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{{Paper
|id=Vol-3606/52
|storemode=property
|title=Unveiling the Roots of Big Data Project Failure: a Critical Analysis of the Distinguishing Features and Uncertainties in Evaluating Big Data Potential Value
|pdfUrl=https://ceur-ws.org/Vol-3606/paper52.pdf
|volume=Vol-3606
|authors=Massimiliano Gervasi,Nicolò G. Totaro,Giorgia Specchia,Maria Elena Latino,Loredana Caruccio,Stefano Cirillo,Tullio Pizzuti,Giuseppe Polese,Angelo Marchese,Orazio Tomarchio,Lorenzo Di Rocco,Umberto Ferraro Petrillo,Giorgio Grani,Alessandro La Ferlita,Yan Qi,Emanuel Di Nardo,Simon Mewes,Ould el Moctar,Angelo Ciaramella,Claudia Diamantini,Alex Mircoli,Domenico Potena,Simone Vagnoni,Claudia Cavallaro,Vincenzo Cutello,Mario Pavone,Patrik Cavina,Federico Manzella,Giovanni Pagliarini,Guido Sciavicco,Eduard I. Stan,Paola Barra,Zied Mnasri,Danilo Greco,Valerio Bellandi,Silvana Castano,Alfio Ferrara,Stefano Montanelli,Davide Riva,Stefano Siccardi,Alessia Antelmi,Massimo Torquati,Daniele Gregori,Francesco Polzella,Gianmarco Spinatelli,Marco Aldinucci
|dblpUrl=https://dblp.org/rec/conf/itadata/GervasiTSL23
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==Unveiling the Roots of Big Data Project Failure: a Critical Analysis of the Distinguishing Features and Uncertainties in Evaluating Big Data Potential Value==
https://ceur-ws.org/Vol-3606/paper52.pdf
Unveiling the Roots of Big Data Project Failure: a
Critical Analysis of the Distinguishing Features and
Uncertainties in Evaluating Big Data Potential Value
Massimiliano Gervasi1,2,3 , Nicolò G. Totaro1,2,∗ , Giorgia Specchia4 and
Maria Elena Latino1
1
Department of Innovation Engineering, University of Salento, Lecce, Italy
2
Centre for Applied Mathematics and Physics for Industry (CAMPI), University of Salento, Lecce, Italy
3
Artificial Intelligence & Data, Deloitte Consulting S.r.l. S.B.
4
Department of Human and Social Sciences, University of Salento, Lecce, Italy
Abstract
The potential value intrinsic in Big Data represents an opportunity for companies and organisations,
which invest their resources in search of a return on investment capable of guaranteeing efficiency in
production and procurement processes, cost reduction and support to decision-making processes through
targeted strategies. However, the implementation of Big Data-driven strategies often does not generate
the expected value, recording a failure rate of over 80 per cent. Such percentages lead to think of a
systemic error, probably inherent in the management models used. For these reasons, we analysed the
major Big Data frameworks discussed in the literature and their respective characteristics, specialising
them into three classes. By comparing these frameworks with those used in software engineering and
IT projects, on which they are based, it was possible to understand the differences between the two
generations of models and identify the critical aspects in Big Data initiatives. So, the analysis led to
the definition of a first model for the implementation and management of Big Data driven strategies,
highlighting what requirements a modelling framework should necessarily have to support companies
and organisations in the transformation of the Big Data Potential Value in Big Data Business Value.
Keywords
Big Data, Value Framework, Business Value, Potential Value
1. INTRODUCTION
Data own a value that companies and organisations are called to capture and exploit in economic
terms to increase the level of competitiveness [1], thanks to ad hoc strategies, resources, and
technologies [2], in fact, there is strong hype in the literature about investments in Big Data
initiatives. In [3, 4], it is predicted that if companies would use Big Data in their innovation
processes, they would save up to 20-30% on development and have time-to-market cycles
that are 50-60% faster; in the public sector, Big Data would reduce the costs of administrative
activities by 15-20% and thus generate a value of EUR 300 billion [2]; in the Energy and
ITADATA2023: The 2nd Italian Conference on Big Data and Data Science, September 11–13, 2023, Naples, Italy
∗
Corresponding author.
