An Evaluation of Big Data Analytics Projects and the Project Predictive Analytics Approach
Introduction
Big Data collects and stores huge volumes of data whose management and
analysis are increasingly becoming more challenging. A majority of firms or
companies are now investing in Big Data Analytics motivated by the potential
benefits and competitive edge of this new technology. Big Data involves the processing and management of large
volumes of data obtained from a variety of heterogeneous
data used in the enterprise,
whether structured or unstructured data. In Big Data Analytics, analytical methods
and technologies are heavily
utilized in the management and analysis of huge volumes of complex data sets
for use by various applications that augment the performance of a
business. The
purpose of the paper is to conduct
an evaluation of Big
Data Analytics Projects which
discusses why the projects fail and explains why and how
the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine
learning, and artificial intelligence.
Nikumbh and Pimplikar (2014) define a project as a unique and temporary task, with a specific start and finish dates, entail a commitment of resources, and seeks to attain specified objectives. The success of projects requires sound management, which is done by project managers. Therefore, there is a need for the assignment of the appropriate managers to the projects. According to Patanakul et al., (2003), the assignment of a project manager is a critical project decision that determines project success and organizational performance. Project management is the application of knowledge, skills, tools, and technology to project activities in order to meet or exceed stakeholder expectations and requirements (Harrington & McNellis, 2006). Project management is an organized control of a project that starts with project planning and ends with the project closure.
Many organizations
in the contemporary
world collect, store, and analyze huge volumes of data. Big
Data is characterized by the volume of data, the velocity with
which it arrives, and the variety of forms it takes. Big Data precipitates a new generation of decision support data
management where the potential value of the data is now being realized by
businesses and appropriate technologies, people, and processes are now extensively utilized to explore the unlimited opportunities available. In light of the significance of Big Data, this paper is an
analysis of Big Data analytics projects, which embodies an elaboration
of the reason for the failure of the interventions. Focus is also placed on
discussing the reasons for the failure of Big Data
analytics projects. To enhance the success of Big Data
analytics projects, there could be the application of Project Predictive
Analytics (PPA), which shall be interrogated
further in this paper. There is also a discussion on other
methods for enhancing the success of Big Data
analytics projects, such as data mining, machine learning, and artificial
intelligence.
Big Data may be
conceived as more and different types of data than is usually handled by
traditional relational database management systems (Su, 2013). In a similar
vein (Ajah and Nweke, 2019) posit that Big Data
describes the high volume, high-velocity data with high variety, uses new
technologies and techniques to acquire and analyze it; enhances decision-making capabilities, and provides more insight and discovery, and support process optimisation. The Big Data is
collected from a large assortment of sources, such as social networks, videos,
digital images, and sensors. Tapping into data analytics
creates a strategic
advantage, leverages competitiveness and
identifies new business opportunities. Some of the wide applications of data analytics include
credit risk assessment, marketing, and fraud detection (Watson, 2014). Hemlata
and Gulia (2016) argue that there are many types of analytics approaches that can be categorised into
descriptive, predictive, diagnostic and prescriptive analytics. Big Data analytics is purposed to
discover new patterns of knowledge and provides new insights (Weibl
and Hess, 2018).
Singh et al.
(2015) stated that Big Data analytics
uses systematic architecture of Big Data, Big Data
mining, and software for analysis. The common examples
of analytics methods include Bloom
Filters, Hashing, Index, Triel, and Parallel Computing. There is also use of
tools that may include R software, Excel spreadsheets, and Rapid Miner (Ajah
and Nweke, 2019). Nevertheless, most of the Big Data
analytics projects fail. Axryd (2019) argues that there are varying statistics
on project failure rate, whilst Gartner (2013) estimated that 60% of Big Data projects fail. Nick Heudecker (2017), a Gartner
analyst, believes that the failure was close to 85%.
The next section discusses the reasons for
failure of Big Data projects.
