Big Data Adoption in Project Management: Insights From French Organizations
Tuba Bakici , André Nemeh , and Öncü Hazir
Abstract—Big data have the potential to revolutionize the project management, but it is not clear how. Despite the growing interest, there is a paucity of exploratory research to assess the practice and the impact of big data tools and technologies on project management approaches and practices. To address this gap, in this article, we initially embrace the technological–organizational–
environmental (TOE) framework as the research basis for the adop- tion process and conducts in-depth interviews with project man- agers from French organizations in different sectors. The synthesis of interviews reveals that many organizations are still in the early stage of the adoption process, mainly due to a lack of resources, especially expertise. Besides the factors of the three contexts of TOE, the project-level factors are also found to be critical for the adoption of big data in project management. Most of them adopted big data solutions to support them in the conception, definition, and execution phases of project management. Drawing on the findings, this article also provides guidelines to broaden the understanding of big data applications and their role in project management. Based on these results, we present a model with testable propositions and discuss insights that arise for organizations and project managers regarding how to apply big data tools and technologies to create value and overcome the related challenges.
Index Terms—Big data, project life cycle, project management, qualitative research, technology adoption.
I. INTRODUCTION
A
S THE use and application of big data tools1 are intro- duced in many industries, so do opportunities for busi- nesses to revolutionize the way they operate [10], [30], and even- tually, they improve firms’ productivity [3], [16]. Thousands of big data tools and technologies have emerged to perform various tasks and processes to enable businesses to glean insights at a relatively low cost. By adopting big data solutions, organizationsManuscript received 19 May 2020; revised 7 August 2020; accepted 14 June 2021. Date of publication 29 July 2021; date of current version 21 July 2023.
Review of this manuscript was arranged by Department Editor S. Makinen.
(Corresponding author: André Nemeh.)
Tuba Bakici and Öncü Hazir are with the Department of Supply Chain Man- agement and Information Systems, Rennes School of Business, 35065 Rennes, France. (e-mail: [email protected]; [email protected]).
André Nemeh is with the Department of Strategy and Innovation, Rennes School of Business, 35065 Rennes, France. (e-mail: andre.nemeh@rennes- sb.com).
Color versions of one or more figures in this article are available at https://doi.org/10.1109/TEM.2021.3091661.
Digital Object Identifier 10.1109/TEM.2021.3091661
1In this article, big data are defined as datasets in such a large scale that typical tools and software cannot store, manage, and analyze it as before. The big data come in various forms (structured and unstructured) from various sources and requires a fast-real-time analysis. Broadly, big data technologies include distributed file systems (Hadoop MapReduce framework), NoSQL, MPP systems, cloud computing platforms (storage and computing resources), in-memory database processing, and data mining tools [17], [40], [65].
can generate both social and economic values [44]. These tools have been changing the established ideas on management prac- tices [65] and presenting the potential for innovative products, services, and insights [29]. Eventually, big data become a part of the core business and operational functions by monitoring the operations in real time, optimizing the supply chain, selecting the right resources, increasing sales, and customer satisfaction [23], [28], [33].
Specifically, big data had a significant impact on operations management by transforming firm capabilities and enabling more effective decision making [94]. Project management is also inherently related to the use of data. Nowadays, the amount of data associated with projects is enormous, as businesses undertake many projects and group them in programs and portfolios [86].
Prior studies suggest that digital technologies transform project management by enabling fast and flexible forms of project organization and delivery [96]. Especially in dynamic in- dustries that deal with large data, project managers need to adopt new digitally enabled approaches that can allow more responsive and real-time decision making [60]. Thus, big data and their tech- nologies are, in fact, closely related to project/program/portfolio management and should be explored in-depth to understand their impact. In this study, we focus on project management, the management discipline that develops and implements various tools and methods to ensure that project targets are achieved.
While managing the projects, different approaches could be adopted in different life cycle phases, such as the conceptual design, definition, planning, monitoring, and controlling, and termination. We investigate these phases separately and discuss how big data tools can support the processes and managerial decision making in these phases.
Larson and Chang [57] point out the need to rethink project management in light of the promises that new technologies in general and big data in particular bring. Especially with the increasing complexity of projects, classical project management approaches might not be effective anymore [15], [74], [96].
Hence, besides the well-established tools that are critical for project management success [79], major technological inno- vations, such as service-oriented architectures (SOA), can be adopted to support project management further. For instance, Chenet al. [21] argued that enterprise SOA adoption aligned with business IT would facilitate better project management, scheduling, and execution. Such innovations also expand the role of project managers and create a new age project manager with continuous improvement [75]. Hence, project managers will have more knowledge on some other aspects, such as process transformation and program management methodologies ([58],
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p. 33). However, unlike the SOA-driven project management, big-data-driven project management creates drastic changes and requires the use of data-driven management methods [48], [54].
Furthermore, Spalek [86] suggests that the use of data analytics to support project management leads to better results compared with the traditional approaches focusing mainly on the balance of the triple project constraints, the scope, budget, and schedule.
Thus, project managers can benefit from big data in terms of new project management approaches.
Having discovered these numerous and various opportunities, researchers have recently started to study big data applications in project, program, and portfolio management. Even though big data are closely related to projects and their management, studies in this domain are scarce. This scarcity can be par- tially explained by the fact that projects are unique; however, many of the activities and processes are repetitive or already known. We also lack an exhaustive knowledge of the selection, adoption/implementation, and impacts of big data solutions for project management. More precisely, we need to know more about the process by which organizations adopt big data solu- tions. Thus, this article aims to fill the gap between what we know about big data management and analytics and the actual practices in project management. It also focuses on the nature and goals of big data initiatives, challenges, and other factors that contribute to big data implementation in project management.
