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

ADAPT Centre, School of Computing, Dublin City University, Dublin 9, Ireland

ADAPT Centre, School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland

Department of Accounting, Finance and Information Systems, University College Cork, Ireland

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

https://orcid.org/0000-0001-8236-362X

https://orcid.org/0000-0001-7507-1864

https://orcid.org/0000-0002-1757-7987

https://orcid.org/0000-0002-6123-7349

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

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

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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.

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

, Austria and

the clients and partners of Castlebridge data

governance consultancy

. 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.

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

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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

Some non-essential processes are late

Imperfections are noticeable, but fixable

12%

New costs appear

21%

There is a bigger halt to operations and wrong decisions are likely- new urgent

action is necessary

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

Cost exists but it is low

Higher costs appear

29%

Cost is hardly tolerable

29%

Intolerably high costs

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

18%

Competitor has all unimportant data about our company available

Competitor has insight in our important business processes

29%

Competitor can reach the company

Competitor gets competitive advantage

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

24%

It is necessary to keep the data for a brief period

The organizations should keep the data but without consequences

21%

Keeping the data is obligatory and the company can suffer sanctions

15%

Keeping the data is obligatory and the sanctions are strict

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

Quickly

25%

After 1 year

After a few years

25%

Does not fall at all

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

11%

89%

2. Do you think the Data Value Questionnaire has changed your perception on

data value?

Yes

42%

58%

3. In the future, will you change how your data is stored, maintained, or

secured?

Yes

32%

63%

4. Was the Data Value Questionnaire easy to answer?

Yes

95%

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.

Operational Impact

Replacement

Costs

Timeliness

Regulatory Risk

Competitive Advantage

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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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