Project
Management Consultancy Value Adding Contributions
There
is a need for planning and effective management so that projects can be
successful. This brings the notion of project management consultancy (PMC) or
project manager into the limelight. A project manager is empowered to plan, direct, organise and control the project from
the start to finish (Project Management Institute, 2013). Such an individual
should provide effective leadership given the project environment, which is
typically dynamic and highly unpredictable. There is a need to
enhance the possibility of project success taking into consideration the
characteristics of the environment and coherence of management
strategies. In addition to that, the project manager
must use diplomacy, worker participation, and conflict resolution skills to be
an effective leader. The ability to achieve teamwork becomes crucial to the
achievement of project objectives. The argument is, over and above
understanding that projects consist of a
series of interrelated activities, which are problem solving, time phased as
well as being cost-bound, there are other intervening factors related to how the
project is managed and the general environment uniqueness of that project.
Cristobal
(2017) pointed out that the project manager performs basic management functions
(planning, organising, leading, and controlling). A project manager is expected
to motivate and inspire people working on the project. PMC is also responsible for time management.
Time is a very important resource in project management and in some cases, it
can be referred to as a constraint. Time is also a measure of project success.
Kerzner (2013) contends that successful project management requires the accomplishment of the project objectives
within time and cost, at the desired performance or technology level and while
utilising assigned resources effectively and efficiently. Riahi (2017) supports
this view by arguing that time is one of the three basic elements in a project.
The other two basic elements are quality and cost. Kerzner (2013) further
argues on the need for effective time management, as time is
a critical resource.
PMC
is critical in the entire life of a project, from identification to evaluation.
During the project identification stage, PMC examines the rationale for project
implementation. Projects are costly endeavours and the project manager ought to
conduct a thorough examination pertaining to the significance of project
implementation. After project identification, PMC plays an essential role in
project design. According to Mazur and Pisarski (2015), the design stage utilises
the data gathered, to specify the
project objectives, activities, outputs, and inputs. Thus, this should be
carried out to a sufficient level of detail to allow the estimation of
technical, social, and institutional parameters, and the preparation of a feasibility
study with an assessment of cost and benefits. The role of PMC to conduct the technical,
financial and organisational designs of the
project. The project manager is also responsible for coming up with the
implementation plans. After
the project design, PMC conducts project appraisal. Project appraisal is a critical review of every aspect of a
project plan by an independent team of specialists
to establish whether the proposed project is sound and appropriate for
resources to be committed to it.
It is apparent from the foregoing
that project appraisal is an analytic, systematic integrated, and comprehensive
exercise that seeks to determine whether or not the project is worth
implementing based on decision criteria and is only worthwhile for long term
projects. Project appraisal as a tool assesses proposals
before the commitment
of resources. At this stage of the PMC, projects can be accepted or rejected. The project manager is supposed to come up with
technical appraisals, institutional appraisal (interrogating the institutional
capacity to implement the project), and financial appraisal. The financial
appraisal determines the financial viability for sound implementation and
efficient operation. It aims at investigating the financial aspects of the project,
financial soundness, efficient operation, cost of production, return on
investment, prospects of marketing, profitability, effective, effective
controls, budgeting, and pricing.
The
project manager is also supposed to conduct a risk analysis. A risk is a potential and unforeseen
trouble spot that may affect the project. Possible project risks include financial
limitations, personnel constraints, budgetary constraints, and standard
constraints.
PMC has the task of managing specific project risks. Risk identification determines the specific risks that are likely to
affect the project (Cristobal, 2017). Following risk
identification, the project manager then
develops the appropriate plan to mitigate against the risks. External
risks are often beyond the control or influence of the
project team (Shibani and Sukumar, 2016).
Implementation comes after project appraisal so that the project is completed on time, on budget
and within specification. Project managers provide direction, coordination, and integration
to the project team. Project managers also have direct responsibility over
quality. According to Kerzner (2013), it is not worthy to
complete a poor
quality. A project manager ensures that the quality expectations of
stakeholders are met through quality planning, quality assurance, and quality
control (Riahi, 2017). After implementation, PMC conducts project evaluation.
Evaluation is conducted to establish whether the project is attaining the
intended objectives. Evaluation is the last stage in the project life cycle,
but in practice, there is a need for evaluation at each stage of the project
life cycle (Project Management Institute, 2013).
