Data at the Core Establishing a Data-Driven Culture

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Data at the Core: Establishing a Data-Driven Culture
by Joe Gibson
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Research Thesis Submitted in Partial Fulfilment
of the Requirements for the Degree of Master of Business Administration in
Collaborative Leadership
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Canterbury Christchurch University
Christ Church Business School February 2022

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Abstract:
In the UK construction sector, the pace of change toward data and big data adoption has been
slow (Rumpenhorst, 2016). To date, construction entities have had solitary focus on the physical
asset as being the principal enabler for margin enhancement. More significantly, back-office
functions (Procurement, IT, Data and Legal) have not archetypally been acknowledged as
strategic functions but rather, tactical (CIPS, 2021). Consequently, low industry margins, the
cultural norms toward support functions, compounded by a lack of investment in transformative IT
infrastructure, has led to many UK construction entities facing a perilous period of data-driven
cultural inertia (Jalona, 2020).
Furthermore, since Carillion’s insolvency, Grenfell disaster and more recent, Covid-19, the UK
construction sector has encountered a noteworthy amount of economic precariousness over the
past few years. In addition, a 2019 research journal by the Institute of Civil Engineers found that
the top-10 UK construction main contractors delivered, on average, a c. 1% net margin over the
past three financial years. As such, large construction entities have recognised the need to
reimagine their relevancy, by acknowledging that a change is needed in their organisational
operating models, and more importantly, an acceptance that the quest to become data-driven
is no longer a ‘nice to have’ but it is existential, it is a matter of survival (Mauri, 2020).
Purpose: The purpose of the research study was to investigate how an organisation can
exchange defective, human-centric decision-making with more vigorous, reliable, and
dependable technology and data-centric based decisions. The research focused on a large
construction organisation (OA) currently undergoing a significant transformation programme.
Methods: This research followed the case study manner in an interpretivism philosophical
approach. The case study / interpretivism consolidated approach involves an in-depth study of a
single or collective issue. The study was premised on the semi-structured, qualitative approach.
The sample consisted of eight Procurement professionals with mixed seniority. The interview data
was categorised, prepared, and then analysed into themes.
Results: The research findings articulated that a data culture has six distinguishing characteristics;
data-driven decision-making, a fail fast culture, a common language for data, trustworthiness (in
the data and the leadership team), a strong leadership team and a significant investment in
technology. The study found leadership is imperative in data cultures. Lastly, ten actions were
recommended to introduce and enable a data culture (see section 5.1).
Keywords: Common language, culture, curiosity data, decision-making, fail fast, learn fast
leadership, maturity models, standardisation, transformation, teamwork, trustworthiness.

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Contents
Abstract:................................................................................................................................................................2
List of tables:.........................................................................................................................................................4
List of figures: ........................................................................................................................................................4
List of acronyms: ..................................................................................................................................................4
1. Background: ................................................................................................................................................5
1.1 Purpose of the research:...................................................................................................................6
1.2 Research questions: ...........................................................................................................................7
2. Theoretical foundation: .............................................................................................................................7
2.1 Organisational culture:......................................................................................................................9
2.2 Data-driven organisations: .............................................................................................................10
2.3 Data cultures:....................................................................................................................................11
2.3.1 Characteristics of a data-driven culture: ................................................................................12
2.3.2 The importance of a data-driven culture:...............................................................................14
2.4 Culture transformation:....................................................................................................................15
2.5 Leadership: ........................................................................................................................................16
3. Methodology:............................................................................................................................................17
3.1 Design: ................................................................................................................................................17
3.2 Approach:..........................................................................................................................................19
3.3 Data collection:................................................................................................................................20
3.4 Sampling:............................................................................................................................................21
3.5 Data analysis: ....................................................................................................................................22
3.6 Feasibility: ...........................................................................................................................................23
3.7 Research ethics:................................................................................................................................25
4. Findings and Discussion: ..........................................................................................................................25
4.1 RQ1 - What constitutes a data-driven culture?..........................................................................28
4.2 RQ2 – Is leadership important in a data-driven culture?..........................................................31
4.3 RQ3 - What actions can organisations take to introduce a data-driven culture? .............34
4.4 Summary of research findings:.......................................................................................................36
4.5 Summary of findings in relation to the literature:........................................................................37
4.6 Limitations:..........................................................................................................................................38
5. Conclusion: ................................................................................................................................................39
5.1 Recommendations for action:.......................................................................................................40
5.2 Recommendations for future studies:...........................................................................................41
6. Appendices: ..............................................................................................................................................43
7. References: ................................................................................................................................................44

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List of tables:
Table 1 – Core literature
Table 2 – Characteristics of a data culture
Table 3 – Detailed research philosophies
Table 4 – Profile of primary participants
Table 5 – Profile of secondary participants
Table 6 – Sample of risks
Table 7 – Ethical considerations
Table 8 – Identified themes RQ1
Table 11 – Recommendations for action
List of figures:
Figure 1 – Research onion
Figure 2 – Thematic analysis visual
List of acronyms:
COVID-19 – Coronavirus disease, global pandemic
OA – Organisation A – A large construction organisation
DMM – Data maturity models
ICE – Institute of Civil Engineers
CIPS – The Chartered Institute of Procurement and Supply
Brexit – Britain’s exit from the European Union
Grenfell – The Grenfell Disaster
RQ – Research question

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1. Background:
Information technology capabilities, specifically with reference to the organisations use of data
analysis, has been demonstrated to unlock endless opportunities regarding strategic decision-
making and the associated competitive advantage gained (Cech et al., 2018; Barkholt and
Jesssen 2020; Dodds, 2015). When discussing an organisation’s capability within the confines of
data, the concept of ‘data maturity’ is typically noted (Cech et al., 2018). Research scholars
utilise the term data maturity to define an organisation’s ability to (a) gather, (b) store and (c)
management report, as well as the implementation of solutions from both a process and
technology-based perspective (Chen and Nath, 2017).
Moreover, Cech et al. (2018) defined a data maturity model that categorises data models as (1)
descriptive, (2) diagnostic, (3) predictive, or (4) prescriptive. Each element of the framework
requiring increasing complexity with reference to business process and technology-based
capabilities. McElheren (2016) suggested that the more mature an organisation’s data is, the
easier is it for employees to execute data-driven decisions, at pace. Importantly, over the past
decade there has been a preceding and unprecedented shift from human-centric to data-
driven decision-making with use-cases quadrupling in the construction and manufacturing
industries (Ylijoki and Porras, 2016; Garcia-Perez, 2018; Fawcett, 2013).
Specifically, in the UK construction industry, the pace of change toward data and big data
adoption has been slow (Rumpenhorst, 2016). For many years, construction organisations have
had solitary focus on the physical asset as being the primary driver for margin enhancement.
More importantly, support functions (Procurement, IT, Data and Legal) have not typically been
given recognition as strategic functions but rather, tactical (CIPS, 2021). Consequently, low
industry margins, the cultural norms toward support functions, compounded by a lack of
investment in transformative IT infrastructure, has led to many UK construction entities facing a
perilous period of data-driven cultural inertia (Jalona, 2020).
Furthermore, because of the Carillion insolvency, Grenfell disaster and more recent, Covid-19, the
UK construction sector has encountered a significant amount of economic volatility over the past
few years. Furthermore, a 2019 research journal by the Institute of Civil Engineers (ICE) found that
the top-10 UK construction main contractors delivered, on average, a c. 1% net margin over the
past three financial years. As such, large construction entities have recognised the need to
reimagine their relevancy, by acknowledging that a change is needed in their organisational
operating models, and more importantly, an acceptance that the quest to become data-driven
is no longer a ‘nice to have’ but it is existential, it is a matter of survival (Mauri, 2020).

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Procurement senior leadership teams have failed to implement more efficient, smarter, data-
driven transformational cultures because frankly, they do not understand it (Díaz, Rowshankish
and Saleh, 2020). This is despite a 2020 whitepaper by Accenture that notes that vast technology
enhancements and the emerging data revolution toward big data adoption, advanced data
analytics, machine learning and SaaS provisions, all enhance the Procurement profession.
To date, most literature on organisational culture has historically fixated on culture as a wide-
ranging concept in determining how culture manifests itself in the organisational context.
However, there is a significant gap in the literature. There is no strong definition of a ‘data culture’,
no clearly articulated actions an organisation can take to foster a culture predicated on data-
driven decisions and finally, there is minimal analysis on different archetypes of culture and, more
importantly, on organisational cultures which enable and encourage data-driven behaviour.
1.1 Purpose of the research:
As a business, Organisation A (OA) are currently undergoing a vast transformational change
programme after several years of poor financial performance. To some extent, all internal
business functions have been through a change – leadership (Technical and IT), re-structure (HR)
and the formation of a Data team. The Procurement function has been through a significant
change. Three ‘Heads of Function’ over the past 5-years, a re-organisation, and a core operating
model change to adopt a Category Management strategy. Furthermore, and in the specific
context of the procurement function, a transformation team has been formed to strategise a five-
year technology and data change programme for delivery into the global procurement function.
These changes, compounded by the wider-organisation’s financial performance, has led to a
group-wide drive to reduce overhead expenditure to under 4% (at time of writing it was 7% for
FY21). According to Jalona (2016), the simplest, most efficient way to reduce organisational
overheard is to re-structure operations through a redundancy programme. As such, the
procurement senior leadership team have recognised that a functional-wide mentality change
toward being data-driven is not merely needed, but it is the only option to enable successful
delivery of functional objectives (Gosney, 2021).
The purpose of the research study is to investigate how an organisation can exchange defective,
human-centric decision-making with more robust, consistent, and reliable technology and data-
centric based decisions. The premise of the research was solely on one organisation within the UK
construction sector endeavouring to transform and instil a data-driven cultural environment. On a
fundamental level, the aim of this research is to add to the existing body of knowledge. The
generalised problem was explored through a detailed review of a procurement professional’s
knowledge of data and their role in a data-driven culture.