Envelope-Open massimiliano.gervasi@unisalento.it (M. Gervasi); nicologianmauro.totaro@unisalento.it (N. G. Totaro);
giorgia.specchia@unisalento.it (G. Specchia); mariaelena.latino@unisalento.it (M. E. Latino)
© 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Transport sector, the potential is estimated at a reduction of 380 mega tonnes of CO2 , due to
time and fuel savings of USD 500 billion [2]; other examples of Big Data potential value
can be found in [5, 6, 7, 8, 9]. In industry, frameworks have been developed to assess the
maturity level of Big Data management [10], in fact, despite these promising predictions, there
are many examples where Big Data initiatives have failed to translate their potential value
into captured/created value, and consequently in business value [6]. In [11], according
to reports from 300 companies, 55% of Big Data projects remain incomplete, while several
others fail to achieve their goals [12]. Gartner predicted that 60% of Big Data projects up to
2017 would not go beyond the pilot and testing phases and risk being abandoned, and another
survey of 199 technology executives revealed that around 48% of organisations that invested in
Big Data failed to transform data into useful information [9]. Ultimately, it is estimated that the
failure rate of Big Data initiatives ranges from 50% [13] up to 85% [14, 15].
The low success rates of Big Data initiatives, found even in organisations that are leaders in
their field, lead to profound reflections. It becomes legitimate to ask what are the causes and
factors behind this high failure rate, and whether the discrepancy between the value generated
and the expected value is due to systematic or contextual factors, thus dependent on individual
initiatives.
With the aim of answering to this question, in this paper we will analyse some of the most
important frameworks for capturing and creating value through Big Data, and compare
them with those commonly used for IT initiatives. The objective is to identify what are the
distinguishing factors that differentiate Big Data initiatives from others, so that current business
model can be made adaptable and dynamic, in order to orchestrate and successfully manage Big
Data driven projects and strategies.
2. BIG DATA AND IT PROJECTS FRAMEWORKS
In the literature, several frameworks designed to support business strategies can be identified
in order to capture Big Data value, configure and manage the necessary resources or identify
the business value generated by what are defined as Big Data Initiative [16].
Depending on the purposes and characteristics of Big Data frameworks identified in the
literature, the following classification is proposed:
• Big Data Value - Transformation Process: modelling the processes of data trans-
formation from the extraction or creation stages to its use to generate useful value for
organisations; this class includes the Big Data Value Chain. In some models, the archi-
tecture is enriched by the analysis of the initiative’s objectives and application contexts
up to the measurement of the value generated.
• Big Data Value - Creation Process: process modelling to identify, configure and manage
the resources and skills required for the development of the Big Data initiative, specialised
along the different implementation phases.
• Big Data Value - Dimensional Framework: dimensional modelling of the Business
Value, which can be generated by Big Data initiatives, in order to identify all competitive
and performance advantages achieved or potentially achievable.
In order to provide an overall view, in Table 1 for each identified class, the theoretical
background, the models applied to Information Technology and/or data analysis projects
identified as predecessors of Big Data frameworks, and the Big Data models identified in
the literature are made explicit.
THEORETICAL IT PROJECT BD PROJECT
CLUSTER
BACKGROUND FRAMEWORKS FRAMEWORKS
Value chain [17],
RFIKW hierarchy [25], BD
DIKW hierarchy Data Value Chain as
Information Value Chain
BDV - [18], Virtual Value a Service [21],
[20], Big Data Value Chain
Transformation Chain [19] Linked DVC [22],
[23, 24, 26, 27, 28], BDVC
Process Information Value Data Value Chain
implementation models
Chain [20], Process [23, 24]
[16, 29, 30, 31, 32]
Theory [4] .
Value-based Big Data Analytics
Management Framework [9],
Resource-based view
Framework [37], Configurational Big Data
[33], IIRF model [34],
Value-creation Analytics Capability Model
VRIO model [9],
BDV - Creation model for [40], AC/TC Model [4],
Dynamic Capability
Process value-based Conceptual Model: Digital
View [35],
management [38], innovation integration to
Contingency Theory
Information promote organizations
[36]
Technology value benefits [41], Inductive
framework [39] framework [25].
Value Creation and
Types of value creation from
Capture [43],
Big Data [6], Conceptual
Value-creation
IIRF model [34], Big framework: How value is
BDV - Dimensional model for
Data Analytics created from BDA [9], Big
Framework value-based
Business Value [42] Data multi-dimension value
management [38], IT
framework [45], Value
Business Value
creation dimensions [41].
[42, 44]
Table 1
Classification of frameworks identified in the literature
The three classes are not a partition of the frameworks identified in the literature, but labels
that highlight which aspects are modelled and specialised. For this reason, a framework can
also belong to two distinct classes, as, for example, the model of Wu et al. [25] which specialises
resources and, in particular, the competences required to move from one node to another in the
value chain, or the Grover model [9] which includes the value dimensions within a process of
creating value from Big Data starting from the resources and skills needed.