It is also essential to note that, during the entire project life, PMC is responsible for stakeholder management. Mazur and Pisarski (2015) consider individuals or groups as important project stakeholders. From this conception, one can note that stakeholders are central to projects. Project success is defined by the extent to which stakeholders are satisfied. According to Riahi (2017), one has to ask how good the quality of the products or services is in order to satisfy the customer. Project failure or success is dependent on PMC.
The Significance of Project Manager Assignment Decisions
Section 1.2 elaborated on the important role of PMC or project manager. It explained that the success of projects is dependent on who manages it. Therefore, a sound project manager assignment is critical. A project manager is the leader of the human resources in any project. According to human resource management literature (Bartlett and Ghoshal, 2011; and Armstrong et al., 2016), people are the most valued assets in any organisation (project), and they are the sources of competitive advantage. Therefore, sound project manager assignment, a process that ensures that the right people are assigned to projects, is of paramount importance. Richardson et al. (2015) argue that the project manager is expected to perform better than if there was no match with requirements that match his competencies.
Challenges Faced in Making Project Manager Assignment Decisions
It
was highlighted earlier that three elements
underpinning successful project management are time, cost and quality. The
appointment of the right project manager will ensure that these three are
achieved, thereby speaking to the success of the project. An inappropriate assignment could have
devastating consequences not only for the project but for the organisation as a
whole, as in many instances, projects have a direct link to the fulfilment of
organisational goals. It could also lead to issues such as low morale amongst
teams, cost overruns, and poor quality, etc. An appropriate project management
assignment will ensure that milestones are not missed and there is adequate
coordination of resources as well as efficient and sufficient communication
with stakeholders.
There
are several challenges faced when making a project manager assignment. One of
the biggest challenges that have been faced especially in multi-project
environments is the lack of managers that have appropriate competencies for the
project (Patanakul, 2015). Projects by their very nature have a strategic fit
to the overall performance and therefore a project manager with the right
competencies will ensure that this strategic fit is maintained, and
organisational goals are met.
Numerous
psychology graduates are employed in human capital management
business, and often get involved in recruitment, selection, and assessment tasks.
Nevertheless, Salgado et al. (2013), claim that despite the longstanding
employee selection research and practice, the field is still full of
controversies. Some of these controversies include exploring
‘settled’ questions, working on ‘intractable’ challenges, expanding into literatures
and organisational levels far removed from those historically investigated, and
constantly being pushed by practitioners, who continually are confronting
questions to which researchers have not yet produced answers. The key point
here is that as alluded to by Patanakul 2015, information on an effective
assignment is still rather scarce in the literature.
It
is essential to note that making a sound decision in project manager assignment
is a difficult exercise. There is room for errors when assigning managers to
the project. Some errors arise because it may be difficult to predict human
behaviour. There could also be unforeseen circumstances, which can affect a
manger’s performance (Armstrong et al., 2016). Moreover, Cristobal (2017)
argues that projects are complex endeavours, which involve multiple
stakeholders. There is a differentiation of functions in a project between
clients, contractors, subcontractors, suppliers, and financiers, or the
internal differentiation of the contractor’s organisation (degree of
manifoldness). Therefore, assigning the appropriate project manager is not an
easy exercise. Other challenges that have been identified, include the
availability of project managers especially in multi-project environments where
a project manager could be tied up on an assignment. There is also the risk of
overloading project managers if the assignment process is not handled properly
which could result in a failure of the project (Patanakul 2015).
The Criteria for
Project Manager Assignment
According
to Armstrong et al., (2016), it is essential in project management to be clearly focused
and measure
the ‘hole’ so that ‘square-shaped pegs are not put in round-shaped holes’. One
of the most important reasons for validating the traits needed in a specific
job is to ensure that the organisation avoids the
costs of poor project assignments (Mazur and Pisarski, 2015). The criteria for
project manager assignment stresses that a successful project assignment is one
in which a project manager possesses competencies compatible with project
requirements, that is, type of project, its size, complexity, and durations
(Patanakul, 2015). The competencies that are
correlated with project requirements should be the
area of focus.