With these in mind, this article addresses two issues: First, what are the major factors that influence the adoption and impact of big data solutions in the project management, and second, how progress in big data tools and technologies can shape the future of the project management approaches and practices. In order to answer these questions, we adopted a qualitative approach.
Such an approach is well suited to the research problem because it will give a rich coverage of the adoption choices that actors made and present what enables and restrains these choices at different project phases and organizational levels [56]. This also needs to be complemented by a global vision of the industrial factors that influence the adoption processes. Consequently, we conducted in-depth interviews with project managers from French companies in 11 different industries. It was our intent to interview organizations that have considered to implement big data technologies and to learn from their experiences.
Our results identify different challenges that face the adoption of big data tools in project management. These challenges range from the definition of the problem to the availability of resources needed (budget, time, and skills) to the organizational commitment and stability. The adopters of big data tools in project management use them for multiple reasons as follows:
1) project external environment diagnosis and prediction;
2) to match or exceeds competitors’ capabilities;
3) to meet the complex needs of project clients;
4) to implement agile management and assess projects per- formance (KPI).
In the majority of the cases, the adoption of big data tools took place at the projects’ early phases and took a top–down approach.
Nonadopters, in general, see no need for big data tools because only a small amount of data is needed to be analyzed, the size of the company is small, only a few projects are carried out, and the company activities are limited to a small geographical region.
The article is organized as follows. In Sections II and III, we first summarize the literature and then present the research
method in Section IV and the results in Section V. Finally, in Section VI, we discuss the research’s implications and present promising research directions.
II. ADOPTION OFBIGDATA INPROJECTMANAGEMENT: A TECHNOLOGY–ORGANIZATION–ENVIRONMENT
(TOE) FRAMEWORK
The quest toward a “smart” project management information system that
1) uses different sets of information;
2) that is agile;
3) that supports a range of different functions;
4) that is intelligent in terms of analysis, and the overview of information sets held throughout the project life cycle was previously highlighted by researchers [50].
At that time, different challenges, mainly technological, were preventing the building of such a system. Today, a massive amount of data is arriving daily with high volume, velocity, and variety [65]. Additionally, the use of data has changed toward the large-scale real-time analysis of data, including text, audio, video, images, transactions, sensor data, GPS data, log files, etc. [17], [40]. Furthermore, big data collected and stored from different business functions required special techniques, such as data mining, machine learning, neural networks, social network analysis, signal processing, pattern recognition, optimization methods, and visualization to be able to process such volume of data [22]. These techniques also need specific applications that include distributed file systems (Hadoop MapReduce frame- work), NoSQL, massively parallel processing (MPP) systems, cloud computing platforms (storage and computing resources), in-memory database processing, and data mining tools [39]. De- spite the increasing number of technologies, many organizations still struggle to select the right tools to handle big data [45] and even hesitate to adopt them at all.
In their project management practices, organizations can benefit from big data in several ways. First, as organizations undertake many projects (i.e., a project-based organization) to accomplish their organizational goals, they need to implement effective project portfolio management2 [86]. However, to select the right portfolio for the company and choose between several project ideas, data analytics can support the process by examining the relevant data. It can then weigh up the possible project alternatives and prioritize the most promising ones by taking various resource constraints into consideration [84].
While selecting the projects to invest in, an important step is the identification and assessment of risks, their probabilities of happening, and their impact on the project’s success. The results of this step guide managers to make the investment decisions. Risks can be examined by descriptive (current state) and predictive (future related) data analytics models [86], and
2According to Miterevet al. [68] a project-based organization is one that makes the strategic decision to adopt project, program, and project portfolio management, as business processes to manage its work. Consequently, project based indicates the nature of the business processes the organization adopts.
Projects could be defined as “temporary organizations to which resources are assigned to do work to deliver beneficial change” [89]. A program is “a collection of projects managed together to deliver strategic change objectives that cannot be achieved by one project on its own.” A project portfolio is “a permanent organization consisting of a collection of projects or programs sharing common resources” [89], [90].
the probability of achieving the project targets can, thus, be increased [78].
After selecting the projects, big data tools can also support the planning and monitoring of projects and prevent managers from being overstrained by a large amount of data available for an individual project [84]. Visualization of the data can provide a global view of the project as well as more detailed breakdowns, such as detailed what-if-analyses and monitoring reports [78]. At this point, Vanhouckeet al. [92] point out that the data analytics enables project managers to measure the project progress, predict time, and cost more precisely. The adoption of big data technologies for data collection and analysis also alters maturity models and creates more holistic and adaptive maturity models [95].
Finally, with big data tools, project managers can make re- liable real-time decisions. Big data tools can also improve the overall decision making in projects [47]. Especially in an era of rapid technological changes and the emergence of various opportunities, decision-making practices in projects cannot be expected to remain stagnant. Thus, big data are one of the promising technologies for project management.
One might assume that big data adoption is similar to any tech- nology adoption (e.g., SOA and enterprise resource planning).
However, big data technology adoption comprises challenges originating from the previously stated characteristics of big data, namely, volume, variety, and velocity [18]. It also presents the high levels of risk due to the uncertainty of the adoption outcomes. It involves layered complexity due to a selection of several technology components for data storage, data process- ing, and data management to build a big data system [20] and new technologies to deal with the challenges emerging from big data and their characteristics [59]. Thus, big data adoption requires significant changes in terms of infrastructure, resource allocation, and processes [20]. The particular characteristics of big data, project managers as involved individuals, and the appli- cation context should all be taken into account when considering big data adoption in project management.