Regardless of the class of models considered, the crucial role of the Resource Based View
(RBV) is clear. A correct configuration of resources, whether these are Tangible, Human Skills
or Intangible, is a necessary condition for the creation of a competitive advantage [33, 40].
However, the adaptability required of organisations, namely the ability to evolve and scale their
strategies according to changing contexts such as those in which Big Data-driven strategies
are developed, has led to an evolution of RBV that is realised in the Dynamic Capability View
(DCV) [33, 35]. Thus, the configuration of resources will dynamically change according to
the initiative and the ”intermediate” results obtained within the value chain, as well as the
organisation itself, as can be seen in [40], in which resources are specialised according to the
size of the company (SME or Large).
3. SPECIFICITIES OF BIG DATA INITIATIVES
The analysis of the frameworks in Section 2 allows to highlight the characteristic aspects in
the Big Data initiatives that organisations are called upon to manage, with respect to classical
software engineering or IT initiatives in general. It might be plausible that in some of these
differences, reported below, may lie the causes of the high failure rate of Big Data initiatives.
a) Randomness of the Big Data project life cycle: in traditional software engineering,
architecture design and requirements negotiation, although related, are performed at
separate times. This separation of concerns is not conducive to value creation [31] and is
unsuitable for Big Data contexts, in which, on the contrary, significant randomness is
present, which often forces a negotiation of requirements in the process. In fact, as high-
lighted in the model in [4], the value creation process in Big Data projects is probabilistic.
item[b)] Uncertainty of results: it is not possible to know the level of quality
of the information in the data before analysing it, it is not even certain that the
information someone intends to extract from the data is actually present in the data,
or that one has sufficient technology and knowledge to ensure the success of the
initiative [12]. In addition, the results of analyses may be unpredictable because
they depend on machine learning and deep learning techniques. This forecasting
impossibility, which does not allow the deterministic identification of data extrac-
tion and processing strategies according to the available resources, is not present
in software projects, which are subject to less ambiguity in implementation strategies [12].
c) Data, an atypical resource because shareable: data are a resource [46], but is an
exception compared to the others because it is easily shared. While an exclusive asset,
such as an apple, can only be consumed once, data can be used by several actors, even
simultaneously [47], as well as the knowledge derived from the analyses. In this direction,
the ease whereby data could be replicated or shared among several actors in the same
network becomes a competitive advantage, since analyses restricted within too specific
perimeters could become limiting in that they lack information or are constrained by
misleading information bias.
d) Co-creation of value in Big Data contexts: as can be seen in [48], in IT models,
value is created by organisations and is distributed to the market, in which customers
are merely ”recipients of value” [49, 50]. In Big Data contexts, on the other hand, it
is often directly the customers who provide the data to the organisations, who are
then called upon to capture the information hidden in it. Data become a dynamic and
changing resource over time, constantly being updated to generate new information
value [51]. Thus, the customer or user of services goes from being a receiver of value to a
co-producer, who participate actively in the creation of perceived value [48, 52]. This
aspect will be taken into account in the model presented in Figure 1.
e) New value generated through Big Data: when talking about the value generated in
Big Data, the analysis of massive sources of historicised or analysed data in real time,
the speed with which these are collected and analysed, and their variety become the
key to generating new insights for making better and faster decisions, that make the
difference [48, 53, 54]. Decision support, thus, becomes a feature of the value associated
with Big Data officially recognised in the literature as ROI in the implementation of the
respective initiatives [36, 55]. As highlighted in [56], managers use Big Data to change
their products, optimise production processes and refine their strategies.
f) Critical obsolescence in Big Data initiatives: time becomes an even more critical
resource compared to traditional software engineering and IT initiatives, since organ-
isations cannot prevaricate in investing in Big Data strategies if they want to remain
competitive over the years [33]. At the same time, the technologies and skills required
to implement Big Data initiatives are constantly evolving, and even those at the cutting
edge may become obsolete over limited time periods. Finally, the information power of
data is not persistent over time, but could lose its value; for these reasons, data-driven
strategies must be as efficient as they are targeted and timely, in order to be able to define
or consolidate in the short term a competitive advantage resulting from the initiative,
which could otherwise be nullified by excessively long adoption and implementation
periods.