We first analyse the important competencies
such as technical knowledge, administrative skills, and leadership ability
including communication, problem-solving, conflict resolution, integration, and
analysis (Project Management Institute, 2013). Additional competencies
required may include problem-solving techniques, administration, supervision, project
team management, interpersonal relations, and some other personal qualities for
selecting project managers (Riahi, 2017). Table 1 shows the aspects that are
considered in the project assignment.
Table 1: Aspects to Consider in Project Assignment
| Category |
Selection Criteria |
| Organisational Factors |
Organisational Objectives or Goals |
Innovation; Business Expansion; and High Profit Margins |
| Required Competencies |
Technical Competencies |
Technical Expertise; and Problem Analysis |
| |
Administrative Competencies |
Planning and Scheduling; Monitoring and Control; Team Building and Management |
| |
Human Competencies |
Leadership; and Communication |
| |
Business/Strategic Competencies |
Strategic Thinking; Stakeholder Coordination; and Business Sense |
| |
Additional Competencies |
Experience; Inter-Project Planning; Inter-Project Resource Allocation; and Multi-Tasking |
| Project Requirements |
Project Type |
Size; Duration; and Complexity |
| Organisational Constraints |
The capacity ofProject Managers |
The Effective Capacity; The Current Workload; and The Availability |
Source: Patanakul et al., (2003)
Moreover,
the project manager assignment ought to assess the potential of incumbents, as shown
in the Figure 1 below.
In addition, there are many studies to determine what makes someone a high potential for project management (Garavan et al., 2012, Swailes, 2013). Consider, for example, someone who could be promoted two vertical levels in five years is high potential (Bartlett and Ghoshal, 2011). Ambition entails that any project or business success comes with a price including personal time, hard work, emotional dedication, and perseverance. High-potentials can demonstrate the required personal drive and ambition to pay the price for success.
Consequently,
there are seven steps followed in the criteria for the project manager
assignment (Patanakul, 2015). The steps involved are the
identification of:
- Potential
projects to be assigned;
- The
strategic elements of the organisation and prioritisation of projects with
respect to their contribution to those strategic elements;
- The
project requirements and translation into the level of project manager
competencies that a project requires;
- Project
manager candidates and their level of competencies;
- The fit
between a project and a project manager with respect to the level of competencies
that the project requires and the level that the project manager possesses;
- The organisational/personal
limitations regarding the project assignments;
- Assignment criteria for a project to a manager based on the
priorities, the fit between project and project manager, and the
organisational/personal limitations (Patanakul, 2015).
The
introduction, adoption or implementation of Big Data
analytics poses benefits as well as challenges, threats, and problems that need
to be well managed to fully leverage on its potential. As a result of these
challenges, 65-100% of data analytics adoption or implementation projects
failed and are concluded as incomplete, overbudget and out of time (Axryd, 2019).
Materials and Methods
A qualitative research methodology
was used. The research design was discourse analysis supported by document
analysis. Laclau and Mouffe’s discourse theory was the most thoroughly
poststructuralist approach.
Discourse analysis can be used as a framework
for analysis of national or institutional identity to explore the significance
of national identity for interaction between people in an organisational
context such as a workplace. All discourse analytical approaches converge with
respect to their views of language and the subject (Jorgensen and Phillips,
2002). Discourse theory aims at an understanding of the social as a discursive
construction whereby, in principle, all social phenomena can be analysed using
discourse analytical tools. A discourse is understood as a fixation of meaning
within a particular domain (the knots in the fishing-net). A nodal point is a
privileged sign around which the other signs are ordered; the other signs
acquire their meaning from their relationship to the nodal point (Jorgensen and
Phillips, 2002). A nodal point in political discourses is “democracy” and in
national discourses a nodal point is “the people”. In medical discourses, for example, “the body” is a nodal
point around which many other meanings are crystallised. Signs such as “symptoms‟, “tissue” and “scalpel” acquire
their meaning by being related to “the body” in particular ways.
Discourse, then, can be understood as a type of
structure in a Saussurian sense – a fixation of signs in a relational net. Thus
the discourse is a temporary closure: it fixes meaning in a particular way, but
it does not dictate that meaning is to be fixed exactly in that way forever.
