1. 1
Insight in the gap: combining expert judgement with data analysis
to perpetuate knowledge in infrastructure asset management.
I Gordon MRICS*
J Jarritt MEng, A McCullough MEng, C Beedie MEng
†
*Amey Strategic Consulting, UK, ian.gordon@amey.co.uk, +447809605041,
†
Amey Strategic Consulting, UK, jonathan.jarritt@amey.co.uk, adam.mccullough@amey.co.uk, chris.beedie@amey.co.uk
Keywords: Knowledge management, data analysis,
infrastructure asset management.
Abstract
The theory of knowledge management distinguishes between
tacit and explicit knowledge and posits that these two forms
of knowledge are inseparable, complementary, and explain
the creation of new ideas within organisations. The asset
management profession places great importance upon
information and knowledge to help realise business
objectives. This paper describes how the concepts of
knowledge management theory apply to the field of
infrastructure asset management. The paper uses case studies
to illustrate how this creates the opportunity for new and
creative solutions to asset management challenges, and
greater continuity of knowledge within infrastructure
organisations.
1 Introduction
Asset management thrives on knowledge. The application of
the best knowledge available to the management of assets is a
cornerstone of the profession and standards such as PAS55
and ISO55000. It is through the effective use of knowledge
that the asset manager becomes confident that they are
maintaining their assets in the safest, most effective manner
possible.
The purpose of this paper is to use theory and case studies to
demonstrate how knowledge management theory has a direct
application to the analysis of asset management challenges
and management of information within infrastructure
organisations. The paper argues that the application of
knowledge management theory can improve organisations’
ability to:
1. Understand the sources of the organisation’s
knowledge assets;
2. Identify where investment is required to ensure
knowledge continuity;
3. Facilitate the process of knowledge creation and
innovation.
The paper will describe the concepts of tacit knowledge,
explicit knowledge, the knowledge continuum, knowledge
conversion, and the SECI model popularised from the 1990s
onwards by Ikujiro Nonaka and others. The paper will also
discuss how recent developments in the analysis of ‘big data’
relate to the concepts of organisational knowledge
management and asset management problems.
2 Literature Review
The field of organisational knowledge management
developed throughout the 1990s and 2000s. The field draws
heavily from earlier work by the Hungarian chemist and
social scientist Michael Polanyi who popularised the notion of
‘tacit knowing’ in the 1960s. Polanyi argued that the human
mind relates to reality- fallibly- through a personal awareness
of the world and that “we know more than we can tell” [2].
The concept of ‘tacit knowledge’ was expanded further by
Nonaka and von Krogh, who described it as “knowledge that
is unarticulated and tied to the senses, movement skills,
physical experience, intuition, or implicit rules of thumb” [5].
Tacit knowledge is product of an individual’s interactions
with reality, and so is “rooted in action, procedures,
commitment, ideals, values, and emotions” [5]. Conversely,
‘explicit knowledge’ is information that is “objective and
rational”; it exists outside individuals, and may be duplicated
and stored as a commodity [5].
Exploitation of these different types of knowledge assets by
organisations is often in some way a function of their business
model. In a 2001 paper Hansen et al use case studies taken
from a major global accounting firm, and one of the ‘big four’
consulting firms, to describe how each prioritises the type of
knowledge that will best meet their business objectives. The
accounting firm spends heavily on an IT system that helps
them to store an extensive library of explicit knowledge for its
employees. The objective of this approach is to make
knowledge independent of individuals, and to achieve “scale
in knowledge” by codifying sufficient explicit knowledge to
ensure that staff across the firm can deliver the firm’s services
consistently and independently [1]. Hansen et al suggest that
this approach to knowledge management is suited to
standardised, mature, and commoditised products and
services, where the required skills can be codified using
explicit knowledge, and where the objective of the firm is to
maximise scale and revenue.
By contrast, the consulting firm seeks to leverage the
exchange of tacit knowledge within communities of
individuals to permit its staff to “use their analytic and
creative skills on unique business problems” [1]. This
approach seeks to provide employees with the support that
they require when delivering customised solutions to

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ambiguous problems. In this context, the IT system adopted
by the organisation facilitates communication between
individuals rather than serving as a warehouse of data.
Hansen et al describe how this approach requires employees
to commit to creating dialogue with each other, restricting the
scale of the firm, but ensuring that the smaller number of
employees can deliver higher margins on their specialist
services.
