Risk Identification of Data Science Projects: A Literature Review

Abstract

While cost, time, and resources are considered to have a high impact on data science projects, risks are the key for the successful implementation of a project. The correct handling of risks has been proven to increase a project's chances of success. Therefore, awareness of existing and emerging risks as well as their assessment play a major role in data science project management. In 2021, 87 percent of data science projects fail and are thus not implemented successfully. The path of successful implementation of data science projects in companies is more complex and uncertain than for conventional projects, in particular the identification and availability of the necessary skills in a company before and during the project. Regarding software engineering projects, the analysis and evaluation of potential risks is well-known, but for data science projects potential risks have not yet been analyzed at a larger scale. To identify the potential reasons for failure of data science projects, an in-depth understanding of potential risks and measures for mitigation these risks is inevitable. In this paper, we conduct a systematic literature review on risks of data science projects and present the top fiteen risks as first findings. Furthermore, we compare the identified risks to the major risks of software engineering projects to highlight similarities and diferences between these two disciplines. The findings of our literature review can be used in guiding the development of future risk assessment systems for complex data science projects.

1. Introduction

Data science projects have become an integral part of today’s companies. Leveraging data has proven essential in the ongoing competition for market share and competitiveness [ 38 ]. Instead of the service or product itself, the collected data is often considered as valuable asset, which can be used for developing innovative business models such as ”Smart Services” [30]. In a representative study conducted by MIT [ 35 ] of 2602 managers, executives, and data professionals, it has been demonstrated that companies were able to gain a competitive advantage, optimize existing processes and increase in the development of innovative business models through the use of data and analytics. In addition, the study also reveals that these companies were able to supplement human skills through the use of smart machines, thus reducing time-consuming tasks [ 35 ]. However, the path to successful implementation of data science projects in companies is more complex and uncertain than for conventional projects, ”a multidimensional challenge requiring specialised tools, processes and methodologies” [26], in particular the identification and availability of the necessary skills in a company before and during the project [ 11 ]. In 2013, ”55 percent of big data projects don’t get completed, and many more fail their objectives” [19]. In 2015, Gartner [ 14 ] states that ”through 2017, 60 percent of big data projects will fail to go beyond piloting and experimentation, and will be abandoned”. In 2021, 87 percent of data science projects fail and do not get deployed [53]. To address this challenge, data science process models [18] have been developed to assist during the realization of a data science project. While process models in software projects are commonly used and well-evolved over time, they gain in importance for data science [26]. A comparison between seven data science process models shows, that four of seven highlight aspects of project management [26], but pre- and post related project tasks such as risk management are poorly represented.

Risk management can not only help to create an awareness for existing risks at the beginning of a data science project, but also to make the project team sensitive to emerging risks during the project duration. In this way, challenges can be recognized and addressed at an early stage. Therefore, the first step in risk management is the identification of risks to assure the project success [ 47 ]. For software engineering projects, several risk factors have already been identified. Keil et al. [23] published a framework for identifying software project risks and in the last twenty years, critical factors and a top ten risk lists for software project failure have been published [ 1, 4, 13, 15, 24, 44 ]. Compared to software development, data science is still quite young and has only gained importance in recent years [ 36 ], so that there is considerably less literature available to date in the area of risk management and project risks, which focus on data science-specific aspects such as cross-disciplinary competences of team members.

This paper aims to investigate the risks of data science projects. First, a literature review on risks of data science projects will be performed and second, the identified risks will be categorized. Therefore, the literature review aims to answer the following research question (RQ1): What are the major risks of data science projects? The top fiteen identified risks will be then described and compared to the most common risks of software engineering projects, which leads to the second research question (RQ2): What are the similarities and diferences between the risks of a software engineeering project and those of a data science project?

The paper is structured as follows: First, the background is described in Section 2. Second, the methodology of the data collection is described in Section 3. Third, the findings are presented and discussed in Section 4. Finally, the future work as well as the limitations are given in Section 5.

2. Background

Based on Muhlbauer, risk can be defined as result in potential failure or loss [ 32 ]. In the context of a project, risk can be defined as an event that is uncertain with negative impact or positive objectives in case of occurrence [ 33 ]. This led to the standard definition of risk. According to standard ISO 31000:2018, risk is defined as the ”efect of uncertainty on objectives” [ 21]. An efect is described as deviation from the expected and can be positive, negative, or both, and can address, create, or result in opportunities and threats [21]. Objectives can have diferent aspects and categories and can be applied at various levels [21]. Regarding risk, important terms are sources, events, consequences and likelihood [20]. Risk sources identify where risks can originate, risk events denote the concrete realization of a risk, risk consequence implicates the potential outcome of the risk event and risk likelihood is a qualitative assessment that describes how likely a risk will occur. The related term risk management then explains the coordination and controlling of risk activities within the organizations, which includes risk assessment, risk treatment, risk acceptance and risk communications. [20, 21, 22]