Discourse theory suggests that we focus on the specific expressions in their
capacity as articulations: what meanings do they establish by positioning
elements in particular relationships with one other, and what meaning
potentials do they exclude? Individuals are interpellated or placed in certain
positions by particular ways of talking. In discourse theoretical terms, the
subjects become positions in discourses (Jorgensen and Phillips, 2002).
Discourses always designate positions for people to occupy as subjects. For
instance, at a medical consultation the positions of “doctor” and “patient” are
specified. In this research the
positions of “project management” and “PPA” were used. Corresponding to these positions, there are certain expectations about
how to act, what to say and what not to say. The understanding of identity in
Laclau and Mouffe‟s discourse theory can be summarised as follows (Jorgensen
and Phillips, 2002:43):
- The subject is fundamentally split, it never
quite becomes “itself”.
- It acquires its identity by being represented
discursively.
- Identity is thus identification with a subject
position in a discursive structure.
- Identity is discursively constituted through
chains of equivalence where signs are sorted and linked together in chains in
opposition to other chains which thus define how the subject is, and how it is
not.
- Identity is always relationally organised; the
subject is something because it is contrasted with something that it is not.
- Identity is changeable just as discourses are.
- The subject is fragmented or decentred; it has different
identities according to those discourses of which it forms part.
- The subject is overdetermined; in principle, it
always has the possibility to identify differently in specific situations.
Therefore, a given identity is contingent –
that is, possible but not necessary. In summary, some of Laclau and Mouffe‟s
concepts of discourse theory are useful as tools for empirical analysis in this
research from this context:
- Nodal points, master signifiers and myths, which can be collectively labelled key signifiers in the organisation of discourse;
- The concept of chains of equivalence which refers to the investment of key signifiers with meaning;
- Concepts concerning identity: group formation, identity and representation; and
- Concepts for conflict analysis: floating signifiers, antagonism and Hegemony.
Discursive practices – through which texts are produced (created) and consumed (received and interpreted) – are viewed as an important form of social practice which contributes to the constitution of the social world including social identities and social relations. It is partly through discursive practices in everyday life (processes of text production and consumption) that social and cultural reproduction and change take place. It follows that some societal phenomena are not of a linguistic-discursive character. The aim of critical discourse analysis is to shed light on the linguistics-discursive dimension of social and cultural phenomena and processes of change at the university. Discourse encompasses not only written and spoken language but also visual images. Document analysis is a form of qualitative research in which documents are interpreted by the researcher to give voice and meaning around an assessment topic. Analyzing documents incorporates coding content into themes similar to how focus group or interview transcripts are analyzed. In this case publications and research papers on project management and project predictive approach (PPA) were analysed.
Evaluation of And Why Big Data Analytics Projects Fail
Project failure
is when the project objectives have not been met in terms of project scope, schedule, or
cost. Generally, IT project implementation is commonly associated with low levels of success
(Mpingajira, 2013). Big Data analytics projects are complex and difficult. They involve
fundamental changes to business processes, there is the implementation of new
and unproven technologies. More so, there is the requirement for urgent
short-term specialist resources, the constant pressure to deliver more quickly
and cheaply, the project risks are difficult to control, and the non-routine
projects are becoming more prevalent (Hussain and Manhas, 2016). A large number of
e-government implementation projects in Africa have failed to live to their
expectations (Mutula and Mostert, 2010). In Britain,
the government is believed to be wasting billions of pounds every year on
unsuccessful IT projects. The
literature points to a whole host of reasons for the failure of Big Data analytics project, we will focus on just
but a few of the very major ones for this paper as follows:
Misalignment
of Technical and Business Goals and Expectations
According
to Patanakul (2015), projects should be selected with their
ability to
meet the strategic fit to ensure proper strategic
alignment. Most
data science projects are undertaken to provide important insights to the
business team. However, often a project starts without clear alignment between
the business and data science teams on the expectations and goals of the
project, resulting in that the data science team is focused mainly on model
accuracy, while the business team is more interested in metrics such as the
financial benefits, business insights, or model interpretability. In the end,
the business team does not accept the outcomes from the data science team
(Preimesberger, 2019).