Hansen et al’s discussion of these two case studies focusses
on how the technology used by organisations to manage
knowledge follows from the organisation’s desired use of
knowledge. However, Hansen et al acknowledge that even in
the case of the firms offering highly commoditised services
the organisation will retain some reliance on their employees’
tacit knowledge. Walsham identifies the importance of tacit
knowledge even among organisations with sophisticated and
extensive IT systems, “there is no objective explicit
knowledge independent of the individual’s tacit knowing,” in
other words all explicit knowledge relies on individual
interpretation and understanding in order to become
meaningful [7]. In no organisation, particular large, global
firms, is it reasonable to assume that individuals with the
same job title possess the shared norms and values that will
allow them to arrive at a consistent understanding of explicit
knowledge. Organisations will rely on ‘organisational
translators’- a role that may be familiar to asset managers-
who can “frame the interests of one community in terms of
another community’s perspective” [7]. Organisational
translators are required to help catalyse the spread of
knowledge between individuals and across systems. In part
translators will counteract the limitations of computer
systems, as McKinley states “we have to accept that we
cannot manage knowledge in the sense of hard-wiring a
system… we have to allow people to make their own links, to
give them the techniques to allow them to construct, interact
with knowledge” [4].
The importance of knowledge management, and effective use
of IT systems, to competitiveness in fields such as asset
management, will only grow. As the Economist Intelligence
Unit reports, “firms are looking for IT tools that allow
employees to prioritise information, and to extract valuable
insights from an ocean of data” [6]. The benefits of
knowledge management are highlighted by new hardware,
software, and analysis techniques that promise to help
organisation to harness ‘big data’ in order to generate insights
into the performance of their assets through statistical tests for
correlation within large data sets [3].
However, it seems likely that substantial benefits from
knowledge management may arise in more humble areas,
such as enabling firms to understand “who knows what, and
how people use different types of information as part of their
work” [4]. Amey Strategic Consulting believe that an
understanding of the use of knowledge combined with
investment in effective externalisation (documentation) of
knowledge promises to provide organisations with flexibility
in the face of changing labour resources. Although the work
of Hansen et al, Walsham, and McKinley caution against
attempting to translate all tacit information into explicit
information, facilitating effective conversion of tacit
knowledge into explicit knowledge, and vice versa, should
increase the spread of business critical knowledge away from
single critical individuals and systems.
The idea that organisations can harness both tacit and explicit
knowledge to improve the performance of their business
underlies Nonaka’s SECI model (and associated SECI cycle),
discussed later in this paper, and is evidenced by the case
studies reviewed in this paper.
3 The Knowledge Continuum
Figure 1 – The knowledge continuum (refer to appendix for larger image).
Tacit and explicit knowledge are distinct concepts, but in
practice, the categorisation of knowledge is not always so
clear. Nonaka envisions a continuum of knowledge. On one
side of the continuum is purely explicit information, facts and
figures that are easily codified, stored, and transferred, and do
not require human involvement for their expression. On the
other side of the continuum is the most tacit of knowledge,
the experiences of an individual that are intimately tied to
their person and difficult or impossible to express. Moving
along the continuum, knowledge becomes easier or harder to
express, more or less personal, to a greater or lesser extent
codifiable. Figure 1 provides an example of this knowledge
continuum and the types of knowledge that exist at different
points along the continuum.
Due to the wide spectrum of available information assets,
when working to meet asset management objectives it is often
useful to frame problems in the context of the knowledge
continuum. Viewing the organisation in the context of the
knowledge continuum helps the asset manager to understand
the type of knowledge that individuals are using to make
decisions in their day-to-day work. The asset manager can
evaluate what tacit and explicit knowledge is being used to
run the business, manage the infrastructure, and achieve
individual and overall objectives.