Recently, few researchers have attempted to investigate the underlying factors that affect organizational intention to adopt big data (e.g., [14], [70], and [87]). However, the number of such studies that focus on organizational adoption is rather scarce since the adoption is in its early stages [94]. Previous studies on this matter identified in-house information system competence, industrial competitiveness, company size, and financial readi- ness of companies as major influencing factors for the adoption [1], [14], [70]. While the factors influencing the adoption process for big data have already been extensively discussed, none of the prior studies focused on the adoption of big data tools and technologies for project management. Even if the project managers use tools of different disciplines, the development of the field of project management justifies the research that focuses on the specificity of their practice [12], [49].
Adoption theories describe the introduction of technological innovations in organizations. To understand the adoption of new technologies, such as big data, we need to use a firm-level adoption framework. Introduced by Tornatzky and Fleischer [88], the TOE framework is one of the technology adoption models. It describes the impact of technological, organizational, and environmental factors that influence the organizational
decision-making process of technological innovation adoption.
The technological context involves relevant internal and external technologies. The organizational context represents the multiple characteristics and resources of a firm, such as the organizational culture, structure, processes, and policies. The environmental context represents the domain where an organization conducts its business, including its competitors, customers, suppliers, regula- tions, industry structure, and size. All these contexts present both opportunities and limitations for technology adoption [34]. The existing studies using the TOE framework have demonstrated its broad applicability across a number of technological, industrial, and cultural contexts [8]. For instance, this framework was used to understand the adoption process of different innovations by firms, such as the electronic supply chain management system [63], eCRM system, or IT innovation [97].
In the context of project management, these factors have been used, separately or combined, to understand the adoption of different new technologies, such as software and IT systems.
Liberatore and Pollack-Johnson [62] showed that environmental factors, such as demographics and work environment character- istics, could impact the selection and use of project management software [77]. Technological factors and the role they can play in supporting project managers in managing projects effectively and efficiently had also been discussed in the literature [5].
Project management technologies’ acceptance and belief in its benefits on the project performance had also been highlighted as factors that influence their acceptance [31]. Finally, Bani Ali et al. [9] studied the organizational and project factors that affect project professionals’ acceptance of project management soft- ware and its perceived impact on their performances. All these arguments show that a framework that combines environmental, organizational, technological factors constitutes a valid initial framework to understand the factors that influence the adoption of big data tools in project management.
III. BIGDATAIMPACT ONPROJECTLIFECYCLE
To explore the impact of developments in big data on project management practices, a few studies addressed the topical areas, such as planning and control, evaluation of risks, and perfor- mance, in addition to some specific problems related to the project lifecycle management.
Among these studies, some examined the conception phase of the project lifecycle by mainly using a case study approach (e.g., [51] and [99]). Studying the project costs, specifically the construction costs, Zhang et al. [99] developed a system that integrates big data analytics in evaluating the tender prices of a metrostation construction project. In this way, managers could estimate the cost of the new construction projects more precisely.
In general, the data used in construction cost management show the characteristics of big data. Therefore, big data techniques could be used to support cost estimation. The statistical and artificial intelligence (AI) algorithms are used to compute a reasonable bidding quotation range and help managers to make an offer. In the context of cost management, Ahiaga-Dagbui and Smith [2] focused on predicting the cost overruns in construction projects. Using data mining techniques, they developed models that can be used at the initial cost estimation phase. These models can lead to more reliable cost forecasts and, hence, decrease the
procurement costs. In addition to cost management, a number of other studies addressed the optimization of resource planning, such as crew assignments [19] and scheduling [52], using big data techniques.
Jamil and Carvalho [51] addressed the conception phase and presented the examples of real cases from various sectors. For instance, they considered how big data analytics could support governments in decision making to construct new interstate traffic roads. By disseminating information about the projects in social media at the conception phase, a large set of data on the reactions of the population, companies, and politicians can be created, and useful conclusions can be drawn. They also described two more case studies, one on the adoption of Industry 4.0 technologies of a traditional industrial food processing company that analyzes customers’ reactions to the transformation. Another one is on the advertisement projects of a communication agency that collects data from different channels about the market trends and customer preferences to define their marketing strategies. Thus, researchers have effec- tively used big data to optimize resource planning and cost estimation.
At the planning phase of projects, the analysis of risks is crit- ical for many sectors. Big data analytics allow new ways of risk analyses in areas, such as logistics, construction, finance, eco- nomics, and industrial systems [24], [25], [73]. Consequently, risk factors could be considered while making plans. In this way, robust plans and schedules can be generated, and proactive managerial approaches can be adopted.
Regarding project risk analysis, Rekha and Parvathi [82]
focused on the risks in software development. They pointed out that risks can occur in each phase of software projects, and the risk factors can be identified using big data analytics. In software development projects, agile project management has been widely adopted due to the need for frequent requirement changes. The agile approach uses iterative development cycles and bases on continuous design and development rather than developing a product or a process upfront. The project starts with a high-level project scope that is much less detailed than the ones defined in the traditional project planning approaches.
In this regard, Batarseh and Gonzalez [11] addressed the agile software development lifecycle. They investigated that how software failure can be predicted using data analytics in the development phases.
Unlike the software industry, the construction industry usu- ally opts for the traditional process-based project management that emphasizes the preplanning and effective control. Big data analytics can also be effectively used to identify and analyze the risk factors in these projects. In this regard, Owolabiet al.