Lack
of Proactive Risk Management
Proactive
risk management requires improvement in managing both
existing and emerging risks and adaptability to near crisis
situations. A deeper understanding is required to measure and manage
emerging risks and their impact on the project. Risks should be
proactively assessed,
reported and mitigated.
Lack
of a Skilled and Efficient Project Team
Axryd
(2019)
suggests that 30% of the failure is attributed to the
lack of skills in organisations. The effects can be felt at
the executive level, line managers and the rest of the organisation. Neijt (2017)
postulates that a very skilled and efficient project team is required to
implement Big Data projects effectively
and successfully.
Poor
Project Communication Methodology
Project
management communication is pivotal in initiating and
mobilising a project
effectively. Industry practice recommends that a project
manager should spend 90 percent of their time communicating. Poor
communication contributes to project failure. A
project organizational culture where there is a free flow of communication is one of the critical success factors in project management.
Lack
of an Experienced and Visionary Data Scientist
A modern organisation requires a Data
Scientist to provide strategic direction and guidance on new
ways of looking at the data and realising its
potential value. Hence, the Data Scientist is expected to be efficient, experienced and visionary.
Poor Data Integration.
The major technological problem behind Big Data failures is the integration of siloed data. Old legacy systems face tremendous difficulties in connecting with the stored
data, struggle with acceptable formats, and incur huge
expenses in data cleansing. Consequently, Big Data projects become time-consuming
and often exceed the given timelines leading to customer dissatisfaction. However, here are tools
used for data management such as Hadoop which handles
different data formats and also used in Big Data
analytics projects (https://www.flydata.com/the-6-challenges-of-big-data-integration/ )
Change Management
This
is a huge challenge encountered when implementing a Big Data analytics project. The top management must be
comfortable with going through dashboards and getting high-level views
generated by analytics. Most functional heads who should be participating in
the project are threatened by the way analytics can affect their work and
fiefdoms. This creates fear amongst management, and they will resist
change for fear of their job security. The project will then lack the support
of the top management or is sabotaged. There is often
very little appreciation by executive management on the potential value of Big Data projects because of the challenges
associated with time consumption, waste of resources, and huge funding
requirements.Management
fear data driven decisions and they thought they are valueless if all decisions
are now based on data.
Lack of Infrastructure
Project failure is more certain when companies
solve Big Data problems using traditional data
technologies. The major impediments in achieving high success
rates with Big Data projects are the inadequacy of the budget and the use of
inappropriate technology. Big Data analytics is an interdisciplinary
approach that involves mathematicians, statisticians, data engineering,
software engineers, business analysts, etc and importantly, subject matter
experts. Depending on the size and scope of the project, companies might deploy
numerous data engineers, a solution architect, a domain expert, a data
scientist (or several), business analysts and perhaps additional resources.
Many companies do not have and/or cannot afford to deploy sufficient resources
because hiring such talents is becoming increasingly challenging and also because
companies often have many data science projects to execute, all of which take
months to complete.
Lack of clear business objectives
Big Data has been hyped and its growth of
implementation has been exponential at an enormous rate. It is very easy for
many organisations to be caught up in the hype. Most organisations enter the Big Data environment with a me-too attitude since the barriers
to entry into this space have been reduced especially with the availability of
cloud or proprietor hardware and commodity. There is a need for a clear
understanding of why the organisation should invest so many resources and time
into the project and reasonable or expected outcomes are established. Lack of clarity of objectives may lead to poor planning which leads to
project failure. Project predictive analytics can improve the success
of Big Data project analytics.
Other
Factors
Deloitte
suggests that other factors that contribute to project failure which include the inherent complexity of a project, the capability level of the
project team, and
the management of governance issues. Other
factors that have been highlighted include lack of effective leadership as well
as ineffective project scope definition. Furthermore, Big Data projects fail because of the impossibility of
accurately capturing requirements before a project begins. In addition,
organizations change over time, requirements are subject to constant change, a
phenomenon called requirements drift (Qassim,
2012). The more recent work by Axryd (2019) shows some reasons why Big Data projects fail (Figure 2).