Amey Strategic Consulting often encounter situations where
different sources of tacit and explicit knowledge result in
competing views of how to best maintain infrastructure
assets. In such cases, a perception of conflicting evidence can
lead to management processes where decisions focus on one
source of knowledge to the exclusion of others, and the
organisation may miss the opportunity to benefit from a more
complete understanding of the situation. An example of these

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competing views arose in our analysis of cleanliness
passenger perception scores, a key performance metric, at
Heathrow Airport. Whilst current passenger perception of
cleanliness at the airport is consistently high (>4 out of 5),
even further improvement and consistency was desired. This
prompted an in-depth analysis of the scores and their
fluctuations over time to build the best possible understanding
of the factors that influenced the customer satisfaction scores
and the opportunities available to influence them. A range of
different tacit explanations existed for fluctuations in these
scores, and Amey Strategic Consulting applied analysis of
explicit information and tacit knowledge in order to develop a
more comprehensive and consistent understanding of the
mechanisms at play. The primary tacit models competing to
drive decision-making were as follows:
1) Changes in the scores awarded by passengers largely
reflect changes in the quality and frequency of
cleaning achieved at the facilities;
2) Changes in the scores largely reflect changes in the
general experience of the passengers, including the
journey prior to arrival, the experience within the
terminal, the prevailing weather, and other external
factors unrelated to cleaning;
3) Changes in the scores largely reflect normal
statistical variance in the data collection, sampling
and analytical processes, and therefore do not reflect
significant changes in general passenger experience.
Each of these explanations is logical and can be tested using
explicit knowledge where it is available. Tacit knowledge to
support each of these explanations could conceivably be
gained during day-to-day work within the airport terminals.
However, considering the importance placed upon customer
perception within the airport- both in terms of performance
measures for suppliers and financial implications for the
owner - analysis of the explicit data was warranted in order to
validate and build upon staff’s tacit knowledge.
The explicit data gathered by Heathrow Airport took the form
of a scoring system whereby passengers were asked to rate
numerous aspects of the facilities on a scale of 1 to 5. The
performance of terminal facilities was then measured by
observing the absolute value of, and movement in, the
average score over time. Prior to Amey Strategic Consulting’s
work, staff often responded to small monthly movements in
this average score by making adjustments to cleaning
strategy. This response was based on the tacit assumption that
even small movements in the passenger perception scores
represented genuine and significant changes in passenger
perception, due to a prevailing organisational favour for
explanation 1 (see above).
The first step in the analysis was to arrive at an agreed
interpretation of the feedback provided by passengers. Amey
Strategic Consulting conducted extensive analysis of the data,
the distribution of responses, and the manner in which they
were collected. The team concluded that the surveys
conducted by the airport were generating ordinal data
permitting analysis using a Mann Whitney U test (non-
parametric). This method takes into account the sample size
as well as the distribution of the data to assess whether two
samples of customer responses with different average scores
are likely to represent a statistically significant change in the
underlying passenger perception. This analysis enabled Amey
Strategic Consulting to test the validity of explanation 3) and
determine that the threshold for statistical significance of
changes in monthly average score was five times greater than
the size of movement that maintenance staff had generally
responded to in the past. Therefore, in many cases, the
movements in the passenger perception score reflected only
statistically insignificant movements in the sample data. As
part of this analysis, Amey Strategic Consulting calculated
the threshold beyond which a change was likely to reflect a
statistically significant change in passenger perception. This
represented a case where reflection on explicit knowledge
resulted in improvement to the tacit knowledge used by the
asset management team. As a result, the team could state with
confidence that small month on month variations in the score
were likely to reflect statistical noise and hence should not, on
their own, be used to drive costly interventions or
adjustments.
Having arrived at improved knowledge on the statistical
significance of movement in the score, Amey then sought to
identify the cause of those movements that were statistically
significant by testing the prevailing explanation. To assess the
assumption that passenger perception is driven by variations
in the quality and frequency of cleaning Amey compared
fluctuations in an independent, objective measure of cleaning
performance (another set of explicit knowledge) against
fluctuations in passenger perception scores. If the
performance of cleaning staff was affecting passenger
perception then the movement in the two measures should be
correlated. The result of this analysis showed no significant
correlation between the two measures, suggesting that
explanation 1) was overstated. Whilst this finding seems
counter intuitive in some respects, after reflection with local
staff, tacit knowledge suggests a plausible interpretation of
this result, which is that beyond a certain threshold passenger
feedback does not respond to small variations in cleanliness.
If normal statistical variation in scores has led to constant
pressure to improve cleaning performance, it is possible that
cleaning effort has been managed beyond the point where
improvements achieve a return in passenger feedback.