[73] predicted the delays and pointed out the completion risks using different predictive techniques for public–private part- nership projects. A ranking of delay causing factors supported project managers in focusing on some specific risk factors and avoid project delays or cost overruns. Likewise, Alleman and Coonce [4] developed a new method for analyzing trends and forecasting the time and cost performance of projects using autoregressive-integrated moving average algorithm. This new approach has revealed the underlying trends that were not possible to detect by using only the traditional earned value management calculations.
Vanhoucke [91] focused on the monitoring and controlling phase and suggested using big data statistical analysis for control purposes. In this regard, Colin and Vanhoucke [26] opted for a statistical project progress monitoring approach and made use of the simulation based on the control charts. Their method facilitates detecting the deviations in project performance on time. All these examples show that big data enables the exten- sion of risk analysis and project control to deal with complex project management problems. They also provide a more precise estimation of the risk factors using the more abundant data.
In projects, big data can also help to allocate resources ef- fectively. N’Cho [71] focused on the recruitment of human resources and studied forming the project teams in the aerospace industry. In their case, data analytics tools are used to collect and analyze all information about the candidates. The analysis helps to recruit the qualified and experimented project team mem- bers according to the requirements of project phases. Kusimo et al. [55] examined the current challenges related to resource management in construction projects. By interviewing some construction industry experts from the U.K., they identified seven major problems that resulted mainly from the poor man- agement of data. In this regard, they concluded that the creation and effective use of a resource database that gathers information from all the projects of an organization would help to effectively deal with these problems.
Similarly, Ramet al. [81] explored the construction industry and underlined that the adoption of big data tools could facil- itate project management capabilities in this industry. Studies focusing on mobile network design showed that big-data-driven optimization and analytics facilitate the efficient allocation of vast resources, network optimization, and operational cost min- imization (e.g., [46] and [100]). With the insights gained from the big datasets, organizations can improve the user experience quality, make informed decisions for future investments, and make better choices on where and how to deploy the nodes in the networks and predict network traffic trends. Thus, big data tools not only provide efficient resource allocation but also novel insights for data-driven decision making.
Another application in project management is the perfor- mance evaluation of the projects (e.g., [85]). In this context, Olsson and Bull-Berg [72] examined building and transportation infrastructure projects. Based on their interviews and review of the literature, they listed the critical factors of using big data as follows: availability, applicability, relevance, privacy, ownership, cost, and competence. They emphasized that a dis- tinction must be made between the evaluation of ongoing and finished projects. The assessment of ongoing projects relies mostly on schedule and cost data available in the IT system, whereas finished projects can also be evaluated with data col- lected from the users through surveys. Li et al. [61] focused on the poverty alleviation projects in a Chinese town. They analyzed the project performance management problems and proposed countermeasures based on using large data entry and processing technology. A recent study by Bilal and Oyedele [13] presents a benchmarking system for tender evaluation that helps contractors to produce the high-quality project tenders and achieve greater profitability performance. They generated benchmarks from big data of 1.2 TB and tested the system with a case study of an underground cabling project. Thus, big data
could be used to evaluate a project’s success and improve the decision-making process. However, as Ekambaram et al. [37]
stated, it is also essential to consider the costs of using big data and compare with the more conventional methods.
All these studies point out that the big data and project management are interrelated, and the decision makers can ben- efit considerably from the accumulation of knowledge through analyzing big datasets. Summarizing all these application areas and results, we conclude that the big data analytics promises considerable support and benefits by enabling PMs and top managers to measure the progress of their projects and to predict their time and cost more precisely [92]. Despite all its potential benefits, a recent survey within the polish construction industry perceived a low interest in implementing big data analytics [42].
Besides such low interests and limited implementations, the current studies mainly examine the managerial functions, such as risk assessment, cost estimation, or planning separately.
However, these functions should instead be seen as a whole to evaluate the improvements made by applying big data tools in managerial decision making. Additionally, the literature only covers cases of a few industries, mostly the construction. Thus, despite these recent studies, we still lack an exhaustive knowl- edge of adoption/implementation and the impact of big data technologies and tools on project management decision making.
IV. RESEARCHMETHODOLOGY
Considering the state of the literature on the adoption of big data in project management, which is still in the stage of theory construction, and our aim, that is to understand the major factors that influence the adoption and impact of big data solutions in the project management from the perspectives and behavior of project managers and organizations [53], an exploratory research needs to be undertaken. Such an approach can enable a more systematic and in-depth examination of the phenomenon while perceiving the subjectivity of knowledge.
A. Data Collection
The primary data collection method comprised semistruc- tured interviews with one project manager from each organiza- tion to access their perspectives and experiences of big data im- plementations in project management within their organizations.
More precisely, we wanted to understand the motives to adopt big data tools, identify the type of tools, the obstacles/enablers of adoption, the experiences in project life cycle management, and the impact of these tools on project managers’ effectiveness and profession. Thus, project managers rather than organizations or projects are the unit of analysis of this article.
To address our article questions and objectives, the TOE framework is initially adopted. The constructs under each of the three contexts of the TOE are adapted to the project management setting. To collect empirical data, we used an interview protocol with questions derived from this theory and the literature [83].
The protocol includes questions, such as: What type of big data tools is used in project management? What are the approaches to manage complex projects with big data tools and techniques?
How are the project management practices altered with big data tools? What are the project management lifecycle phases in which these tools could be effectively used? What are the
challenges faced during the adoption/implementation of these tools? What types of skills are required? Additionally, a variety of demographic, organizational, and project-related questions were asked.