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1.2 Research questions:
To ensure the purpose of this study is achieved and to enable a broader understanding, there are
three underlying research questions that will be answered:
• RQ1 - What constitutes a data-driven culture?
• RQ2 – Is leadership important in a data-driven culture?
• RQ3 - What actions can organisations take to introduce a data-driven culture?
2. Theoretical foundation:
Organisations find themselves in a time where change and digitisation are moving at a pace
never seen before. Leaders have recognised they will need to adapt, mould, and support their
employees (Dodds, 2015). As data takes a bigger hold on day-to-day tasks, the role of a leader
has significantly changed. Leaders must now navigate this new paradigm and make risk-based
decisions on both human and robot capability which has enforced a change in mental and
physical boundaries.
The changes to daily rituals mean the skills of the future will require leaders to become ever-
increasingly more vulnerable as they step into the unknown and co-create leadership circles
across the enterprise landscape to gain a wider perspective about problems and solutions.
By enabling a diverse range of data-driven cognitive skills, it will allow the procurement leader of
the future the ability to transform the way they strategise and execute against business objectives
and have greater, cross-functional alignment (CIPS, 2021). These new skills and thought processes
will mean they continually challenge and grow to ensure they enable an inclusive, innovative,
and entrepreneurial data-driven culture across their organisation.

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Authors
Key Topics
Organisational culture Data culture Leadership in
organisational culture
Data-driven decision-
making
Accenture (2020) X
Barkholt, M and Jesssen, N (2020) X X X X
Burnison, G. (2020) x x
Carruthers, C. and Jackson, P. (2019) X X X
Cech, T.G., Spaulding, T.J., Cazier, J.A.
and Jonathan, S. (2018)
X X X
Chen, J, Hagstroem, M., Rifai, K. and
Selah, M. (2017)
X X
Davenport, T. (2020) X X
Díaz, A. and Saleh, T. (2020) X
Dodds, L (2015) X
Farrell, M. (2018). X
Garcia-Perez, A. (2018) X X
Gourévitch, A., Fæste, L., Baltassis, E. and
Marx, J. (2020).
X X
Halaweh, M. and Massry, E. (2015) X X
Mauri, T. (2020) X X X X
Mikalef, P. (2017) X X X
Pettey, C. (2018) X X
Schein, E (2016) X X
Skyrius, R., Katin, I., Kazimianec, M. and
Nemitko, S. (2016)
X X X
Ylijoki, O. and Porras, J. (2016) X
Table 1 – Core literature
This section includes the core theory utilised within this research study. Table 1, above, gives an
overview of documentation used for this project. In section 2.1, an overview of relevant
organisational culture literature will be examined. Section 2.2 will review what constitutes data-
driven organisations and what tools are available for organisations to appraise their data-driven
capabilities. Section 2.3 will evaluate what a data culture is, the importance of a data culture in
today’s ever-changing business environment and the three key characteristics (section 2.3.1)
highlighted from current literature.

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In addition, section 2.4 discusses culture transformation and how organisations can develop the
transformation. Lastly, leadership is reviewed in terms of culture, data cultures and the
requirements of a leader to enable data culture manifestation, see section 2.5.
It is important to note that there is a significant gap in the literature in terms of data-driven
cultures, firstly within the UK Construction industry and secondly, with specific reference to
procurement professionals. A potential recommendation for exploration in future studies of this
kind. Nevertheless, there are three key characteristics that are continually noted within the
literature of a data-culture (a) data-driven decision-making, (b) fail fast, learn fast and (c) a
common language (Skyrius et al., 2016; Cech et al., 2018; Mikalef, 2018; Halaweh and El Massry,
2015).
2.1 Organisational culture:
Much academic literature has been written about organisational culture. It is a complex notion
that is difficult to comprehend and has numerous diverse definitions described by many different
academic scholars, however, there is no consensus on a sole meaning (George and Gupta,
2016). Scheider et al. (2013) propose that organisational culture incorporates the day-to-day
norms that the employees of an organisation describe and experience in their work settings.
Therefore, it could be suggested that such norms shape how employees adapt, behave, and
achieve results in their function and organisation. Stroud and Simoneaux (2014) suggest that
organisational culture is a manifestation of how the employees of a business interact with each
other and other stakeholders.
Nevertheless, the early basis of organisational cultural studies is predicated on the research by
Bernie Bass, Edgar Schein, and Hal Leavitt at the latter end of the 1960’s. Importantly, Schein
(1996) introduced the initial concept of organisational psychology, academics at that time
looked to segment elements of sociology and social psychology that dealt with organisational
phenomena from the recognised industrial psychology research.
However, preliminary studies formulated an individualistic bias as it did not consider organisations
systemically and further failed to note that culture was one of the most influential and stable
forces operating in business (Schein, 1996). In fact, so influential, norms held across large social
units are more probable to change leaders than to be changed by them (Schein, 1996). As a
result, in his 1996 research paper on ‘Culture: The missing concept in organisation studies’ Schein
devised ‘culture’ as the absent notion in organisational studies and argues that researchers have
failed to acknowledge culture seriously enough.

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Schein’s (2016) model of culture is founded on three distinct layers and is analysed as follows:
➢ Artifacts:
Artifacts are the most observable or visible levels of culture within an organisation and describe
what can be heard, felt, or seen. For example, the architecture of a company’s physical
environment, the observable rituals and/or language used (Schein, 2016).
➢ Espoused beliefs:
The espoused beliefs/values incorporate the ideals and aspirations of a company. It has been
suggested that these are the beliefs that a company aspire to. However, espoused beliefs are
often formulated in the ideology of a company, for example, one organisation might value
teamwork whilst another might value loyalty more favourably (Schein, 2016).
➢ Basic underlying assumptions:
The basic assumptions are the deepest layer of the model. The underlying assumptions are
intricately ingrained within the social unit. Therefore, when shared assumptions are discussed, it
suggests that a strong consensus exists across employees in an organisational culture. The basic
assumptions in Schein’s (2016) model provides its employees with a basic sense of identity, defines
behaviour amongst the employees and states to them how to feel positive about themselves,
which in its entirety, explains why culture as a notion is so influential in an organisational setting
(Schein, 2016).
2.2 Data-driven organisations:
To date, many data maturity models (DMMs) have been developed by scholars as an answer to
the big data revolution. Whilst there are various types of maturity models in circulation, in the
main, they are frequently utilised to target challenges. Nevertheless, the overall premise of DMMs,
in a benchmarking capacity, is to identify strengths and weaknesses of an organisation’s data
capability (Osman, 2008).
According to Dutta and Lanvin (2014), DMMs often encompass the creation of an ecosystem
containing relevant management of data, technologies, governance, and analytical
components. Whereas Carruthers and Jackson (2019) propose that the term ‘maturity’ points
towards a position in which the organisation is in a virtuous state for realising their specified
business goals.
DMMs assist organisations in determining where to start when moving towards becoming data-
driven and they have also been specified as a method for measuring and tracking an
organisations progress, as well as, to categorise pertinent initiatives to be embedded at an

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enterprise level. In addition, some organisations also apply it as an instrument for communicating
their big data aspiration throughout the organisation (Barkholt and Jesssen 2020). Thus, every
employee understands where the organisation’s data strategy is being directed and what it
requires to be considered successful (Barkholt and Jesssen 2020).
The most essential element in maturity models and the universal reasoning as to why an
organisation should attain maturity is that “a higher level of maturity will result in higher
performance” (Boughzala and Vreede, 2012, pp. 206) as the company will be able to foresee
potential downfalls, govern its progress and therefore, enhance its efficiency.
By comparison, Drus and Hassan (2017) argue DMMs are established and then developed on
previous experience of the author with their specific industry and as such, reliability can
sometimes be questioned. Either way, DMMs are crucial to organisations in evaluating their
current position in terms of data maturity and highlighting what they may need to do to move
upwards on the maturity curve and not merely manage data but also leverage it for a
commercial gain and thus, enhance their competitive advantage.
Most researchers recommend data maturity models that are premised, mainly, on the hard skills
essential for the execution of business intelligence solutions (Chen and Nath, 2018; Drus and
Hassan, 2017; and Cech et al., 2018). Most of these models omit soft skills and internal
communications/marketing with reference to the organisations data initiatives but they do
include the technological capability of a business (Chen and Nath, 2018).
Nevertheless, DMMs, according to Cech et al. (2018) do have their downfalls. Too data-centric,
rigid, and not easily implemented within organisations that do not have a data function. Garcia-
Perez (2019) proposes that DMMs are excellent for mature organisations that have time and funds
to spend on data initiatives. Whereas Farrell (2018) concurs with Cech et al. (2018) and further
notes that DMMs are not a methodology for measuring the efficiency and maturity of the culture
within an organisation, another further gap in knowledge to highlight within the literature.
2.3 Data cultures:
According to Diaz and Saleh (2018), culture, in its entirety, can be either a compounding solution
or a compounding problem because it should come as no surprise that a data strategy
disconnected from the organisational strategy and core operations will result in unsuccessful data
initiatives. However, if a strategy can generate excitement about data analytics for the greater
good of the organisation and is infused at functional level, it becomes a source of energy and
momentum (McElheren, 2016).