No data set of explicit knowledge existed with which to test
the explanation 2) for fluctuation in passenger perception,
namely that it is the passenger’s overall experience of their
journey that influences their rating of the facilities. Instead,
Amey had to refer to their own tacit knowledge to assess the
likelihood of this assumption. Key to this assessment was an
understanding of how sensitive the measure of perception was
likely to be to low scores influenced by temporal events such
as delays and inclement weather. The structure of the scoring
system (a 1 to 5 scale) and the typically high scores received
from passengers (a rolling average of approx. 4.25 at the time
of the analysis) as well as the statistical significance of small
movements (0.05 or more), all suggest that movements in the
score are highly sensitive to clusters of negative scores caused
by temporal events. Tacit knowledge based on experience
working at the airport suggests that these clusters are
commonly triggered by events unrelated to the general level
of cleanliness observed across the facilities, rather than

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statistically unlikely variations in the responses of otherwise
similar passengers. Based upon this tacit knowledge, and the
results of the analysis of explicit knowledge, Amey Strategic
Consulting concluded that the most efficient way to improve
passenger perception scores was to maintain an acceptable
level of cleaning, ignore small variations in scores, and to
focus on ensuring a positive customer experience throughout
the airport facilities.
This case study, amongst many others, demonstrates that to
develop insight into the behaviour and performance of
infrastructure systems it is necessary to consider resource of
both tacit knowledge and explicit knowledge, whilst
challenging tacit knowledge with analysis of explicit
knowledge where possible. Tacit knowledge is invaluable in
its ability to direct and shape complex analysis of the
available explicit knowledge assets, that would potentially
otherwise go untapped, and it is evidently possible to arrive at
robust conclusions based upon a combination of explicit and
tacit knowledge. Asset managers should not expect tacit and
explicit knowledge assets corroborate each other on every
occasion. However, this should equally not be grounds for
dismissing tacit knowledge assets, as they are an integral,
important, and useful part of the fabric and culture of any
organisation. The following section will describe how the
process of combining tacit and explicit knowledge in this
manner can lead to a virtuous cycle of knowledge conversion
within organisations.
4 The SECI Model
Figure 2 – The Knowledge Continuum and Knowledge Conversion (refer
to appendix for larger image).
The recognition that knowledge exists in many different
forms, some quantifiable, others not, allows an organisation
to appreciate the depth and breadth of its knowledge assets.
The recognition of knowledge as an asset and as a source of
competitive advantage provides an incentive to organisations
to foster the creation, conversion, growth, and spread of
knowledge within their organisation. An efficient
organisation will be one that can leverage its knowledge
assets for competitive advantage, or, to borrow
Schlumberger’s dictum for knowledge management, “apply
everywhere what we learn anywhere” [6].
A common tacit understanding of knowledge creation is that
new knowledge most often occurs where different types and
sources of knowledge interact. This is in part the logic
underlying public and private sector attempts to bring
together groups of organisations in tech clusters, incubators,
and accelerators. Returning to our definitions of tacit and
explicit knowledge we can see how there is scope to expand
an individual’s tacit knowledge through new experiences and
discussion of experiences with others, and to expand explicit
knowledge through the collection of broader and deeper data.
It is also apparent that individuals can often translate their
tacit knowledge into new explicit knowledge, for example by
documenting observations and common practice. Similarly,
individuals can convert explicit knowledge into tacit
knowledge by studying and reflecting on documents and data
sets, and interpreting that knowledge in terms of their own
experience. Each of these conversion processes creates new
knowledge assets, and increases the overall stock of
knowledge available to the individual and the organisation as
a whole. Nonaka formalised this process of knowledge
creation through conversion in the SECI model (Socialisation,
Externalisation, Combination, Internalisation). Each
conversion step of the SECI model forms part of the SECI
cycle, increasing the stock of knowledge available, and
repetition of the process creates a virtuous cycle of
knowledge conversion. Socialisation is the sharing of tacit
knowledge between individuals, often through shared
experience, for example learning through apprenticeships.
Externalisation is the articulation of tacit knowledge as
explicit knowledge. Combination is the analysis of explicit
knowledge, for example through statistical analysis.
Internalisation is the process of reflecting upon explicit
knowledge to expand one’s tacit knowledge.
Reviewing the SECI model we can see that there is cost
involved in each step of the cycle, for example in making the
facilities available for individuals to exchange knowledge
through socialisation, or the IT investment required to
facilitate effective combination of explicit knowledge.
However, the greatest cost to organisations is likely to occur
in managing externalisation and internalisation. The process
of converting tacit knowledge to explicit knowledge, or vice
versa, requires investment of individual’s time, and requires
the individual to “experiment with words, concepts, and
linguistic relationships that enable [them] to convey meaning
to [themselves] and others” [5]. Anyone who has tried to
write an academic paper will understand the time and cost
associated with externalisation, and anyone who has tried to
learn a new language will appreciate the difficulties of
internalisation.