As Cooke-Davieset al. [27] stated, a new project manage- ment approach would not deliver expected results if it does not comply with the organization or with its environment. This means that the factors affecting the adoption of big data for project management might vary in different sectors of activity and organizational contexts. Thus, a set of criteria is used to identify and select the interviewees, which were based on project manager profiles, organizational size, and the type of industry. A total of 117 organizations and project managers were identified as potential interviewees and we started to interview project managers who agreed to participate in our study. Interviews continued until we reached a thematic and theoretical saturation [43]. In the end, we conducted 16 interviews from 11 different industries in France from May 2019 to July 2019. The duration of these interviews ranged from 20 (for nonadopters) to 62 min, with a total duration of 10 h and 34 min. Table I summarizes the characteristics of the interviewees and shows the criteria used to select the interviewees. The interviewees remained anonymous to ensure confidentiality, but the data in Table I can reveal the profiles of interviewees and their organizations. All the interviews were recorded and transcribed, allowing the accurate representation of opinions. A majority of these interviews were conducted in English via video conference, and some were conducted in French and then their transcriptions were translated into English.
The data also present the characteristics of projects managed by these managers. Project duration ranged from one week to two years but mostly less than a year. On average, project managers managed yearly between 3 and 20 projects of different sizes. The budget for these projects ranged from 3000 to 50 million euros.
These projects included different organizational departments, and some of them regrouped external stakeholders, such as suppliers and customers (B2C/B2B). Even though some of them work with data scientists, data scientists are not directly a part of the project team but instead they interact with them as a separate department. The size of project teams ranged from 3 in the small projects to 150 people in the big ones. Some of these projects were considered complex based on the number of stakeholders that they regroup the number of subprojects or the technology that they implement.
B. Data Analysis
Building on the recommendations of Miles and Huberman [67] for data display, reduction, and verification, we conducted a thematic content analysis to summarize and categorize the empirical data into themes [41] using NVivo 12, a software pack- age for analyzing the qualitative data. As in our case, thematic analysis is fitting for exploratory research and theory-building objectives [36].
Based on our article questions and literature, we adopted and completed the predefined themes of the interview guide [83]. Initially, two researchers applied a coding schema to a subset of transcripts, discussed and refined the schema until both researchers agreed on a more comprehensive set of codes. This
TABLE I
CHARACTERISTICS OFINTERVIEWEES
TABLE II THEMATICCODING
way, we ensured a reliable and accurate analysis of the data [67].
Table II presents the thematic coding, which was also broken down into subthemes. During the coding process, new factors related to project level have emerged. Thus, as will be described in Section VI, our analysis resulted in a revised TOE framework that confirmed the major factors derived from the literature and identified the new ones related to projects. Next, we summarize the interview results.
V. RESULTS
We analyzed the interviews and summarized the findings by focusing on the adoption and implementation of big data tools and technologies for project management. More specifically, we examined the challenges, motivations for adoption, and reasons for nonadoption. The results can be summarized as follows.
A. Challenges to Big Data Solutions Adoption/Implementation While our interviewees, in general, believe in the potential of big data for project management, they emphasize the challenges to overcome in order to transform big data from an idea or
“Buzzword” into a reality/application. Ensuring the availabil- ity and quality of data is among these challenges. In some companies, managers discuss that it could take a lot of time and investment to figure out how big data can be used. They also struggle to choose the tool to use. When an application area is found out, it is usually challenging to discover which data are available, which data to collect externally, and decide whether data can be shared with third parties, such as customers or suppliers. Some interviewees cited expertise/technical issues, organizational stability, and data control as the main challenges for successful big data solutions’ implementation. They usu- ally consider that these solutions are difficult to implement/use and implementation requires an organizational change. They
TABLE III
CHALLENGES OFBIGDATAADOPTION
underline the need for a “step-by-step approach” that ensures that all parties confirm willingness to adopt the new solutions, train the teams, overcome the technical problems, which are mostly related to the creation of big data platforms, and create data collection checkpoints to ensure the quality of continuous data flow. Some interviewees also mentioned the challenges at the individual level. For instance, even the organization itself adopts big data tools, project managers need to invest time and effort to be able to use them. This needs to be done mostly by themselves in addition to their daily work. Referring to big data platforms, one interviewee said, “I need to learn, I need to have time to use it” (PM 13). Lack of training and complexity of these tools discourage many project managers. Some of the interesting comments of the interviewees on these challenges are listed in Table III.
B. Adopters of Big Data Solutions in Project Management and Their Motives
The adopters of big data solutions are mainly motivated by different factors, such as governmental requirements or supports, the nature of the industry (high tech versus traditional), organiza- tional culture, and size, by competitors’ preemption and clients’
needs. More precisely, the main motives of the adoption are as follows.
1) Project External Environment Diagnosis and Prediction:
Big data tools are usually adopted based on the sectoral charac- teristics, trends, and expectations. Besides the sectoral require- ments, the demand to use big data tools might also arise from the government or customer requirements who seek for precise analysis. The nature of the sector and competitiveness influence
the innovativeness of the organizations within the sector, so their adoption of big data. The implementation helps the project managers to understand how changes in the sector influence their project results, portfolio performances, and relations with the stakeholders.
2) To Match or Exceed Competitor Capabilities: Some in- terviewees mentioned that big data tools were adopted to reach to the competitors’ capabilities or to be different from the competitors as in decision making.