Axyrd (2019) also proposes the following reasons:
Project
Predictive Analytics (PPA)
Predictive
Project Analytics (PPA), as a project risk assessment methodology, offers
the foresight to predict potential risks at any stage of the project and
identify areas where fixes for projects, transactions, and programs are needed
to mitigate risk (Ajah and Nweke, 2019).
PPA is a quantitative, fact-based analysis
of common attributes to determine the likelihood of project
success (Schmidthuysen, F. and Scheffold, P., 2017).
Predictive
Project Analytics is an analytical project risk assessment and management
methodology that examines a project’s characteristics and assesses whether it
has the appropriate level of oversight and governance linking complexity to
project execution. Identification of challenges in project controls allows
adjustments to be recommended to improve performance and probability of
success, lessen the likelihood of unforeseen setbacks that lead to cost
overruns, and preserve project schedules improving on time to delivery. It is an analytical project assessment
capability that examines project characteristics correlating complexity factors
and the likelihood of success using a probability distribution. Using a
proprietary database of thousands of successfully completed projects, PPA
provides clear insights as to the specific level of governance required
throughout planning and execution to achieve project objectives through using
of a proprietary database of thousand successfully completed. With PPA, one can forecast the possible outcome of the project under
various and different scenarios through the use of machine learning techniques.
According to Su Management fear data driven decisions and they thought they are valueless if all decisions are now based on data.(2013), PPA is based upon the premise that all projects can be measured against standard complexity characteristics as highlighted in the table below:
PPA was developed by
Deloitte in partnership with Helmsman Institute in Australia (Fauser Schmidthuysen
& Scheffold 2017).
The 5 stages of the PPA
assessment are summarised by the following
schema shown on Figure 3:
Why PPA Can Make A Difference
A
lot of project management research has been conducted with the Helmsman
institute over many years, which has led to the enhancement of the database as
well as there being a lot of industry expert input. Effectively this means that
your project will be benchmarked against a huge number of established scenarios. Quantitative
methods are combined with a database of empirical
project data in order to derive an objective
assessment of the inherent complexity and specific management characteristics
of the project. The basis of what
has formed the attributes and complexity scale has come from a wide range of
project types, thereby providing a robustness in the underlying engine. This
effectively means that the analytics are based on a wide range of scenarios
that algorithms have had to learn from.
How PPA Can Make A Difference
There
are a number of ways in which PPA can make a difference to project management
success. The fact that the PPA database contains over 2,000 projects of varying
complexity means that your project can be benchmarked against across many
different scenarios and best practice. The complexity engine plays a critical
role, in that Deloitte postulate that there is a direct relationship between
project complexity and project success. PPA therefore allows you to identify
all these issues and therefore put the right people in key places to ensure
project success. PPA allows one to
mitigate project risk and thereby reducing the incidence of project failure. PPA
can also compare the current levels of performance against
predicted expected levels.
Results
prioritisation can be achieved through PPA through an analysis of the project characteristics, and how these may be improved for the sake of project success.Using PPA,
one can realise cost efficiency as the foresights provide potential pitfalls.
The organisation is accordingly guided through the project life cyle stages by
using the PPA methodology in mitigating against potential risks and failures.
The
following are the elements of Project Predictive Analytics:
- Inherent risk and complexity assessment
- Interviews and structured content
- Project
predictive analytic review
- Analysis and synthesis
- Reporting
Future Project Management Methods – Data Mining, ML and AI
The real world of projects is increasingly getting
complex due to the advances in Science and Technology. The project to launch a
satellite uses Big Data and generates huge volumes of data, which data is
generated as the project progresses (Ertek,
et al,
2017).
Data Mining
One
of the methodologies for enhancing success of Big Data
analytics projects such as these is data mining. Hemlata and Gulia (2016)
define data mining as a process which finds useful patterns from large amounts
of data. The steps in data mining include exploration, pattern identification,
and deployment. One of the data mining techniques commonly used is called
association mining, driven by a popular algorithm often called
Apriori.