Despite its cost to individuals and organisations, the SECI
model is of immense value to infrastructure organisations and
to the asset management profession. In order to ensure
continuity of knowledge it is necessary for organisations to
reliably document and share the tacit knowledge of
experienced staff and to convert this knowledge into explicit
knowledge that can be used to inform and communicate
business decisions. In these instances, the act of
externalisation can permit the entire organisation to leverage
an individual’s experience and judgement. Similarly, it is
incumbent upon organisations to increase the tacit knowledge
of inexperienced members of staff through socialisation with
peers and mentors, and by providing the opportunity to
internalise explicit data, such as codes of best practice.

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Explicit knowledge expressed in a consistent format, for
example historical designs, surveys, and reports, can create
continuity between generations of individuals maintaining the
same assets where face-to-face meetings are no longer
possible. An organisation that effectively uses the SECI
model will be able to retain knowledge, share insights, and
demonstrate informed decision-making across its operations.
Whole life cost models are an example of a combination of
tacit and explicit knowledge developed using the SECI
model. Amey Strategic Consulting’s work, along with several
other consultancies, in supporting Network Rail’s
development of ‘Tier 2’ Whole Life Cost Models provides a
working example of this process as applied to the field of
asset management. The objective of this project was to
provide to the business with a suite of decision support tools
that would allow for the consistent analysis of investment
decisions across the organisation. A decision support tool
functions as a repository of explicit knowledge. For
infrastructure assets the pertinent explicit knowledge includes
the rate at which assets degrade over time, the maintenance
and renewal actions required to ensure asset performance, the
cost- in terms of labour, capital, time, and money- of
maintenance activities, as well as a statistical analysis of the
uncertainty around each of these parameters. In order to
create a decision support tool informed by the necessary
explicit knowledge it was necessary for Amey Strategic
Consulting to combine historically collected explicit
knowledge from existing data sources from across the
business. In addition, it was necessary to create new explicit
data by working with subject matter experts from within and
beyond the organisation in order to externalise the experience
and practices that they use in their day-to-day management of
the assets. This externalisation process required both the
careful documentation of working practices and experience,
as well as exercises to review the conclusions of the analysis
of data sets in context of the expert’s tacit knowledge.
The finished decision support tools represented both a
repository of knowledge, and an opportunity to ‘scale up’
analysis of maintenance decisions to a level of complexity
that would not be achievable for individuals reliant solely of
tacit knowledge. Having collected the explicit knowledge
required to conduct whole life cost modelling of the asset
base it became possible to apply the explicit rules to a far
larger population of assets and consider a greater variety of
maintenance policies that was possible without the decision
support tools. This functionality allowed the decision support
tools to support scenario planning and uncertainty analysis to
support business planning within the organisation.
The decision support tools function as a repository of explicit
knowledge that can facilitate continuity across the business.
As the resources of tacit and explicit knowledge on the
infrastructure assets improves, Network Rail asset managers
can in turn improve the explicit knowledge incorporated
within the decision support tools. Similarly, as individuals
change role, and as new engineers and asset managers enter
the business, it is possible to use the decision support tools to
facilitate their internalisation of knowledge from across the
business. In this way, decision support tools act as a catalyst
for the SECI cycle, converting and creating new knowledge
assets that help to increase awareness of decision-making
processes and build strategic asset management capability.
As the volume of data available to organisations grows, it is
necessary for asset managers to take ownership of that
information and to understand where it can be incorporated
into the SECI cycle to improve both the tacit and explicit
knowledge within the organisation. The SECI model
emphasises the importance of combining tacit and explicit
knowledge, as Nonaka states “Tacit and explicit knowledge
are not two separate types but inherently inseparable.”
However, as we enter the age of ‘big data’, others are looking
for opportunities to finally separate tacit and explicit
knowledge and rely on analysis of explicit knowledge on a
massive scale.
5 Knowledge and ‘big data’
Figure 3 – The Knowledge Continuum… extended (refer to appendix for
larger image).