Risk assessment is the overall process of risk identification, risk analysis, and risk evaluation. The purpose of risk identification is to find, recognize, and describe risks that prevent or help an organization from achieving its objectives. By identifying risks, the fundamental questions are what, where, when, why, and how a potential risk could happen and then categorizing risk elements [21]. According to the same standard, risk analysis is a systematic process for understanding risk and its characteristics. It involves a detailed view of uncertainties, sources of risk, consequences, probability, events, scenarios, controls and their efectiveness. An event can have multiple causes and consequences, which in turn afect multiple objectives. In terms of analysis techniques, these can be qualitative, quantitative in nature, or a combination of both. Risk analysis provides input to risk assessment and decisions about whether and how a risk should be treated. Risk assessment involves the comparison of the results of the risk analysis with the established risk criteria to determine where additional measures are required [20, 21]. According to Grassi et al. [17], risk evaluation is the most important task in risk assessment.

Although risk assessment is an important part of software project management, it is still at its infancy in data science projects and thus highly underestimated and often not performed at all. As Kutzias et al. [26] demonstrated, recent data science process models such as CRISPDM or DASC-PM emphasize project management, but neglect pre- and post project-specific tasks, which also include risk assessment. In software projects, many risks occur during the process of creating the software, where the risk may lie, for example, in understanding the requirements, integrating modules or feasibility [ 37 ]. Risk identification in the design phase is crucial because ”if senior manager fail to detect such risks, it is possible that such projects may collapse completely” [31]. To create awareness of risk assessment within data science projects, this paper focuses on identifying potential risks by means of a literature review. The methodology is described in the following section.

3. Research Method

Based on the recommendations of Webster and Watson [52], a literature review is conducted, including keyword and backward search. According to the procedure of vom Brocke et al. [49], the process is documented. The procedure of the literature review process is summarized in Figure 1.

The keyword investigation is focused on risks of data science projects. In the first step, the scientific literature databases Scopus, IEEE Explore, EBSCOhost and ACM Digital Library were searched by defined search queries (cf. Table 1).

As a first result, 354 papers were found. In a second step, the duplicates were removed, so that 314 paper remained. In a third step, the abstracts of these remained paper were evaluated according to their content, leading to 57 paper which were considered to be relevant. In a fourth step, the entire content of these papers was evaluated by extracting named risks. As a result from the keyword search 24 papers (cf. Table 2) and the therein mentioned 143 risks were considered relevant for a further backward search. In a fith step, the references of the 24 relevant papers from the keyword search were searched by further papers regarding risks of data science projects. As a result from the backward search, 16 papers (cf. Table 3) were added to the total amount of relevant papers as well as additional 105 identified risks. As a final result, a total of 248 risks of data science projects were examined by the aforementioned literature review process.

Title Towards an Improved ASUM-DM Process Methodology for Cross-Disciplinary Multi-organization Big Data and Analytics Projects The Age of Data: What You Need to Know About Fundamentals, Algorithms, and Applications Achieving Agile Big Data Science: The Evolution of a team’s Agile Process Methodology.

Reference Lahiri and Saltz Aho et al.

Martinez et al.

Saltz Aßmann Lai and Leu Haug Barham and Daim Ajah and Nweke Malik and Singh Abdullah Jothi et al.

Saltz and Lahiri Jin et al.

Koronios et al.

Preimesberger Connolly 2021 2021 2022 2023 2017 2016 2018 2019 2020 2018 2016 2020 2015 2014 2019 2015 keyword keyword keyword keyword keyword backward backward backward backward backward

4. Results

As described in the previous section, a total of 248 risks emerging in data science projects have been identified through our literature review process. Since these risks can have diferent wordings depending on the literature source, the next step is to classify the risks into categories. As a result, we obtain 29 risk categories of varying frequency. These categories are summarized in Table 4.

As can be seen in the table above, ”Insuficient project management” is the risk category comprising the most frequently mentioned risks. This category includes risks such as ”poor task communication”, ”poor time management”, ”lack on focus on process and team coordination” or ”plan cost overrun and schedule delay”. In literature, these project management risks dominate over technical issues. Saltz et al. [ 40 ] criticizes for example that ”data science projects need to focus on people, process and technology” and that in most cases immature processes risk are among others responsible for a project failure [ 42 ]. The risk category with the second most frequently mentioned risks is ”Data security and privacy”, which includes security concerns such as ”cyber attacks” or ”data privacy” concerns. The third risk category, named ”Poor data availability, quality, and timeliness”, includes risks such as ”bad data quality”, ”broken data”, ”limited data access” or ”timeliness of data”. In literature, data quality issues are mentioned in a manifold way: in case of data cleansing, impact on model accuracy or high complex but faulty data sets [ 46 ]. Risks, which are categorized in ”Lack of data science competence/skills”, were also named quite often and reflect the challenge for companies to find skilled data scientists, data analysts or machine learning engineers on the labor market to handle successful data science projects [ 12, 48 ]. Our findings are summarized in Figure 2, answering the first research question (RQ1).

