collected to support this initial finding.
Our previous capability maturity model (CMM)
for data value monitoring and assessment (Brennan et
al., 2018) suggested a hierarchy of data value
dimensions, i.e. Utility (including Operational
Impact), Context (including Timeliness and
Competitive Advantage), Usage and Quality, Cost
(including replacement costs). The analysis here of
the data value assessment survey provides further
evidence of this hierarchy - usage and cost are the
easiest to implement but utility or operational value is
the most important dimension for organizations. This
contributes to our second research question and
indicates that manual survey-based methods are
worth deploying to gather further evidence.
The survey results indicate an impact on
practitioners by performing data value assessments.
This is encouraging as next we intend to provide an
online tool for conducting assessments and allowing
organisations to compare their performance to others
in terms of the CMM and hence recommend
strategies for improving data value assessment and
monitoring in their organisation.
Replacement
Costs
Timeliness
Regulatory Risk
Competitive Advantage
ICEIS 2019 - 21st International Conference on Enterprise Information Systems
206
ACKNOWLEDGEMENTS
This research was partially supported by Science
Foundation Ireland and co-funded by the European
Regional Development Fund through the ADAPT
Centre for Digital Content Technology [grant number
13/RC/2106] and grant 13/RC/2094 co-funded under
the European Regional Development Fund through
the Southern and Eastern Regional Operational
Programme to Lero - the Irish Software Research
Centre (www.lero.ie). This paper has also received
funding from the European Union’s Horizon 2020
research and innovation programme under the Marie
Sklodowska-Curie grant agreement No. 713567
(EDGE). The authors wish to thank Dr Pieter De
Leenheer of Collibra, Nora Murphy and Laura
Tierney for their assistance in this research.
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