The mechanisms behind the occurrence of potholes on
highway pavements are well understood within the industry;
it is tacit knowledge shared within the community of
highways asset managers. Despite this shared tacit knowledge
it is extremely difficult to predict where and when a pothole
will occur along a stretch of road due to the highly uncertain
nature of the factors at play (e.g. where tiny cracks form in
the surface allowing water penetration). This is an example
within asset management where tacit knowledge is sufficient
to describe a problem, but not to solve it. Amey’s Area 9
maintenance team required a quantitative approach to assess
the correct maintenance frequencies for different inspection
routes across the Area 9 road network. The Amey Strategic
Consulting team recognised that in order to arrive at a risk-
based inspection interval for specific inspection routes it was
necessary to separate the random noise in the measured
phenomenon (e.g. the precise occurrence of problems in the
road surfacing) from the key drivers of risk for which data
existed (e.g. traffic flow, historical defect rates, seasonal
variation). Using tools developed in the critical plant and oil
& gas industry to predict the risk of failure of pipelines- along
with a combination of statistical analysis of failure data, and
in-depth consultation with subject-matter experts- Amey
derived a robust methodology to arrive at inspection
frequencies for Area 9 highways. This approach lends itself to

6. 6
update and amendment as data resources improve, costs
change, and the subject matter expert’s understanding of the
drivers of defects evolve. The main advantage of Amey
Strategic Consulting’s approach was the use of statistical
techniques to estimate the distribution of fault incidence
across different inspection routes on the network. This
allowed the team to make meaningful and defensible
predictions from the data without relying on tacit explanations
as to why performance varied across road sections.
The ability to draw conclusions from large resources of data
without relying on explanations grounded in tacit knowledge
is one of the key promises of ‘big data’ techniques. One of the
key concepts behind the ‘big data’ movement is the reliance
on correlation rather than causality. Taken to its extreme, this
implies that if patterns reveal themselves in data then we
should be willing to act on those patterns regardless of
whether there is a coherent causal explanation (e.g. an
explanation that we can reconcile with our individual tacit
knowledge). As Figure 3 demonstrates, ‘big data’ promises to
allow us to generate new explicit knowledge that is not linked
to tacit knowledge at all therefore bypassing the SECI cycle
and the costly work of externalisation and internalisation.
Amey Strategic Consulting’s work with Area 9 on attempting
to quantify the risk associated with different sections of road
drew upon data relating to historical defect frequency, cost of
maintenance, seasonal variations in defects, and passenger
flow. A ‘big data’ dataset could potentially include a far
higher number of factors such as the road surfacing material,
incidence of precipitation and frost, braking zones, freight
tonnage, elevation, slope, and many other factors, some of
which may not at the outset appear, according to our existing
tacit knowledge, to be causally related to the performance of
the road. Analysis of this much-expanded data set could
return correlations between different factors that would not be
possible to demonstrate using tacit knowledge or smaller
datasets. The two main drawbacks of this approach would be
a) the cost faced by the asset manager when collecting such a
disparate set of data on an on-going basis, some of which
might not be demonstrably useful, and b) the need for the
asset manager to trust the meaning of the correlations without
any causal explanation. The first dilemma, to collect or not to
collect, reveals the biggest challenge in applying ‘big data’
methods to asset management: the marginal cost of
information. The second dilemma, to trust or not to trust, is
especially pertinent in the asset management profession
where individuals are often personally accountable for
demonstrating that they have taken every reasonable and
informed action to ensure the safety of the public and of their
staff.
Big data solutions promise to work best where the expansion
of datasets occurs with little or no marginal cost to the user.
This is often the case where the datasets are generated as part
of another service, for example, the vast amount of data
generated as we interact with technology as part of our day-
to-day lives (e.g. web searches, social media, ecommerce,
online news, business documents). To the analyst, data sets of
this type are effectively self-generating and largely free. In
practice, our daily interactions with infrastructure do not
automatically generate such a wealth of data. Part of the
reason is that our infrastructure is often- by necessity-
protected from interaction with all but a small number of
specialised individuals. This means that in order to collect
extensive information on his assets the asset manager will
generally need to purchase large quantities of specialist
equipment or pay for extensive on-going data-collection
exercises. As a result, in most cases, the asset manager will
only be able to justify the expenditure of collecting
information where tacit knowledge suggests that the
information will be of value to his operations (e.g. is
somehow causally related to performance). The need to
justify, in advance, the type of information collected
substantially reduces the opportunity to find unexpected
correlations within the datasets. As a result, the datasets
available to asset managers are relatively small, which
increases the requirement for intensive SECI-based analysis
of (and reflection on) those limited datasets to extract
maximum value.