With regard to the second research question (RQ2), software engineering is concerned with the development of software and thus, a software engineering project includes the design, implementation and testing of the software. In addition, the planning of a software system, the requirement analysis and the maintenance are added to the design process [ 8, 51 ]. To structure a software engineering project, process models such as the spiral model are used, which is a risk-driven procedure model for software development and follows the principle of the repeated run of its partial steps: description of the basic conditions with definition of the objectives, evaluation of the identified alternative solutions to mitigate or avoid any risks, development and reflection of an intermediate product and planning of the next iteration [ 51].Despite this close-meshed approach, not all risks can be reduced.

The top ten risks in software engineering projects are insuficient requirement management, lack of management commitment and lack of project management methodology [ 1, 4, 13, 15, 16, 24, 44 ]. Ghazali et al. [16] conducted a literature review, which categorized the identified risks in ”management risks”, ”people risks” and ”technology risks” and conclude that management risks are the highest risks compared to people and technology risks. These management risks include, for example, ”project milestones undefined”, ”requirements change”, ”lack of agile progress tracking mechanism” as well as ”lack of resources”, ”failure to manage end-user expectation” or ”lack of management commitment” [16]. With regard to data science project risks, the insuficient project management, which is comparable to Ghazali’s management category, is also the highest risk to occur. We can conclude that the risk of insuficient project management techniques on software engineering projects as well as on data science projects is high and should not be underestimated.

Regarding Ghazali’s category ”people risks”, risks such as ”lack of necessary skill-set”, ”experience and training problem”, ”lack of team work” or ”unmotivated team member” address the risk of an insuficient team management, which is also a common problem of data science projects [16]. In addition, the goal of a software engineering project is to develop a product that is useful to the end-user and if the end-user has dificulties using the final product, then it’s a considerable risk. Therefore, frequent testing is vital for software engineering projects. Compared to data science projects, a successful customer expectation management is also crucial for the project outcome.

The last category of Ghazali’s literature review, ”technical risks”, includes risks such as ”lack of key technology”, ”inappropriateness of technology and tools” or ”processor management insuficient”, which appear less frequent in data science projects [ 16]. Compared to Ghazali’s ”technical risks”, the risk of ”Poor data availability, quality, and timeliness” is one of the highest risks to occur in data science projects and can contribute significantly to the failure of the project.

5. Conclusion

This paper reports the results of a literature review on risks of data science projects. Through this literature analysis, relevant sources were collected and potential risks identified and categorized (RQ1). At the end, 354 sources were found, whereas in 40 papers 248 relevant risks were documented. After cleaning the results, 248 risks were categorized by main term and the most frequently mentioned risk categories were presented. The results show the need for a more detailed risk assessment to assist the project manager during the project duration. Risks, which are summarized by the term ”insuficient project management”, can be addressed through a frequent risk sensitivity analysis to highlight, for example, upcoming time schedule challenges right at the beginning to avoid project failure. Risks, such as ”data security and privacy” risks, as well as ”poor data availability, quality, and timeliness”, which efect the project outcome should be aware as early as possible in the project. Therefore, a risk assessment, which evaluates the identified risks of a data science project at the beginning and during the project period is vital.

Furthermore, the similarities and diferences between the risks of a software engineering project and those of a data science project (RQ2) have been described. The comparison between the risks of a software engineering project and those of a data science project show that there are similarities between both disciplines, especially regarding the risks of a insuficient project and team management. Regarding the technical risks, data science projects have a particularly high risk of failure if, for example, the data is not available or of poor quality, while software engineering projects fail less frequent due to technical risks.

The limitations of this literature analysis lie on the one hand in the definition of the search queries and on the other hand in the naming and assignment of risks to the individual categories, since these are always shaped by subjective criteria such as personal level of knowledge and experience.

Regarding the risk assessment, the first step of risk identification was successfully performed in this paper. As an outlook to future work, these categorized risks form the basis for the development of a method for automated risk assessment of data science projects. Among others, discrete multi-attribute decision-making (MADM) methods and conitnuous multi-objective decision-making (MODM) methods [ 10 ], are considered for this purpose.

6. Acknowledgement

Parts of this paper were conducted as part of the DS3W project at the Research Center Work – Education – Digitalization. This project is funded by the Ministry of Culture and Science of North Rhine-Westphalia, Germany.

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