TRADITIONAL IT PROJECTS BIG DATA PROJECTS
Deterministic life cycle Random life cycle
Certainty of the final result Uncertainty of the final result
Single-use static resource Mutable, sharable and multi-useable resource
User perceives the value generated User co-creator of value
Operational value Strategic value
Normal rate of obsolescence Higher rate of obsolescence
Table 2
The main differences between traditional IT projects and Big Data projects
Among the differences between traditional IT and Big Data projects, we consider plausible
that the randomness of the Big Data project life cycle and the uncertainty of results may be two
determining factors of the high failure rate of Big Data initiatives. These two factors particularly
involve value, described as one of the V’s of Big Data [26]. Business models, to handle this form
of indeterminacy, should be flexible, scalable and capable of accommodating changes in the
status quo and then update accordingly, quantifying when the potential value changes over
time, depending on the resources used and those expected to be used after an update.
4. A GENERAL FRAMEWORK FOR CONVERTING THE
POTENTIAL VALUE OF BIG DATA INTO BUSINESS VALUE
Starting from the distinctive features identified in Section 3, a first generalised model is proposed
below, aimed at managing uncertainties (points a and b) intrinsic to Big Data initiatives, intro-
ducing the concept of Big Data Potential Value into the Big Data Value Chain. The modelling,
although at a high level, is intended to respond in the first instance to the new requirements
accompanying Big Data projects, while at the same time preserving and integrating those
business models currently used and validated over time by companies and organisations. In
Figure 1 it is possible to see a representation of the model whose main points, that contributed
to its creation, will be explained.
Figure 1: Model for converting the potential value of Big Data in business value
In the model, we chose to use the Information Value Chain, which is considered to be as
representative as the Big Data Value Chain, but less constraining in terms of granularity and
ordering of the different transformation processes that the data should undergo in order to
generate the expected value. Instead of specialising the same architecture by dividing it into
different sectors, as in the 5 use cases of the Big Data Value Chain in [27], there is a preference
for greater flexibility, deferring to the project needs and the skills and creativity of the data
scientists for the best possible implementation strategy [35].
The presented Information Value Chain is adapted from the RDIKW model [25], which
originates from the DIKW model [18], replacing the data format node with the data product
node, capable of grafting the Information Value Chain into the Data Mesh of [57], as already
presented in [58]. The data product, in such a view, becomes a collection of data that is
universally usable and agnostic with respect to a particular context or objective, in such a
way as to limit the potential value inherent in the data as little as possible, while at the same
time adhering to high quality standards guaranteed by the domain owner. As visible in [59],
the different nodes of the chain, depending on the objectives and contexts, require specific
technologies, which are constantly evolving, for the implementation of Big Data strategies. In
this sense, the presented model also allows value to be created through a network of actors
orchestrated by a common governance, in which the use of resources is optimised (e.g. through
the Technology Mesh [58]), in such a way that the value generated is greater than the sum of
the values that each actor in the network could have generated individually.
As reported in Section 2, the literature suggests that each class of frameworks identified
focuses on one or more aspects of the implementation of Big Data initiatives. It is considered
useful, however, to propose an initial generalised model in order to simultaneously include
the different points of view.
From the BDV - Transformation Process, the chain of capturing value from data was taken
up, meaning the transformation of data into information, knowledge and wisdom. Creation
Process models engage in the transition between the different nodes of the value chain. The
configuration of resources and capabilities, typical of the BDV - Creation Process, supports
the capture and creation of value to move from one node to the next in the BDVC [25, 40].
Therefore, capabilities are part of the configuration of resources, which in Big Data initiatives
must be dynamic and depend on multiple factors [40]. Therefore, in Figure 1, it was decided to
generically represent Capability A, B, C, D to emphasise that their dynamic specialisation is
necessary. From the BVD - Dimensional Frameworks we adopt a dimensional definition of
value. In the proposed model, the structure in [45] has been taken as a reference, which involves
the five dimensions shown in the figure (Informational, Transactional, Transformational,
Strategic, Infastructural). This dimensional view is present both at the beginning, in the Big
Data Dimensional Potential Value node, where for each dimension the value to be captured
(potential) is estimated, and at the end of the chain in the Big Data Dimensional Business Value
node, where for each dimension the value actually generated will be measured, deciding
whether the initiative succeeded or failed.