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When an organisation and/or a function is building a culture that promotes and rewards data-
driven behaviour, the business will need to evidence that beliefs and values about data analytics
can generate efficient, successful, and repeatable solutions to problems (McKinsey, 2016).
Consequently, transforming them into basic assumptions and, will eventually, result in them being
embedded as a norm.
Like electricity, data has developed into a basic enterprise asset that is swiftly revolutionising the
world, enabling faster, cheaper, better business processes (Díaz, Rowshankish and Saleh, 2020),
Data-driven organisations are dedicated to gathering data regarding all aspects of the
organisation (McKinsey, 2016). By enabling staff at every level to use the correct data at the
correct time, data can foster irrefutable decision-making and becomes part of the organisation’s
sustainable competitive advantage (BCG, 2020).
However, there does not seem to be a single recognised framework or methodology within the
literature that suggests the key components an organisation must have and/or take to have or
develop a ‘data-driven culture’. In addition, there is a further gap in prior scholarly literature when
looking to appraise firstly the culture as a holistic concept, and secondly a data-driven culture
within the UK construction industry. This is of fundamental importance for the purpose of this
research project as the research is premised on an organisation in the UK construction industry
with specific attention to procurement professionals.
2.3.1 Characteristics of a data-driven culture:
Typically, conversation regarding data-driven organisations has concentrated on big data,
analytics tools and technology enablement that have ensured storage, the processing, and
analysis of the data is cheaper and faster (Garcia-Perez, 2019). Whilst these components are
significant, to generate a data-driven culture enterprise-wide it is imperative that organisations
move beyond a mere handful of efficacious data initiatives and siloed data excellence restricted
to some business functions.
According to Skyrius et al. (2016), a data-driven culture combines the use of facts in decision-
making. In this type of culture, Cech et al. (2018) suggests that data is treated as a strategic
organisational asset by ensuring all data, unless confidential/sensitive, is widely and regularly
available. These businesses focus on securing, cleansing, and classifying relevant data cross-
functionally and where possible, cross-business (Mikalef, 2018). A further characteristic of a data-
culture is that it encourages everyday experimentation to learn and improve, a ‘fail fast, learn
quick’ culture norm (Garcia-Perez, 2019).

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The environment, according to the research scholars, recognises that a robust basis of data is
imperious for distinguishing an organisation through machine learning and artificial intelligence
(Ertem and Kilinc, 2018; Farrell, 2018; Garcia-Perez, 2019). Primarily, a data-culture is one with an
enhanced composition of data literacy and has a fundamental belief that data supports all
employees in their performance (Halaweh and El Massry, 2015).
Moreover, it has been argued that the creation of a data-driven culture, is premised at its core,
on the leadership team within the business (Garcia-Perez, 2019). Schein (2016) proposed that
executive sponsorship is compulsory but today, still insufficient. Whereas Skyrius et al. (2016) notes
that executives and their direct leadership team must endeavour to go significantly beyond
merely supporting data as a core component of its culture.
It is imperative, according to McAfee and Brynjolfsson (2012), that c-suite executives remain fully
involved and engaged, visibly concatenating facts (the data) with a good business decision.
Even if, this goes against everything they know from experience (Delallo, 2019)
Due to the complexity of the ‘Data Culture’ concept and fragmentation in specific literature,
table 2 below articulates the three characteristics that are continuously discussed in academia as
being the key to the success of a data culture:
Theme/Characteristic Literature Detail
Data-driven
Decision-making
• Chen, J, Hagstroem, M.,
Rifai, K. and Selah, M. (2017).
• Mauri, T. (2020).
• Farrell, M. (2018).
• Díaz, A. and Saleh, T. (2020).
• McAfee, A. and Brynjolfsson,
E. (2012).
• Tableau (2019).
Chen et al. (2017) proposes that data-driven decision-making is premised on an
organisational culture in which small and large decisions are profoundly informed by
data. Mauri (2020) suggests that a data culture is one in which data, not guesses, is
used to solve problems and where employees are content with continuous change.
Other articles suggest that data cultures are often referred to as a perpetual process,
transitioning from a ‘knowing’ culture to a ‘learning culture’ (Farrell, 2018).
Diaz and Selah (2017) note the competitive advantage created for organisations if
they succeed in concatenating tools, talent, data, and decision-making. They also
state that a data culture is a decision culture. McAfee and Brynjolfsson (2012) concur
but refer to this type of culture as a ‘decision-making’ culture which, by making
decisions based on data, enables employees to make improved decisions.
The software mammoth, Tableau (2019) defines data-driven decision-making “as using
facts, metrics, and data to guide strategic business decisions that align with your
goals, objectives, and initiatives. When organisations realise the full value of their data,
that means everyone – whether you’re a business analyst, sales manager, or human
resource specialist – is empowered to make better decisions with data, every day”.
• BCG (2020).
A fail and learn fast culture has been defined by many academics as a ‘test and learn
culture’, a data-driven test and learn culture (BCG, 2020). Chen et al. (2017) suggests
that demanding individuals failing fast is a crucial attribute of an innovative internal
culture. Mistakes must be a source of continuous improvement and enabling a
workforce to embrace this mentality and/or even celebrate failure, will foster a

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Fail fast, learn fast
• Delallo, L (2019).
• Gourévitch, A., Fæste, L.,
Baltassis, E. and Marx, J.
(2020).
• Burnison, G. (2020).
• Wingard, J. (2020).
successful culture. Common definitions note the focus on learning from experiments or
mistakes, “to move more quickly in today’s margin driven world, we have to learn from
failures and then quickly move to the next version” (Delallo, 2019, pp. 6), requiring staff
to fail fast is the most significant attribute of an innovative, learning culture (Chen et
al., 2017).
A key element of working with a vast amount of data necessitates organisations to
generate new ideas and insights quickly, test the ideas and then decide whether to
continue or not. When working in this fashion, it is imperative to communicate
failures/mistakes with team-members quickly and without embarrassment “because
mistakes in a data culture are seen as a source of continuous improvement for the
following iteration” (Gourevitch et al., 2017, pp 28).
Fail early, fail fast and fail often. Embracing failure is a means to a successful end
(Chen et al., 2017). “Instead of fearing failure, become empowered by it” (Burnison,
2020). Broadly speaking, according to Wingard (2020), it is all about failing in a smart
manner, making the notional assumption that failure will lead to valuable learning.
A common language
• Díaz, A. and Saleh, T. (2020).
• Pettey, C. (2018).
• Barkholt, M. and Jessen, N.
(2020).
To transition to a data culture, it is paramount that employees of an organisation can
understand, appreciate, and easily converse about data (Chen et al. 2017). This starts
with leaders and their staff having the ability to speak and interchange
communication about basic data concepts (Díaz and Saleh, 2020).
According to Pettey (2018) one’s capability in communicating in data language is fast
becoming the new key component in organisational readiness. This, in turn, articulates
the importance of a common language in a data culture. Not merely in a data
context, but in every aspect of today’s business world the importance of a common
language is fundamental as it enables all staff to work towards the same goals with
the same understanding (Barkholt and Jessen 2020).
Table 2 – Characteristics of a data culture
2.3.2 The importance of a data-driven culture:
A data-driven culture is a new notion that pre-21st century, was not recognised in organisations
because there was marginal to no access to data and businesses depended upon human-
centric/intuition-based analysis to make decisions (Cech et al., 2018). As such, the premise of a
data-driven culture was only seen as significant when organisations started to heavily depend
upon data and the associated in decision-making (Barkholt and Jesssen 2020). Having a data-
driven culture enables organisations to utilise data as an asset for (a) business intelligence
initiatives (b) accountability, and (c) organisational learning (Slater, 2016).
Today, a significant majority of data maturity models specify culture as a principle, if not, the
leading aspect in data maturity and the associated success of business intelligence (Tavallaei et
al., 2015). Over the years, academics have attempted to provide the success criteria for
achieving a data culture, they are (a) intangible resources, (b) human knowledge, and (c)

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tangible resources. The academics suggest that these success criterions are established on
organisational culture and the distinct configuration between the organisations functions and the
technology that holds the data (Halaweh and El Massry, 2015).
Embracing a philosophy of data-driven decision-making assists in the successful delivery of
predetermined saving targets by ensuring they are fact-based decisions (Tavallaei et al., 2015).
This, in turn, ensures that financial risk can be mitigated as the data can be evidenced as a basis
as to why the decision was made (Garcia-Perez, 2018). This is of fundamental importance to
procurement functions, especially in the construction industry as the margins are typically below
2% and therefore, any savings target not met can have imperative consequence on an
organisations bottom line (ICE, 2019).
Nevertheless, this valued constituent of a data culture ensures that procurement initiatives are not
limited to a single function (procurement) but also to other functional stakeholders. Yeoh and
Popovic (2015) enforced this position by articulating that support must originate from executive
leadership downwards. With the adoption at this level, the data culture strategy cascades
downwards and widespread adoption enterprise wide becomes significantly easier (Mikalef,
2018).
2.4 Culture transformation:
In today’s ever-changing business environment, organisations look to transform their culture to
exploit human potential and to enable organisational change (Dimitrova, 2018). Vicen (2017)
suggests that business culture comprises of the unseen / unheard foundation that runs
concurrently with the visible processes, procedures, and actions within a business. According to
Dimitrova (2018) the foundational principles of which any organisation is built is (a) the company’s
values (b) internal standards and (c) accepted traditions.
Farrell (2018) advises that in any business hopeful of radical transformation or even a mere
modification to their company culture, the leaders must be present. Argenti (2017) notes that
leaders who are absent will be incapable of instilling a certain culture in their employees, who
often look to their leaders for guidance. Farrell (2018) concurs and further adds that leaders must
lead by example, raising the axiom of ‘actions speak louder than words’. Fundamentally, staff
react to pattern fortification and an organisation’s leadership team must deliver continued
consistency through their day-to-day business behaviour (Argenti, 2017).
Therefore, while the ideologies governing an organisational culture transformation are typically,
universal in nature, establishing a philosophy of data-driven decision-making has inimitable
elements that ought to be addressed by a business. In addition, Richards and Santilli (2017)
elucidated that data-driven cultures must have an executive board sponsorship. Calof et al.