To
this data, can then one apply various data mining techniques such
as:
- Predictive
Data Mining – the prediction of unknown data values based on patterns
discovered in historic data. Under predictive data mining you have algorithms
that can perform classification, regression and time series analysis.
- Descriptive
Data Mining – identification of patterns and relationships within the examined
data. Under descriptive data mining, you can deploy algorithms that can perform
clustering, anomaly detection, association rules (e.g. Apriori), process mining
and retrieval (Pospieszny, 2017).
All
these techniques can provide invaluable insights which reduce project risk and
improve project performance, thereby minimising project failure.
Machine Learning and Artificial Intelligence
Other
approaches are Machine Learning (ML) and Artficial Intelligence (AI). Machine
learning is a branch of AI that allows computer systems to learn directly from
examples, data, and experience. In a nutshell, the process of applying machine
learning to project management includes ingestion of data, application of ML
algorithms, and, hopefully, delivering results such as predicting probability
of a certain event or discovering a pattern. In
the case of project management ML will be able to learn from previous project
experiences, whether this is in scheduling, lessons learnt, budgeting,
etc., and apply these lessons on new projects.
Increasing data availability especially in project management where
organisation hold large amounts of historical project data, machine learning
systems can on that historical data.
There
are many branches to ML & AI which lend themselves well to project
managements and these can be divided into supervised and unsupervised learning.
These techniques can be deployed to project management activities such as
estimation, scheduling, cost management, lessons learnt and associated
solutions. ML through supervised and unsupervised learning can help project
managers sort out priorities, re-plan instantly across multiple projects, and
predict future bottlenecks based on metadata. To
understand other impacts that ML can have on project management, one needs to
focus on one of the critical challenges faced by project managers, which is
achieving project goals within the given constraints. According to ClickUp
(2019) a company focusing on ML software for project management, project
management software with ML will have the ability to:
- Predict
and assign tasks to the rightful team members
- Automatically
tag users in comments that are relevant to them
- Visualize
notifications and updates based on their relevancy to a particular user
- Predict
and determine when deadlines aren’t going to be met
- Correct
task time estimates
AI
can also be used to address some of the traditional challenges that project
managers have always faced and algorithms have been developed to deal with the
challenges of for example:
- Prioritisation
- Prediction
&
- Re-Planning
The
success of data mining, machine learning, and AI is dependent on a number of
factors. Firstly, there is a need to identify a clear need and value for Big Data (Watson, 2014). For ML and AI models to be effective,
one would need a lot of data to get trained on – and data from different
projects might not be comparable to be classified in the same. There is also
the issue of how to collect data from across different projects to train
models.
Artificial Intelligence and
Machine Learning in Project Management
The following are
the benefits of Artificial intelligence, Machine learning and data Mining in
project management:
- Risk
Predictions
- Eliminating
repetitive tasks
- Better
project analysis
- Improved
productivity and efficiency
Artificial intelligence
is the future of project management as using AI combines the information of the
past projects to see what will work and want will not work.
Conclusion
Project
management has increasingly become pivotal to organisational success as project
management remains the major conduit for achieving organisational goals. With
the advancement of technology and science, Big Data
Analytics projects have become more and more complex and to avoid project
failure is important to critically analyse the reasons for Big Data Analytics Project Failure in order to address for
better execution of projects.
The
world of Big Data has ushered in exciting new tools in the
form of Machine Learning and Artificial Intelligence, which when harnessed
promise to deliver great transformation to the success of Big Data Analytics projects. The highly competitive
business environment faces tremendous challenges. The pressure to
find the ‘right’ personalities to enhance project success and customer service
and working teams has made project manager assignment decisions critical for
organisations. It is absolutely critical for projects to be managed and staffed
by the right people, with the right skills, right knowledge, right attributes,
at the right time, for the right job. The project manager assignment process
has become a key determinant to the success of
projects.
Acknowledgement
I deeply appreciate the Atlantic International
University for supporting this research work as part of my Doctor of Science
degree in Computer Science.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Conflict of interest
There is no conflict of interest associated
with this publication.
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