3) To Meet the Needs of Project Clients: Big data tools have also been used to reach potential clients. This is the case in the real-estate sector. In this specific case, project managers search for large databases to find clients who have communicated their needs on different platforms. Then, based on the criteria speci- fied by the clients, big data tools are used to prepare proposals or bids.
4) Agile Management and Project Performance Assessment:
When companies have a large number of projects conducted in different locations, big data tools help the companies to monitor and control these projects in real time and based on a large number of indicators to optimize the results of these projects.
Additionally, these tools help companies to communicate cor- rectly and timely the right information to the right stakeholder, reduce the time spent on each task, and allow all stakeholders to work together (PM1).
In majority of the cases, the adoption of big data tools was at the early stages3 and initiated by the top management in a top–down approach, without a clear adoption plan and, in some cases, by the needs of project managers to deploy tools to meet their needs. Some adopted them only for specific departments, such as production. For the implementation of big data tools, interviewees suggest that organizations should start by defining how they can use such tools to achieve their organizational goals.
As PM7 explained, “Big data is a tool; it is not an objective.”
However, due to the amount of the change it requires, it necessi- tates communication and collaboration of different departments.
In terms of resources allocated to big data solutions, adopters think that their companies do not allocate sufficient financial, human, or operational resources, but much more resources are needed in order for them to be able to benefit from these tools.
While for some of them, it is an optional tool, for others, such as pure players, it is the core of their business, as one explained this: “it is not an option” (PM6). Additionally, they think that adopting the tools does not mean full exploitation of its potential unless the management sufficiently communicates the use of the tools to other departments of the organization.
Big data tools alter project management in various ways. It changes the managerial practices and the role of the project manager. Managerial practices evolve and change with the use of big data tools (PM2). “It changes all the working processes”
(PM6). Project managers need to realign their projects for using big data. The role of the project manager is also evolving with big data tools. They need to understand and learn how to use these tools. “Even if there is a tool that exists, you have to code some new functionalities, to display something very precise, you have to code.” (PM13)
Big data also affect the project life cycle management. The big data tools are usually used in the following phases of the life
3Three or four years ago (PM14).
cycle: conception, definition, and execution. Some interviewees highlighted the interrelationship among the phases and indicated that if big data are used in the initiation stage, they have to/can be used in the control phase as well. None of them mentioned about the closing phase and postproject evaluations. The tools that they use include the MariaDB server, BigQuery, MySQL, Pentaho for Big Data, Adobe Analytics, and cloud-based big data platforms, such as Darwin, Skywise, and Confluent. Table IV presents some interview scripts.
For project management, big data can be aggregated from in- ternal/organizational (e.g., transaction records, emails, decision support systems, etc.) and external sources (customers, suppli- ers, competitors, government, etc.) in structured, semistructured, and unstructured formats (text files, machines logs, emails, weather, etc.) in real time from multiple applications and lo- cations. Based on their projects and industries, interviewees defined the type of data they use as internet traffic (PM 5), public data (PM 12), social media activity (PM 7), data from sensors about the physical environment (PM 14), and transaction data (PM 7).
Another interesting observation is that the managers focus on the use of tools to improve project performances; they rarely talked about the practices or potentials in program and portfolio management.
In terms of skills needed by the project managers in the era of big data, the future project manager might need to have two roles:
data scientist and project manager. Interviews show that coding will be considered as an essential competency (see Table IV).
C. Nonadopters of Big Data in Project Management and the Reasons
The nonadopters could be divided into two groups: the skepti- cal and strict nonadopters. The skeptical companies have doubts about the potentials and usefulness. Some of them are currently testing some tools cautiously in one function or one project phase. This skeptical view is reflected in the evaluation of the time needed for big data tools to deliver their potential and their impact on their operations. More precisely, they estimate that it needs between four to five years to turn the promises into reality;
however, they might be underestimating the benefits. The indi- rect benefits, such as the contributions to project management maturity, need to be taken into account along with the financial benefits. One interviewee also mentioned that persuading all the stakeholders, such as associates on board for sharing data, restricts their adoption (PM7).
The strict nonadopters see that there is no requirement for them to adopt big data tools because of the nature of their tasks and projects, i.e., no need for data, only a small amount of data is analyzed, and only a few projects are carried out. In addition, the nature of the organization discourages the adoption, i.e., the size of the company is small; the company activities are limited to a small geographical region. Some interviewees underlined some organizational barriers that limit data sharing between functions or associates (colleagues or partners). Some also do not feel an environmental pressure as one said: “We are one of the biggest players, and if we do not use it, they are probably not thinking of using it right now” (PM1). Finally, some firms consider that the big data analytics is not a part of their core business, so they are considering subcontracting and buying services from other
TABLE IV ADOPTERS OFBIGDATA
TABLE V NONADOPTERS OFBIGDATA
companies and providing the reports that they need for their projects. However, even if the analysis is subcontracted, project managers need to know about the data characteristics and how the analysis was performed; and they need to communicate the project needs well to the service providers. Table V presents some interview scripts.
In summary, the reasons for nonadoption could be the lack of managerial support or initiatives; “But also the vision of the company” (PM10) and culture that see the potential of big data as exceeding its cost of implementation “I think it will be the support of the management, the willingness to implement it.”
(PM09), the nature of the activity in the industries where firms are working “I really think it is because ( ….) our activity ( ….) does not really need to use a lot of big data” (PM08) that does not create data that has the 3V characteristics “I guess that is probably because it (i.e., data) is not that big and we do not have massive volumes of projects in a row at the same time today”
(PM1).