16
(2017) explain that a top-down approach is best practice for corporate intelligence initiatives.
Fawcett (2013) expounded further, displaying that narrow, flat business structures are the most
efficacious in transforming any business culture toward a data-driven one.
As such, the accountability for educating a workforce sits with the leaders. The leader should work
to deliver instructional resources and training to team members with the definitive objective of
coaching staff as to how to consume and process data for themselves (McLeod et al., 2018). Any
business wanting to implement a culture defined by data-driven decision-making ought to follow
the best practices proposed by Calof et al. (2017) and substantiated by academics such as
McLeod et al. (2018).
Contemporary organisations, however, are often incapable of implementing a culture that is
aligned to their businesses strategic goals, leading to weakened financial results (Grover et al.,
2018). Galbraith (2014) noted that even businesses with the technical ability/skills to execute
organisational intelligence initiatives have difficulties implementing a satisfactory data-driven
culture. Olufemi (2019) states that businesses may have an appetite to implement a culture that is
predicated on data-driven decision-making but are frequently ill-equipped to change their
culture. Often, functional managers struggle with cultural change because they see business
intelligence as a contest to their decision-making abilities and to their authority (Galbraith, 2014).
Therefore, there is a distinct correlation between culture transformation and an organisation’s
leadership team and individual managers. To execute decisions established on data, a leader
must foster a culture whereby they are willing to decide on the facts rather than rely on their
intuition, even if it is fundamentally against what they believe (Argenti, 2017).
2.5 Leadership:
In today’s digitally connected business environment, leadership is becoming ever-increasingly
more complex (Schein, 2016). McAfee and Brynjolfsson (2012) infer that for leaders to adopt a
data-driven culture they must personally move away from making human-centric (gut feel)
decisions to decisions premised on data. Brown (2013) advises that the true value for an
organisation is only unlocked when data is applied to explain insights that intuition previously
solved.
It has become inevitable that essential change must be made throughout organisations from top-
down to ensure they are fit for purpose in the new digitally, ever-changing business world. As
such, introducing a handful of digital initiatives and new procedures does not constitute a digital
strategy and fundamentally, will not be enough for most (Brown, 2013). A research study by Tran
et al. (2019) found that of 70 global leading, data mature organisations, such as Amazon, Apple,
GE and Mastercard, 96% of them acknowledged that business transformation, greater agility to

17
changing environments and data-driven leadership were the key determining factors in their
sustainable competitive advantage.
Considering it is evident from academic literature that the root cause of the fragmentation, a
lack of culture to support decisions making predicated on data, is caused by leadership styles,
behaviours, and beliefs (Barkholt and Jessen, 2020). However, this same cultural problem can be
solved by the very same leadership team that causes the fragmentation (Schein, 2016). It requires
functional leaders to adapt/change the way in which they think about culture as a concept and
necessitates the critical role the leader plays in culture transition (Díaz, Rowshankish and Saleh,
2020).
Primarily, without the commitment and support of leaders, organisations struggle to implement
the desired cultural components that will ensure data-driven practices are efficient (Barkholt and
Jessen, 2020). Changing an organisations culture starts by changing the leader (Schein, 2016).
3. Methodology:
Saunders et al. (2016) differentiate between the term methodology and method. Methodology
refers to the cognitive reasoning for the specific research, analysis and how new knowledge is
created, handled and subsequently, perceived. In comparison, the method has been defined as
“the techniques and procedures utilized to acquire the data, referring to the type of data
collection” (Schein, 2016, pp 14-19). For example, interviews and questionnaires, as well as other
qualitative and quantitative procedures. Therefore, the method should be considered a
component of the research and an instrument to enable the answering of a specific set of
research questions (Barkholt and Jessen, 2020).
As such, the following section will describe the methods utilised throughout this case study. The
research design will be discussed in section 3.1. Section 3.2 and 3.3 are predicated on the
approach and data collection methods. In section 3.4, the participant sampling will be
documented for this study. Section 3.5 will discuss the data analysis techniques used for the
research. Lastly, section 3.6 will review the study’s feasibility and section 3.7 will discuss research
ethics.
3.1 Design:
Saunders et al. (2009) propose the ‘Research Onion’. This model is a visual representation of the
various stages involved in the development of research studies. The layers of the onion offer a
detailed description and give the different routes a researcher can take through which a
research methodology can be considered (Saunders et al., (2009). The general premise of the

18
research process is about unwrapping the onion, layer by layer. To reach the core, the researcher
must unwrap the outer layers in sequence. Figure 1 below gives a graphical representation of the
‘Research Onion’:
Figure 1 – Research onion (Saunders et al., 2009)
Bryman (2012) suggests that there are three key research philosophies that are considered
significant in the research process, see table 3 below:
Ontology:
Ontology is the study of reality. It
discusses the nature of reality, what
comes to one’s mind when
completing research and the
impact on the surroundings and
society (Bryman, 2012). Within
Ontology, there are three
philosophical positions:
• Objectivism is the understanding of a social occurrence and the varied
meaning that individuals attach to that event. It separates the impact of
social phenomena upon different individuals (Saunders et al., 2016).
• Constructivism is the opposite of objectivism because it proposes that it is
individuals that generate social phenomena.
• Pragmatism utilises various theories to identify an issue and propose a
solution. It is an alternative to objectivism and constructivism and when
compared with others it should be considered relatively new.
Epistemology:
Inherently utilised in scientific
research. It looks to find
acceptable, common knowledge
and address the associated facts
accordingly. Importantly, after the
acceptable knowledge has been
defined about the field of
• Positivism uses research questions that can be tested in practice. By using the
generally excepted knowledge of people it assists the researcher in finding
an explanation.
• Realism enables one to utilise new methods of researching. To some extent,
it is the same as positivism however, the slight difference being that it does
not support scientific methods.

19
research, information must be
given, and rigorous testing of the
results need to occur (Bryman,
2012). Within Epistemology, there
are three philosophical positions:
• Interpretivism supports the researcher in interpreting how individuals
understand their actions and the actions of others. It assists the researcher in
comprehending individual’s participation in social life and culture.
Axiology:
Axiology is the study of judgement about the value (Bryman, 2012). Precisely, the premise of “axiology is engaged with
assessment of the role of researcher's own value on all stages of the research process” (Saunders et al., 2016, pp 18).
Table 3 – Detailed research philosophies
3.2 Approach:
Cresswell (2013) proposes that a research approach is a procedure and plan that involves several
steps of wide-ranging assumptions to comprehensive methods of data collection, data analysis
and interpretation. In the 2001 book on ‘Social Research: Process, Issues and Methods’, Timothy
May proposes that the overall research process requires the amalgamation of empirical work
together with the assembly of statistics that can refute, contest or concur with theoretical
hypothesis. This, in turn, enables explanation of diverse observations. To date, there are three
different approaches to advanced research, qualitative, quantitative, and mixed methods.
Incontestably, all three approaches to research are not as discrete as they first appear (Creswell,
2014).
Quantitative and qualitative methods should not be regarded as distinct categories, rigid,
contraries, and/or dichotomies (Cresswell, 2014). Alternatively, they signify diverse ends on a
continuum (Benz and Newman, 1998). However, the mixed method approach occupies the
centre of the continuum as it consolidates facets of both quantitative and qualitative
approaches (Saunders et al., 2016).
Importantly, there is a distinct correlation between qualitative research and a process known as
‘induction’. This approach follows data, it is an observation to understand and categorise
phenomena and it is, in the main, concerned with the context in which events take place
(Saunders et al., 2016). Conversely, the deductive reasoning approach is utilised in the data
collection phase in relation to a specific element of investigation. It is from this data collected
that the researcher develops different concepts and theories (Cresswell, 2014).
This research followed the case study manner in an interpretivism philosophical approach. The
case study / interpretivism consolidated approach involves an in-depth study of a single or
collective issue and, typically, follows the qualitative research approach and is used for this
research. Specifically, for this case study on OA, the qualitative research method is considered
more appropriate as this study was undertaken to ascertain greater insight into data cultures, the
importance of leadership in data-driven cultures and what actions are needed to scale a data
culture.