Even where it is possible to identify correlations within the
data, the duty of care owed by the asset management
profession to the public and to staff will limit the opportunity
to use those correlations. On a safety and assurance basis, the
asset manager cannot act solely as a result of the
identifications of correlations within datasets. Those
correlations must be bounded, evaluated, and reviewed
against engineering and operational tacit knowledge before
the asset manager can take action. Following the work Amey
Strategic Consulting delivered for Area 9, the obligations of
the Area 9 contract team to demonstrate their compliance
with Section 58 of the Highways Act (reasonable knowledge
of the condition of the carriageway) meant that the team’s
conclusions resulting from the analysis required full
engineering, operational, and independent review and
validation. It is the responsibility of those conducting data
analyst to raise with their clients the need for this level of
comprehensive review.
The asset management profession should not ignore the ‘big
data’ movement, particularly as it creates the opportunity to
generate insight without reliance on tacit explanations,
potentially removing the costly and time consuming steps of
the SECI cycle that require intensive human participation.
Asset managers should seek to use the SECI cycle to ensure
that they use their limited resources to collect the data that
can best test and refine their existing tacit knowledge, and
which can help to ensure the performance and safety of their
assets. The fact that many asset management data sets are
noisy, incomplete, and of variable data quality is not an
argument against their use. Similarly, the fact that reaching
consensus on all questions of tacit knowledge is not always
possible is not an argument against consulting subject matter
experts. Indeed, the limitations on the data available to asset
managers, and the duty of care owed to the public and to staff,
are both arguments for seeking to identify and utilise
resources of knowledge wherever they exist. Amey Strategic
Consulting’s work for the Area 9 team demonstrates the type
of opportunities that exist to better leverage value and
assurance from existing datasets.

7. 7
6 Conclusions
The application of knowledge management theory to asset
management problems can result in a number of practical
benefits to organisations. As the case study at Heathrow
Airport demonstrates, when dealing with complex problems,
a crucial initial first step is to establish the types of
knowledge that exist within the organisation, and how teams
within the organisation use that knowledge to inform business
decisions and organisational responses. Effective analysis and
evaluation techniques will seek to leverage maximum benefit
from all sources of tacit and explicit knowledge within the
organisation, as well as to challenge any assumptions based
on tacit knowledge.
Tacit knowledge is temporal and tied to the organisation’s
employees. The time and cost associated with sharing and
externalising tacit knowledge means that asset managers
require a strong understanding of the sources of knowledge
that are crucial to their business and how they are going to
efficiently manage those resources. The case study of
Network Rail’s whole life cost modelling initiative
demonstrates that the targeted use of technology combined
with the externalisation of tacit knowledge can serve as a
mechanism for codifying and sharing knowledge within an
organisation. Work of this nature, aligned with the SECI
model, can create new and innovative knowledge assets
within the organisation, and contribute substantially to
decision-making and strategic capability.
Finally, as the volume of data available to asset managers
increases at an accelerating rate, so will the competitive
necessity of leveraging that knowledge to realise business
objectives and competitive advantage. The SECI model sets
out a framework for creating an integrated culture of
knowledge sharing and creation within an organisation. At the
same time ‘big data’ envisions a future where asset managers
are increasingly expected to look to large-scale analysis of
data to create knowledge without supporting tacit knowledge
or causal explanation. Amey Strategic Consulting’s work
prioritising the risk profiles of road sections and inspection
routes for Area 9 built a statistical analysis of explicit data
upon a tacit understanding of the phenomena in question. This
work points towards a compromise position where the tacit
knowledge of engineering and operational staff is used to
frame statistical analysis of large-scale data sets to draw
substantive conclusions. These conclusions can in turn be
reviewed in the context of staff experience and judgement.
In summary, knowledge management theory provides a
mechanism for asset managers to understand the knowledge
resources that they require to maintain infrastructure, while
describing a process whereby they can expand and share their
organisation’s knowledge base to arrive at innovative
solutions to tomorrow’s challenges.
Acknowledgements
The authors of this paper wish to acknowledge the kind
permission provided by Network Rail, Heathrow Airport
Limited, and Amey Group Plc., to publish the findings
discussed within this paper.
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Appendix: Figures in full
Figure 1 - The Knowledge Continuum
Figure 2 - The Knowledge Continuum and Knowledge Conversion
Figure 3 - the Knowledge Continuum... extended