VI. DISCUSSION: THEORETICAL AND
MANAGERIALIMPLICATIONS
In this study, we shed light on the major factors that in- fluence the adoption and impact of big data solutions in the project management by interviewing several project managers that work in different companies: small and big, private and public, and that belong to different sectors. In big data adoption for project management, our main observation is the need for a framework to support project management decision making.
The development of this framework requires examining the conditions, requirements, project types, and phases and business decision-making processes that lead organizations to adopt big data tools.
While our study shares some similarity with previous research on big data adoption at the organizational level (e.g., [87]), such
as competitive pressures, relative advantage, skills required, management support, and cost aspects, our study adds some drivers that are uniquely related to project management context, such as the project phases, where big data are mobilized, agile management and project performance assessment, and meeting the needs of project clients.
In terms of theoretical implications, this study, as one of the first exploratory studies, helps us to understand what the challenges, motives, and impact of big data adoption on project management are. We also noticed that such challenges and impacts not only exist at the organizational level but also at the national/institutional, industrial, project, and individual levels.
These elements will also prepare the base for more future studies on this interesting subject.
Technological Factors:For decades, the forecasts of project planning factors, such as demand and cost, have been sig- nificantly inaccurate [38], so the lure of applying rich and new types of data with a real-time perspective would encourage many project managers to adopt big data technologies. Addition- ally, overcoming the data collection and evaluation limitations due to sampling, project managers would take a census of the population and generating more accurate insights and forecasts with big data technologies [95]. However, projects utilizing big data technologies involve large amounts of and various types of data and require a selection of several technology components [20]. This is significantly different from the traditional projects and project management that project managers are used for practicing. For instance, project managers may need to use new statistical methods for big data and may even adapt the traditional statistical issues to new types of data [37]. Our results also showed that the complexity of these tools might discourage project managers from adopting and using them. This could be interpreted by the fact that a complex technology may need a radical change in the organization’s project management style
[76]. Additionally, we showed that when big data tools are expensive, they would be more difficult to adopt by companies that do not have sufficient resources. This leads us to formulate the following proposition.
Proposition 1: Technical complexities with layered compo- nents, accuracy, compatibility with the existing project man- agement methodologies, and costs of big data technologies will have an impact on the adoption and use of big data in project management.
Environmental Factors: The national and institutional con- text, where organizations conduct their business, was consid- ered as an important determinant of their behavior toward new technologies [8], [34], [88]. Some researchers invited to consider both technological changes and institutional context to understand the transformations in the nature of work and organizing [6] and to understand the adoption of new tech- nologies in operations and supply chain management [98].
Our field research demonstrated that some differences in big data adoption in project management could be related to the difference of national/institutional context and that companies in some countries, such as the USA, might be in an advanced position. An interviewee also pointed this out, “you know in France, we like to separate data and projects.” (PM12). More- over, several interviewees mentioned the difficulty of finding such expertise within the country. Thus, we think that there is a national and institutional difference in the adoption of big data tools in project management that has to be investigated in future research.
Big data applications in project management are especially relevant for dynamic industries that deal with extensive data, such as online retailers. Industries differ according to their dynamics of technological change or what is called technolog- ical velocity “rate and direction of change in the production processes and component technologies that underlie a specific industrial context” [66]. The high-velocity levels characterize high-tech industries. In these industries and in order for firms to survive, managers need to catch up with the industry velocity and adopt new technologies quickly [69]. In such organiza- tions, we observed that project managers need to adopt big data approaches that can allow them to make reliable real-time decisions. This is also confirmed by prior studies stating that big data adoption is positively influenced by competition intensity (e.g., [70]). We have noticed and based on our interviews that firms that belong to high-tech industries may have more tenden- cies to adopt big data tools than firms in low-tech industries4 may, in order to match competitors and technological change.
This led us to doubt that in high-tech industries, organizations are more likely to adopt big data tools in project management than the ones in traditional industries are. This leads to the following proposition.
Proposition 2: Organizations in different institutional con- texts and industries will have different attitudes toward the adoption and use of big data in project management.
Organizational Factors:The benefits of big data tools have been highlighted for both big and small firms’ projects [32].
4The OECD and Eurostat classified industries based on their R&D intensity, i.e., the ratio of R&D expenditures to the output value of the sector. A low-tech industry has an R&D intensity below 1% (OECD, 2002; Smith, 2005), such as hospitality, food and beverage, textile, and the high-tech industry with R&D intensity above 5%, such as pharmaceuticals and computers.
Previous research showed that there are different challenges that large and small companies face, and that leads to a scene that is populated by success stories of larger companies [64], which have the advantage of more sophisticated human and organizational structures. Our results show similar challenges in attracting skills needed (e.g., data scientists) that are only affordable by big firms. Additionally, the fact that these firms’
portfolio of projects is large would lead to an apparent need for big data tools. These technologies are seen as a change initiative in large firms. However, some interviewees argued that organizational size is not a distinguishing standard for adoption.
They mentioned that tech startups that are born using big data have the necessary expertise, culture, and type of business.
This contradicts prior studies (e.g., [1], [14], and [70]) that emphasize the role of company size. Also, organizations with a project-oriented environment might be more inclined to adopt big data technologies as they can easily redesign their structure.
This redesign could be harder for other organizational structures, especially considering that the big data technologies require coordination and commitment between departments to collect and analyze the data [93]. Thus, future research should consider this dilemma between the nature of the organization and organi- zational size. This dilemma may be solved by considering other factors, such as the percent of work effort in project management, top-management commitment of resources to attract, and the availability of data strategy.