20
This approach enabled the researcher to increase transparency of the internal perception of
culture, and more specifically data-driven cultures. In comparison, a quantitative research
approach would be broader in scale, numerically based and significantly more structured
(Cresswell, 2014).
3.3 Data collection:
For this case study on OA, a semi-structured interview approach was utilised. This approach
permitted the interviewees the ability to provide intricate, yet flexible, wide-ranging responses
which allowed the researcher the opportunity to ascertain the interviewees true feelings.
Moreover, this structure enabled all participants to answer the enquiries in their own way, which
according to Cresswell (2014) is something that a standardised, attentive interview encourages.
Nevertheless, while the semi-structured interview process has noteworthy importance in the
collection of detailed, rich data, there are some accompanying limitations. Kumar (2005)
proposes that there can be a difference in the collaboration between researcher and participant
because each interview is unique and the quality of interviewee response may vary due to the
experience, commitment, skills, and willingness of participants and therefore, evidence obtained
can be knowingly different (Cresswell, 2014).
Data collection for this case study research took place between August – September 2021. All
interviews were recorded using a Dictaphone and the associated interview content was
transcribed verbatim. All eight participants were unrelated to the researcher from a personal
perspective and were invited via electronic method (email) which explained all the case study
details of the research and what the interviewee would gain from the research. The researcher
offered all contributors the choice of conducting the interviews at their workplace (any of OA’s
office space), in a social setting and/or via video conference, especially with Covid-19 restrictions
still prevalent, a potential limitation/risk to this study.
Importantly, given the change in working environment over the past c. 18-months, it was felt that
by offering several different interview locations in terms of interview surroundings, the contributors
would have the opportunity to connect and collaborate more openly and freely regarding the
data culture research topic.
The semi structured interview method was selected as this is frequently completed with a
sequence of questions in an archetypal interview-style procedure (Saunders et al., 2016).
Nonetheless, the arrangement of interview enquiries is often diverse (Cresswell, 2014). In addition,
a further beneficial characteristic linked with this method is that there is autonomy for the
researcher to probe and explore supplementary questions, though subsequently permitting
rapport and empathy to evolve between the researcher and participant (Byman, 2009).

21
A schedule was produced to detail the semi-structured interview questions before the data
collection phase begun. This enabled the researcher to meticulously plan and iterate the
question-set and thus, structure and flow was maintained during the interview process. All
participants were asked the same set of open-ended enquiries relating to a data culture,
leadership through data culture transition, as well as the actions required for the embedment of a
data culture within a procurement function. See appendix 3 for a full list of interview questions.
Sarantakos (2012) suggests that open-ended questions enable the interviewee more autonomy
to articulate their opinions, ideas, and beliefs. The interview placeholders were for one hour (60-
minutes). However, the schedule was flexible for a shorter/longer duration should the participant
have felt the need to utilise less/more time. All participants were asked to sign a participant
consent letter (appendix 1) and were all given an interview guide prior to the interview
commencing (appendix 2).
To ensure continuity and fluidity, a pilot interview was conducted in August 2021 before the main
research data collection phase commenced. The trial interview enabled the structure, delivery of
questions by the researcher and identification of difficult questions to be acknowledged and
adjusted prior to the live phase. Nevertheless, for completeness, the data collection from the pilot
interview was not incorporated in the final research analysis.
3.4 Sampling:
The purposive sampling method has been chosen for this research study. Saunders et al. (2016)
allude that this sampling method is strategic in qualitative studies because it attempts to establish
improved communication between sampling and the associated research questions.
The sampling criteria for inclusion is predicated on both senior, global procurement stakeholders,
as well as, a secondary sample of participants that are not senior stakeholders, but all currently
work in OA’s procurement function. All participants of this study have worked for OA a minimum
of twelve months and the age range of the participants was not restricted. Table 4 and 5 below is
a profile of each interviewee:
Primary Participants
Participant Interview Sequence Job Title
SL001 01 Head of Category Management
SL002 02 Head of Procurement
SL003 03 Head of Operations
Table 4 – Profile of primary participants

22
Supporting Participants
Participant Interview Sequence Job Title
ML004 04 Senior Category Manager
ML005 05 Category Leader
ML006 06 Data Analyst
ML007 07 Transformation Manager
ML008 08 Assistant Category Manager
Table 5 – Profile of secondary participants
The purpose of having a primary and secondary participant group is to give an in-depth, well-
rounded viewpoint of a study and allows the researcher to observe similarities or discrepancies in
understanding (Cresswell, 2014). The researcher was employed by the organisation the study is
premised on, and therefore approached all participants through historical work relationships.
Eight participants were enrolled in this study.
3.5 Data analysis:
Braun and Clarke (2006) advise that thematic analysis is a research method of analysing
qualitative data across a specific data set to identify, analyse and report recurring patterns. The
method is not merely used to describe data but to interpret the process of selecting a code
structure and subsequently, determine themes. Figure 2 below articulates the thematic cycle in
visual form.
Figure 2 – Thematic analysis visual
NVivo software was utilised in the thematic analysis in this case study. All material from each
individual interview was transcribed, specifically coded, intricately analysed, interpreted, and
then validated (Sarantakos, 2012). Initially, the transcribing method was used to assist the
researcher in gaining a superior understanding of the topical matter by continually listening to the
audio records and by repetitively reading the transcribed interview (Cresswell, 2014). According
Qualitative Data Codes Themes
Coding Iterative Comparison

23
to Bryman (2009) an integral element of qualitative research is the coding of keywords, applied to
systematise arrangement and then the text categorised.
As such, the data was categorised, prepared, and then analysed into themes and with the
potential for further sub-themes that could materialise into a secondary coding phase. Those
themes/sub-themes were allocated a specific code, see appendix 4. In the interpretation phase,
an identification of recurrent themes and similarities or differences will be controlled within the
data.
Lastly, the data validation phase enabled the researcher to validate the legitimacy of
comprehension by evaluating all the interview transcripts, audial file, and coding assembly
(Saunders et al., 2016). This, in effect, enables a researcher to validate or modify hypothesis
formerly arrived on (Bryman, 2009).
3.6 Feasibility:
The time element of executing a research project while completing concurrent, MBA modules
should be highlighted as a significant risk. This, in effect, could cause quality/performance
challenges to the delivery of the research project because the researcher may, at times, need to
prioritise the adjacent MBA modules and/or work commitments over the research project.
However, these challenges were mitigated against through intricate planning, time management
and close alignment with the researcher’s supervisor.
Nevertheless, the aforementioned challenge is not the only risk that should be considered within
this research project. The primary sample group, noted in table 4 in section 3.4, are all senior level
stakeholders within the procurement function of OA. This, in turn, presents its own challenge and
table 6 below summarises the risks to the successful execution of the data collection phase of the
project:

24
Table 6 – Sample risks
Primary Participants
Risk Name Risk Type Description Proposed Mitigation
Time
constraints
Reduced
sampling –
Inability to find
replacements
There was a risk that participants may not have time to
attend a 1-hour interview. This, in turn, could have
reduced the participants targeted in the primary
sampling. As such, finding replacements could have
been challenging as this case study is predicated on
OA’s procurement team.
Early participant involvement. It was
imperative that the researcher stayed in
close contact with the participants to ensure
they were aware of the study. Ensuring that
the proposed timescale had slippage if one
or more participants were unable to attend
the interview.
Embarrassment
/ Unwillingness
Reduced senior
level dataset –
Reduced validity
This case study topic is sensitive as it could expose a
leader’s knowledge on an organisational-wide topic.
There was a risk that the senior stakeholders would not
willingly answer the questions or could answer the
questions as to what they think the answer should be. This
could have had an implication upon the validity of the
study and make it difficult in the coding phase of this
study.
The researcher continually advised that the
research was confidential. Whilst the findings
will be discussed, no personal information of
an interviewee was documented in the
analysis. Furthermore, all interview transcripts
were stored on an encrypted drive on the
researcher’s laptop.
Knowledge on
topic
Poor response
from interviews –
Reduced
reliability
A further potential risk was that the senior level
stakeholders would not/could not answer the semi-
structured interview questions due to a lack of
knowledge on the subject matter. Given the sensitive
notion of this study and the underlying premise of
obtaining a better understanding of a data culture, a
reduced and/or inability to answer basic questions could
have reduced the validity of the study.
The researcher softened the question and/or
amended/re-sequenced the question-set to
ensure participants fully understood.
Supporting Participants
Risk Name Risk Type Description Proposed Mitigation
Embarrassment
/ Unwillingness
Reduced
supporting
dataset –
Reduced validity
The supporting participants are not in senior positions but
could recognise the sensitivities around this study. As
such, they may not have been willing to divulge their
personal views around data, culture, and leadership for
fear that if they do, it could be used against them by
their management team.
This research is strictly confidential. The
findings have been discussed, but nothing
relating to an individual participant was
documented. The researcher continuously
made it clear in the initial advisory call, as
well as the formal interview.
Emotion Personal feelings
/ negativity
The semi-structured interviews offered the junior – middle
management employees the opportunity to have their
say. Part of the research was regarding leadership,
specifically in the context of ‘Is leadership important in a
data-driven culture?’ – a participant could answer the
questions relating to their personal experience of their
leader.
Whilst this could be considered a risk, the
researcher had the ability to re-clarify the
purpose of the study. Personal opinions
assisted in understanding/answering the
research questions.