Value is one of the V’s of Big Data [26], however, the definitions provided often fail to fully
formalise its characteristics. In the literature, it is often mentioned that there is captured
value from Big Data and indirectly it is accepted that such value intrinsically exists in the data
in the form of potential value. As seen, due to the factors of randomness and uncertainty
(Section 3, points a and b), the expected potential value often does not turn out to be the one
actually generated, a phenomenon that decrees the failure of the initiative. Furthermore, it may
be limiting to think that the potential value lies solely in the data. In fact, value capture may
also depend on the strategies and tools used to extract it. For these reasons, in line with the
model analysis in Section 2, we propose to model potential value along three dimensions: data,
agents and technologies. The ”data” dimension represents the raw material, in its rough state,
of the Big Data initiative, whose characteristics are well described by the V’s (from which,
however, ”Value” is excluded). The ”agents” dimension considers both human agents, such as
data scientists, and ”artificial agents”, such as ML and DL algorithms, which contribute to the
uncertainty of the outcome of the Big Data initiative, as seen in Section 3 point (b). Finally,
the ”technology” dimension perimeters which technologies will be used and how they will
be used in the Big Data initiative, depending on the technological maturity of each of them
[60, 61]. It is believed that such a specialisation could refine the measure of potential value
that organisations are asked to estimate at the beginning of the initiative, thus becoming a key
strategic information for estimating its feasibility.
Modelling the potential value does not reduce the randomness of the initiative, but supports
awareness in its management. However, a first revelation of the value that can actually be
captured from the potential value will only occur in the phases between information and
knowledge, as in the ”patterning” phase of [28], or between knowledge and wisdom. In these
phases, the transformations that the data have undergone in the previous phases may have
inevitably altered the initial potential value inherent in them, to such an extent that this value
can no longer be captured except by updating the entire value chain, as evidenced in the figure
1 by the arrow running from the ”Big Data Dimensional Business Value” node to the ”Potential
Value” node. Indeed, an update of the entire chain can be considered either an improvement of
the implemented process or a necessity due to the failure of the initiative itself, which either
failed to estimate the potential value correctly or was unable to capture it. In both cases, it is
necessary to take timely action on the implementation strategy previously adopted, considering
the reasons that led to the failure of the previous one and the resources that will be needed for
a new implementation.
5. CONCLUSIONS
The analysis of Big Data frameworks and their characteristics is part of an ongoing Research
work aimed at systematically reviewing the literature in order to identify what should be
the requirements and characteristics for a generalised Big Data project implementation and
management architecture. What has emerged so far has made it possible to identify in the
randomness and uncertainty of the results two possible weaknesses of Big Data initiatives.
The creation of a general model thus makes it possible to merge all those features of Big
Data initiatives that are currently specialised in different models, as can be seen in Section 2.
However, it is necessary to increase awareness of the value to be captured and the value created
at each node of the Big Data Value Chain, in order to have visibility into the entire process.
Potential value, as defined, together with a dimensional structure of business value enable the
use of ad hoc techniques to adopt quantitative approaches borrowed from information theory
in a dynamic context [62] and qualitative approaches aimed at studying the compatibility of
relational structures, in particular preference criteria, between different representations of the
system under investigation [63]. The ultimate goal of the Research conducted is to formalise
the state of awareness of those who are called upon to manage the initiative, in order to provide
for cyclical updates of the state of knowledge in response to randomness and uncertainty in
Big Data contexts. The study of mappings (i.e., correspondences) between sets endowed with
different relational structures to represent knowledge states, and specifically the extent to which
such mappings preserve the relational structures, can benefit from the aforementioned methods
[63], which can provide a unified, but also scalable formalism for a structural description of
the different components of the present proposal. This update could lead to a redefinition
of the dimensions used both to define the potential value that could be captured (Big Data
Dimensional Potential Value in Figure 1), and the value that is actually generated (Big Data
Dimensional Business Value in Figure 1). In the model we are working on, this kind of structure
is much more flexible than the one proposed, in fact, dimensional hierarchies can be adapted, as
seen in the [45], and different weights can be used for the different dimensions, in response to
the importance to the organisation of that particular generated value.
In this way the proposed enhancements will make it possible to take into account not only
the classical risk and impact factors, but also opportunities or limitations in the updating of
value recognition, exploiting the reusability of resources wherever possible. Finally, with
the introduction of the concept of data as product, integrating the proposed model with the
Data Mesh and the Technology Mesh, we have actually laid the foundations for a multi-actor
approach capable of increasing the potential value of the paradigm (data, human-agent, Big
Data technologies), fostering inter-company collaboration as suggested [4]. In conclusion,
the model presented aims at structuring the role of human intelligence, and the richness of
the relations it has with technology, and by extension with artificial intelligence, through the
recognition of its own limits. This role, indispensable in the model, can only find in the human
agent the only possible interpreter.
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