Besides the required drastic changes in project management, big data technologies are increasing with exponential speed, so project managers and their training fall behind comparatively.
However, understanding the technology is critical for big data adoption success, such as in any other technology adoption [35].
Unlike other technologies, in project management, this is more important at the individual level, as the project manager, than the organizational level. Before embarking on project management with big data technologies, project managers must understand which technologies perform what types of tasks and the strengths and limitations of each. However, considering the vast number of components the big data systems involve and the required train- ing, it is rather complicated for project managers. Additionally, due to big data characteristics, difficulties also arise for project managers when they want to make sense of the data and use the technologies they often tell us that they feel a data scientist is required to extract insights from the data. This leads us to formulate the following proposition.
Proposition 3: Organizational factors, such as work effort in project management, project-oriented organization structure, training for big data technologies, and having a data scientist, will impact the adoption and the use of big data in project management.
Project-Level Factors:While our literature review showed that the application of big data tools takes place in different project life cycle phases (see Table A in the Appendix), these studies lacked a complete view as they analyze big data adoption in a single phase with a single case study. Our research covered different industries and did not focus on one phase as a starting point. We observed that these tools are more adopted in phases characterized by the large amount and diverse types of data that need to be processed/analyzed to support the decision making at the initiation/planning/execution of projects. This should be further investigated if the big data adoption takes place in the
Fig. 1. Conceptual model with propositions.
first phases of the project than in the later ones. Additionally, factors that add to the complexity of a project, such as the size of the projects, their number of activities, and their number (portfolio) [7], may have an impact on the adoption of big data in project management. This leads us to formulate the following proposition.
Proposition 4: Project-related factors, such as the size of the projects, the number of activities, and the number of projects, will moderate the relationship between TOE factors and the adoption and use of big data in project management.
Besides the previously mentioned challenges, motives, and impact of big data adoption on project management, we also observed individual-level challenges, motives, and impact. Even though organizations’ managers are the ones who decide to adopt big data, individuals, such as project managers, also face changes in their roles and adoption of new technologies. The lack of support from the organization in terms of training and resources creates resistance to change. Studies that focus on the big data adoption conducted the analyses at the organizational level (e.g., [14] and [70]), ignoring the individual factors. However, an indi- vidual level of adoption is necessary to understand a successful implementation within an organization. Previous studies showed that the project manager’s previous experience, acceptance of technology, and perception of its benefits are important factors in adoption [9], [31]. This leads us to formulate the following proposition.
Proposition 5: Project manager characteristics will moderate the relationship between the TOE factors and the adoption and use of big data in project management.
The findings from our study resulted in five testable proposi- tions regarding the factors that influence the adoption of big data solutions in the project management. Fig. 1 presents our model with these propositions.
Managerial Implications:One of the objectives of this article was to advise project managers in their adoption of big data tools.
In the following framework, we distinguish among four main
types of big data adopters according to their big data adoption and of conviction level of their potential for their businesses.
1) Adopters:These companies have already been using big data tools to manage their projects, and they are convinced of their benefits. This was not the case for many inter- viewed project managers. The main challenge that these companies have to deal with is the need to scale up the existing tools in order to reduce the cost.
2) Testers:These companies started to test the big data tools at one phase of their projects but are still not convinced of their benefits. These companies have two choices either to show more commitment following the promising results of the testing phase or to get back to their skeptical position.
A good number of our interviewees could be considered testers. The same could be observed in previous studies.
3) Laggards:These companies are convinced of the big data benefits, but they lack the adoption of these tools. This level of conviction could be related to either a need that emerges or to a competitive race. Still, the problem is the lack of adoption that could be related to the absence of the decision to commit resources to these tools. When the decision to commit to these tools is taken, these companies will become the adopters of these tools.
4) Skeptical: These companies lack both conviction and adoption levels. These companies do not see the need for big data tools either because they are in industries where such tools are not adopted or because of their small size.
Sometimes this could be because of the managers’ myopic vision. Either these companies will stay as they are or they could transform into laggards or testers of big data tools.
Limitations and Future Studies:While this article was instru- mental for the investigation into big data adoption on project management, it focuses on a limited number of project man- agers. We only studied the practices in France, mostly in Paris.
The results cannot be generalized to other regions or countries.
However, it could give insights into industries and trends. Future
studies can replicate this study with a more representative sample and examine different cases in various countries. An application of longitudinal analysis could also provide more insights into the adoption process since it takes a couple of years to implement it thoroughly. Another research direction could be to focus on a giver sector, such as construction and analyze the big data implementation dynamics of this sector. In this research, a case study methodology could be adopted rather than conducting in- terviews with project managers that work in various companies.
The impacts of using big data tools in project management deci- sion making within separate organizations could be compared.
Besides big data analytics, organizations can use AI to access insights. The rise of big data has empowered AI, which refers to computers’ ability to perform cognitive functions and simulate human behaviors [80]. Thus, the processing and analysis of big data can also be done through AI. Even some cloud-based big data platforms (e.g., Skywise) use both big data analytics and AI tools. Thus, future studies could further distinguish between AI tools and big data technologies and identify their role in project management. This study is a first step that will create a basis for future research on big data adoption in project management. It contributes by providing insights to researchers and practitioners to discover how big data analytics support and will support project management.
APPENDIX TABLE A
LITERATURERELATED TOBIGDATA INPROJECTMANAGEMENT
ACKNOWLEDGMENT
The authors would like to thank Eva Tesch for her support in the data collection process.
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