25
3.7 Research ethics:
In any form of research and when specifically conducting a case study on a topical organisation,
the researcher must intricately understand the potential impact their research may have on the
participants, the topical organisation and/or society in general. Specifically, in this research,
anonymity of participants was fundamental as the premise of the research is about one’s feelings,
beliefs, and behaviours. All topics which are considered personally sensitive, especially when
participants are in a senior position. Table 7 below discusses some of the ethical considerations
given to this study:
Consideration Element
No. 1
Participants were provided with a ‘Participant Information’ form 2-weeks in advance of their interview.
No. 2
Participants were provided with a ‘Participant Consent’ form 2-weeks in advance of their interview. This
form must be signed, dated, and returned to the researcher before the interview commences.
No. 3
Before the interview commences, all participants were advised they are under no obligation to answer
any of the questions they do not feel comfortable answering.
No. 4
All participants were advised at the beginning of the interview they can pause and/or stop the interview
if they are not comfortable at any time.
No. 5
Anonymity was provided to all research analysis/discussion. Confidentially is a key component between
researcher and interviewee.
Table 7 – Ethical considerations
4. Findings and Discussion:
The procedure in which an organisation designs and implements a culture predicated on data-
driven decision-making is complex (Rogers, 2020). The process can take a significant amount of
time and spans the business in its entirety. Furthermore, culture transformation necessitates the full
participation of employees, in all areas and at all levels of the organisation. The findings of this
research suggests that such a culture needs a substantial quantity of trust in organisational
processes, technological architecture, and fundamentally, fellow employees.
Transforming a culture toward one that not merely supports but encourages data-driven decision-
making needs functional teamwork and the endorsement of the business intelligence team. The
need for significant dependence on processes to govern connections with computer systems
and data, as well as the advancement of decision support software was also identified by the
participants of this research. Firstly, the findings assist in providing evidence to support the
questions in this research project and secondly, to serve the requirements acknowledged in the
problem statement of this project.

26
This research project addresses some of the difficulties a UK construction entity has in enabling a
data-driven culture. Fawcett (2013) proposes that procurement leaders habitually, are incapable
of supporting data-driven decision-making because of the consequences it has on their personal
decision-making power.
The findings of the research are founded on 16 primary research questions. The exploration into
these questions were conducted in eight semi-structured interviews with participants from a large
UK construction organisation. The interviews highlighted several distinguishing themes,
compounded by numerous subthemes that offer intricate insight into the research questions.
The main themes highlighted were (a) data-driven decision-making, trust, teamwork, and
technology (b) encourage curiosity and a ‘fail fast’ growth mentality, and (c) a common
language for data and design of work procedures/processes. Theme one focused on employee’s
ability to execute a data-driven decision but only if they trust the maturity of the data (enhanced
analytics).
The secondary themes highlighted were that (a) data cultures are predicated on subconscious
(micro-decision level) data-driven decisions throughout the team even when the manager
and/or leader are not involved. And (b) technology enablement was also documented as a
main constituent of a data-driven cultural norm.
Identified Themes (1) Sample Statements
Data-driven decision-making
“Important decisions are often made on gut-feel, but the problem with that
when you are working in an industry with low margins is the risk”
“We must, where possible, make fact-based decisions”
“In a world where data drives everything in our personal and business life, it’s
imperative we use that data to make decisions”
Trust in the data
“We have to be able to trust the data we use to make the decisions, if we
want a data-driven culture”
“Rubbish data in means rubbish data out, that is why I struggle to trust our
data”
“The integrity of the data is imperative”
Teamwork culture
“To me, a data-culture is a culture where all the team work together to use
the data to drive everything”
“The best cultures are predicated on team-working, all pulling in the same
direction”

27
Technology enablement
“I think we need to streamline the internal software-suite”
“We have lots of different tools, but the issue is the tools are not integrated. Its
log into one, log out, log back in and so on”
The second theme considered organisational culture and the way in which leaders compose a
team. The theme established that a leader’s role in a data culture is multi-faceted, they must (a)
encourage curiosity within the team, (b) team design and ensure staff have a growth mentality,
and (c) perpetually support any data-driven decision within the team and recognise with praise.
Curiosity / Fail fast culture
“Our leadership team should look to push us to challenge, rather than inherit
ideas”
“We have to be willing to challenge the status quo”
“My managers role, in my view, is build a platform where we can all
respectfully question / disagree”
Team design
“I want more data-fluent team members”
“The boss has to structure a team that can work with data and be happy to
not use personal decisions”
Celebrate failure and learn
“I want to be praised for having a go, even if I fail”
“Data cultures are ones that share and collaborate on ideas among the
team, staff are not scared to share a failure”
The third, and last theme that emerged from the study was premised on technology investment
within the business and more specifically, within a procurement function. The findings suggest
encouragement from leaders to adopt a purpose driven, data culture and there must be robust
(a) functional policies / procedures, (b) culture transformation, (c) systems integration/technology
investment and (d) championed cross-functional collaboration.

28
Standardised policies / language
“Everybody in the team needs to be talking the same language”
“I think the different policies per function that do not align from a process
perspective don’t help”
Culture transformation
“It absolutely has to start with the leadership team driving the change”
“What comes to my mind is Exec-level sponsorship as otherwise nothing
will change”
“I think we need to define the future state from a culture perspective,
that is not there yet”
Technology investment
“Our systems are outdated”
“We spend about £3 of revenue per year of tech-investment”
“I think our systems are the issue. Inputting data is normal to staff these
days, the issue is where they are inputting is clunky, un-helpful and un-
reliable”
Cross-functional collaboration
“You cannot change a culture within one function”
“I think working together with other teams is the only way you can
change cultural norms”
“I work with the IT team a lot; they can help us here”
The interview questions (see appendix 3) were intended to assist in obtaining insights into the
three principal research questions. The research findings have been organised into several
different themes. These specific themes inform the understanding of the research questions. See
section 1.2 for the specific research questions.
4.1 RQ1 - What constitutes a data-driven culture?
Research question one, requesting interviewees to explain the elements of a data-driven culture,
seeks to distinguish the individualistic components enabling a workplace atmosphere
encouraging of data-driven decision-making. Interviewees described several wide-ranging
elements that contained fragments of all the themes within the findings of this research.
Responding to the first research question, participants inclined to concur that a data-driven
culture is largely the outcome of enhancements to an organisation’s data maturity.

29
Furthermore, the critical constituents of trust and a data mature function, according to
interviewees, is the ability for individuals to intrinsically rely on the data they are making the
decision on. As such, the technological connection a decision maker establishes with their data is
founded on trust. This is distinctly aligned to the premise of Garcia-Perez (2018) research that
noted ‘trust’ is important to establishing a fact-premised culture, utilising the adoption of
technology as an intermediary factor. This form of trust, according to Cech at al. (2018), is gained
through internal success from the data-driven decision.
Of the eight participants that were interviewed for this study, seven, or 88%, specifically noted that
‘trust in the data’ is a vital element of a data-driven culture. Most of the participants insinuated
that trust in the data should act as a representation to quantify the level to which a business is
data-driven. Respondent 1 (SL001-01) mentioned that a prerequisite of a data-driven culture is
trust:
“If you cannot trust the data you are supposed to be relying upon to make the decision then it
makes the data itself redundant. Therefore, staff will continue to make decisions based on their
previous experience… even if that is wrong too”.
Participant SL002-02 agreed and further mentioned that ‘Our issue is that some of the data is
trustworthy, and some isn’t. This leaves it difficult to trust any of it’. Interviewee ML006-06 noted
that the ‘data must be readily available’ with participant ML003-03 mentioning that ‘data must
be correct day after day, month after month’ with no discrepancies ‘for it to be trusted and a
fact-based decision made’.
A mandatory constituent of becoming data-driven is trustworthiness. Fundamentally, an unstable
or inaccurate set of data insights erodes confidence in the strategic initiatives and primarily, the
person making the decision. Interviewees acknowledged five key elements of trust in data, with
(a) nine participants noting consistency, (b) six articulating accuracies, (c) five proposing the
availability, (d) three suggesting integrity, and (e) eight communicating the importance of the
data’s robustness. Organisations with datasets that have these assets, according to the
participants, provide employees the trust needed in the data. In turn, providing a significant
component for data-driven cultures.
Furthermore, data maturity signifies a organisation’s relationship with data and the downstream
associated decision-making processes. Participants ML007-07 and SL001-01 suggested the quality
and maturity of the data as a key constituent, noting that ‘we often make decisions based on
operational datasets’. In addition, a culture of teamwork, according to 50% of respondents, or
four of eight, was a defining factor of a data-driven culture as it is the ‘business environment that
dictates whether an organisation is data-driven’ (ML007-07). Producing this type of business

30
environment generates optimistic results. Those that contributed said that this was a mandatory
mechanism of a data-driven culture.
Throughout the research, numerous elements of a business atmosphere were identified, with (a)
two interviewees identifying responsibility, (b) three noting a culture of sharing, (c) one said a
‘positive, happy environment’ (SL003-03), (d) two proposing the need to celebrate success.
Interestingly, these findings can be correlated to Farrells (2018) research that found a data-culture
fosters an environment in which staff feel positive day-to-day, and a dataset is utilised for a
positive outcome.
Data-driven decision-making was also referenced by multiple participants as a component of a
data-culture. Six of eight, or 75%, noted the importance of decisions being premised on data.
ML004-04 suggested that fact-cultures, are a norm when all staff, ‘up and down the hierarchy
make decisions based on data’.
Whereas ML008-8 proposed data-driven decision-making is often ‘subconscious’, with SL003-03
noting data-driven decision-making has to come ‘from the leadership team to subordinates’. All
findings in this theme align to the discussion by Chen et al., (2017) who notes that data-driven
decision-making is premised on an organisational culture in which small and large decisions are
profoundly informed by data. The findings also correlate to Hasan (2014) and McAfee and
Brynjolfsson (2012) who both agree that data-driven decision-making is a key attribute of a data-
culture but also, concur that data-driven decision-making must come from top-down and is
‘instinctive’ in nature.
A further theme highlighted was technology and systems and their involvement in the notional
concept of a data-driven culture. Participants frequently discussed the importance of the
software they access to capture the data for a decision to be made later. SL001-01 and SL002-02
concurred that ‘for a culture to be considered data-driven, the systems the staff use day-to-day
must be easy to use to input the data’(SL001-01) and SL002-02 noting that ‘one of our biggest
challenges is that we don’t have the systems enabled and integrated to reduce the manual
input, hence we have poor data’.
Some participants noted that the procurement profession is become ever-increasingly more
digitally enabled, which in itself, ‘fosters a data-culture as humans cannot execute the decisions
the technology can at pace’ (SL003-03). Importantly, this finding is distinctly aligned to the
research of Garcia-Perez (2019) and the key characteristic of a data-culture of being one that
can execute data-driven decisions, at speed for sustainable competitive advantage.

31
The constituents of data maturity and a data-driven culture, as detailed by respondents, can be
fairly correlated to the conceptual model established by Cech et al. (2018) and the
characterisations provided by Skyrius et al. (2016), Farah 2017 and Chen and Nath (2018). In the
model provided by Cech et al. (2018), data-driven cultures comprised of business processes,
team design and technology capabilities.
However, this same model does not include specific references to the critical notion of trust in a
data-driven culture. Contrariwise, interviewees rarely conversed about the progressed phases of
organisational data maturity recognised by Cech et al. (2018). This included the adoption of
prescriptive and predictive statistical models (Cech et al., 2018).
As articulated by Farah (2017), Chen and Nath (2018) and Cech et al. (2018), an organisation’s
data maturity refers to the capability of an organisation to gather, store, integrate, and report
specific insights from the data, compounded by the organisation’s ability to establish functional
processes to support data-driven decision-making. With this definition, participants of this study
and research scholars alike are aligned in their support of environments that foster process-driven
cultures, as well as a functions skillset. Nevertheless, but important to note, academic researchers
do not specifically mention teamwork as a theme.
4.2 RQ2 – Is leadership important in a data-driven culture?
The second research question seeks to understand the importance of leadership on how a data
culture can manifest. The majority of those interviewed in the study mentioned the importance of
both the functional leadership teams, as well as the executive leadership teams behaviours. 75%,
or six of eight, interviewees articulated that ‘failure’ must be celebrated because ‘it means that
staff had at least tried’ (ML007-07). Participants spoke about a culture that cares more about
teamwork and ‘having a go’ (ML004-04) than one that is ‘always on damage control’ (ML006-06).
Furthermore, two participants mentioned that for a data-first strategy to be ingrained in staff, the
leaders of the business must display that ideology if subordinates are to follow. These findings are
noticeably interrelated to Schein’s (2016) ‘espoused beliefs’ layer that discusses the underlying
aspirations of a company. The aspirations, according to some participants, should be for the
organisation to foster a culture of ‘fail fast, learn quick’ (SL002-02).
In addition, a further theme highlighted was curiosity. Participants ML004-04 and ML005-05
suggested that leaders must encourage the team to be curious, to ‘challenge the status quo’
and be willing to challenge assumptions. Furthermore, 38%, or three of eight, interviewees
mentioned that a culture of curiosity is an atmosphere predicated on a willingness to learn new
skills, to ‘unlearn’ (SL003-03) where necessary but fundamentally, it is a business environment that
allows staff to ‘speak up’ and to ‘share ideas with colleagues’ (ML006-06).

32
Participants and scholars are heavily aligned in that they agree that a data culture is one that
works on a ‘fail fast, learn quick’ methodology whereby leaders encourage an environment for
staff to try new ideas, share knowledge and challenge norms which leads to new valuable ideas
(Chen et al, 2017; Mikelef, 2018; Wingard, 2020).
The findings demonstrate that there are specific tasks and behaviours that leaders can encourage
to help support the ongoing deployment of a data-driven organisation. It was clear that whilst some
leaders were portrayed in a positive light, others were less so.
The perception was that leaders have a significant role in any data culture programme, and in
some cases, leaders fell into two categories. For instance, a response from ML004-04, who
described the approach as:
“The investment in training is great to see – we all know there are overhead challenges across the
business and our CPO has stuck his neck out to make sure digitisation is at the top of the agenda”.
While also describing the overall leadership approach as:
“...our processes and procedures include limited data-driven decision points – whilst our vision is
strong, the systems and processes we employ are still weak when it comes to data”.
The concept of themes not being mutually exclusive was further supported by SL001-01, who
referred to positive themes in communication, yet were somewhat frustrated around the leader’s
approach to using data. These findings concur with Galbraith (2014) who asserted that
organisational leadership teams have issues relinquishing decision-making power in favour of
data-driven decision-making.
“Our new head of function joined the business six months ago. Since then, I’ve seen a step change
in communication around data, both within and outside the function. What concerns me however,
is that when I ask around data at our annual conference or when I look to see who has logged
onto our dashboarding system, he has not even logged on? I struggle to see how someone can
communicate a clear strategy but not understand the detail”.
Both interviews provided an insight into the perception of the participants on how important leaders
are in a data-driven culture, this can also be in contrast with what the leader thinks when SL002-02
noted:
“We have set out our transformation strategy with clear KPI’s and targets”.

33
Which was contradicted by ML005-05 who said:
“I have a lack of confidence in the ability of my leadership team to deliver transformational
change: we can see digitisation happening in our own lives at pace, so why are we moving like a
snail?”
Moreover, according to interviewees, organisational leaders must assist their staff on the data
learning expedition by democratising access to data, encouraging learning opportunities and
offer staff the flexibility and the time to invest in the learning. Respondents noted that leaders can
do this by integrating up and ensuring re-skilling is part of staff objectives.
Participants SL001-01 and ML005-05’s views we aligned in that they felt that leaders must push
their staff on the data journey to interchange from being consumers of data, to analysts, to
resident data scientist and then pivot back to consumers in a perpetual cycle. Interviewee SL002-
02 said that ‘not all of the team will start in the same place in terms of data maturity’. One
participant mentioned that it is up to the leader to design and recruit a team that is ‘data fluent
in such a data enabled world’. Therefore, team design was another theme highlighted as
important by participants in responding to whether leadership is important in a data culture.
Functional and executive leaders have a significant role in dynamically fostering a data-driven
culture. The senior executive leaders of the business can show the rest of the organisation the
value of data by visibly and continuously using analytics in their decision-making and explicitly
displaying how they come to the decision using the data as evidence. These type of leadership
behaviours, proposed by interviewees, ‘… set an example to staff from top-down’ (SL001-01) by
provoking evocative alterations in organisational-wide behaviour. This discussion, according to
interviewees, was a key objective of the leadership team in successful data cultures.
Whilst cultural change can take a significantly long period to embed, the leaders of a business
must be unyielding in the pursuit of stabilising a data-centric business. McAfee and Brynjolfsson
(2012) inferred that for leaders to adopt a data-driven culture they must personally move away
from making human-centric (gut feel) decisions to decisions premised on data. In addition, Chen
et al (2017) noted that it is the behaviours of the leaders of a business that are important to
changing a culture. And furthermore, the trust staff have in their leader can either expedite or
hinder a cultural transition. Participants of this research study strongly reinforced these scholarly
views with a particular focus on leadership behaviours toward a top-down, example setting-
approach.
Finally, participants identified various leadership components that contribute to an organisation
with a data-driven culture. Respondents reiterated that ‘trust’, one of the key characteristics
highlighted in research question one, was not simply applicable to the data itself but trust in the

34
context of a subordinate trusting a leader to execute a data-driven decision even if, this
fundamentally went against their intuition. Nevertheless, it was evident from the interviews that
participants felt that leadership was not merely important but the overarching factor in a
successful data-driven culture.
4.3 RQ3 - What actions can organisations take to introduce a data-driven culture?
Finally, the last research question endeavours to recognise detailed actions an organisation can
take to introduce a culture driven by data. Participants, in their entirety, acknowledged that a
common language and standardised practices were of fundamental importance in introducing
a data culture. It was noted, by multiple participants, that different functions and ‘even some of
our own team refer to the same dataset in different ways’ (ML006-06). This can be associated with
the ‘trust’ constituent raised in research question one because ‘if we are not all talking the same
language for the same dataset, errors in decision-making occur and therefore, trust gets broken’
(ML007-07).
Whilst more aligned to organisational culture generally, there is a link between participants
comments and the research by Edgar Schein (2016). ‘Artifacts’, according to Schein (2016) are
the most observable or visible levels of culture within an organisation. They are described as the
observable language used and the physical documents. In the case of participants surveyed, this
could be ‘standardised practices, procedures and language for data’ (SL003-03).
Of the eight participants, five or 63% proposed that organisations must have a transition plan
and/or a culture transformation plan to facilitate a move toward a data-driven culture. However,
to change, an organisation must first accept that a change is required. SL001-01 proposed that it
is not just a functional action (procurement) but a ‘company-wide change programme that is
required’.
Respondent ML003-03 had the same view and went further to suggest that it ‘should be an
objective of every single employee to work through the transition’. According to other
participants, a data-culture within the procurement function is ‘absolutely worthless’ (ML004-04)
and ‘will fall over day-1’ (ML008-08) if the other business functions that cross-functionally
collaborate with procurement are not fully aligned.
Interviewee SL001-01 and SL003-03 described leadership as a key action for culture transition
toward being data-first. They noted that as the organisation is privately owned a hierarchical
structure exists and this is a challenge that the business faces in working towards becoming a
data-driven culture. These findings heavily support the views of Fawcett (2013) that notes a flat
business structure is the most efficacious in transforming any business culture toward a data-driven
one. This is further underpinned by Argenti (2017) who suggests that staff respond most to pattern

35
reinforcement and an organisations leaders must provide continued consistency through their
day-to-day business behaviour.
Participants also explained that organisations must have the correct staff to foster a data-driven
culture. According to ML004-04, there is ‘little point in a leadership team driving data-driven
decision-making if middle-managers are data illiterate’. Respondents explained that having the
correct team structure and personnel in the team enables collaboration intra-team, which in turn,
supports data-driven cultures. Participant SL001-01 said that the investment in the organisation’s
newly formed data team was a ‘good start towards respecting data as an asset within the
business’. Conversely, SL002-02 referenced the formation of the new data team but put more
emphasis on the fact that they are not respected in the business.
Cross-functional collaboration, another theme highlighted by respondents, is considered a
determinant action of a data culture because a supportive and collaborative environment assists
the growth of a team. Half of participants, or four of eight, proposed engaging with external
consultants to provide the business with technical advice to enable a data-driven culture.
Interestingly, this discussion correlates with the Mikalef’s (2018) views whereby successful actions
that leaders can take to empower data cultures are largely linked to working cross-functionally
and where possible, cross-business.
Lastly, respondents identified technology investment as an enabler for a data culture. 38%, or
three of eight, recommended that to expedite the implementation, investment in technology-
based solutions that assist with reporting, sales, procurement, and engineering could quickly build
trust in the data. This, in turn, enables data-driven decision-making. Some participants mentioned
the current technology platforms as being part of the issue.
Interviewee SL002-02 noted that the executive leadership team want a data culture within the
business, however, they ‘do not want to invest in the software solutions that assist in fostering a
data culture’. The findings of Chen et al. (2017), Hasan (2014) and Cech et al. (2018) all explicitly
reference the need for organisations to invest in technological based solutions to underpin data-
driven decision-making and subsequently, enforce their data maturity.
Applying recognised themes to the third research question has highlighted several actions
organisations can take to introduce a data culture. This refers to the standardisation of internal
polices and a common language utilised cross-functionally. Furthermore, participants explained
that transforming the company culture across the entire organisation is a prerequisite action to
introducing a data-culture.

36
Respondents added that, you cannot merely transform one functional culture (procurement
function), but it must be an enterprise-wide change programme with many noting that external
counsel from consultants can expedite and embed the change toward being a data culture.
According to participants, the staff, team structure, flatter hierarchy, and the investment in
technology enablement, were all additional actions necessary for a data-driven culture to
manifest. These actions satisfied the research parameters and identified specific activities
organisations can take to foster a data-driven culture.
4.4 Summary of research findings:
The themes identified in the methodology phase assisted in obtaining a greater understanding of
a data culture and supported specific detail with relation to the research questions. Interviewees
discussed multiple topics that were codified and categorised in several significant themes. Firstly,
respondents found that the manifestation of data maturity comes from trustworthy data, which in
turn, fosters a data culture. Participants also noted data-driven decision-making and a teamwork
culture as key constituents of a successful data culture.
Furthermore, it was agreed by all participants that leaders are imperative in data-driven cultures.
Moreover, it is the behaviour of leaders that must embolden and encourage curiosity as a trait
within their team, as well as celebrate failure as a cultural norm. This, in turn, fosters a fail fast
culture toward data-driven decision-making. In addition, team design/structure, according to
interviewees, was another important objective of their leadership team in enabling a data-driven
culture. Leaders must inspire their subordinates to learn. Moving from a data consumer to analyst
to expert and back to consumer on the next dataset.
Lastly, participants agreed on a set of multi-faceted actions organisations can take to introduce
a data culture. It was argued that by standardising policies and procedures, as well as utilising a
common data language would generate cross-functional collaboration and democratise data.
Additionally, a flatter hierarchical structure and team diversity in terms of data-competence /
fluency were also defined by participants as characteristics of a positive data culture.
Technology investment, participants argued, was also a critical enabler to data-driven decision-
making because it encourages trust in the data and assists in speedier execution of a data-drive-
decision.
It is important to note, this research was predicated on a large construction organisation and
more specifically, focus was given to a procurement function. However, it was evident
throughout from participants that a ‘data culture’ is far more holistic than a procurement function
and should be reviewed at enterprise-wide level. Fundamentally, according to interviewees,
having a sub/siloed data-driven culture in a procurement function is redundant and fails instantly.

37
Respondents noted this is because it is impossible to cross-functionally collaborate with other
functions who are less mature in a data-context, a key theme highlighted in research question
one and three.
4.5 Summary of findings in relation to the literature:
The findings of the study support the literature in relation to the three key characteristics of a data
culture commonly identified by academics; data-driven decision-making, fail fast, learn fast and
a common language (Chen et al, 2017; Cech et al, 2018; Skyrius et al, 2016; Barkholt and Jesssen
2020; Wingard, 2020; Farrell, 2018 and Garcia-Perez, 2019). Participants explained that the
characteristics serve as a behavioural baseline for a data culture and assist in enhancing an
organisation’s data maturity.
However, participants highlighted three further characteristics of a successful data culture. Firstly,
‘trust’ was recognised as a theme and a key constituent of a data culture. Respondents noted
that, without trust in the data, colleagues, and the leadership team the organisation will not
achieve data-driven decision-making and therefore, not escalate up the data maturity scale.
Secondly, ‘leadership’ was discussed as an imperative factor to positively transitioning and then
instilling a data culture. Participants articulated that without leadership support, from top-down,
data initiatives will fail. They noted that the leader must encourage curiosity as a behavioural trait,
for staff to challenge the status quo and work in a perpetual cycle of learning. This, according to
participants, was another key constituent of a data culture. Interestingly, whilst there is some
scholarly research noting the importance of leadership, it is considered a secondary, more holistic
element to data cultures, rather than a primary enabler discussed by the participants in this
research study.
Lastly, participants noted the importance of ‘technology’ both from an investment and
enablement perspective. Technology capabilities have been noted by multiple scholars with
reference to data-driven decision-making (Chen et al, 2017; Hasan, 2014 and Cech et al, 2018).
However, the intricate detail was not discussed. In comparison, participants of this study
articulated distinct emphasis on the importance of the software used to capture the data, the
investment in market-leading technological and subsequently mentioned the alignment between
the software provision and their ability to execute data-driven decision-making. These actions,
proposed by interviewees, assist organisations to introduce a data-driven culture.
These findings, compounded by the lack of literature of data cultures and more explicitly,
procurement functional data cultures, serves to inform a significant gap in scholarly research and
as such, it could be recommended as a more finite topic for future research studies.

38
4.6 Limitations:
There are two principal limitations that can be identified within this research study. Firstly, this case
study is premised on a qualitative research strategy rather than a quantitative or a mixed method
approach. Fundamentally, this is due to the researcher looking to understand the feelings,
opinions and beliefs that constitute a data culture. The limitation to adopting only a qualitative
approach over the other two methods is that researcher bias can occur. Specifically, in
qualitative case studies this could be a frequent risk and in the case of OA, the researcher was
employed by the subject company. Nevertheless, reasonable steps were taken throughout the
study to safeguard against any potential researcher bias.
The second limitation of this research is the small sample size. The strength of this approach is that
the researcher obtains deep, meaningful insights into the research they are undertaking because
the sample group is small and typically, generalisation is negated. However, the downside to this
approach is that to conduct a beneficial research study, a large sample population size is
preferred to ensure in-depth, scalability to the challenge, as well as, to enable richer data for a
comprehensive research analysis (Saunders et al., 2016).
Whilst not considered a primary limitation to the study, it must be noted that the premise of this
study was bound on one construction organisation with specific attention given to a procurement
function undergoing a transformation change programme. Furthermore, it is evident from the
literature review that there are significant gaps in the research in terms of data cultures generally,
data cultures within the construction sector and more importantly, data cultures at a functional
level. According to Yin (2018), this is consistent with a typical case-study design.
Although the research findings were expected to be generalisable to some extent, the nature of
concentrating on a solitary organisation suggested that the findings would only be useful to a
single business, in this case-study, OA. However, to increase the trustworthiness of this research
project a significant effort was made to enhance; credibility, transferability, dependability, and
confirmability, all aligned to the trustworthiness model proposed by Lincoln and Guba (1985).
For further studies of this nature, the researcher could adopt an action research approach. This
would involve the researcher scaling the project to incorporate further internal leadership teams
within the UK construction industry. For example, the commercial and technical teams or a
procurement team from a different organisation would enable a richer data collection phase to
offer a well-rounded view of the subject questions. As such, the validity of this research could be
questioned because it has one functions viewpoint, not the whole of OA and/or a procurement
leadership team outside of OA. Additionally, reflections can be found in appendix 5.

Data at the Core Establishing a Data-Driven Culture

Data at the Core Establishing a Data-Driven Culture

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