Design of a Process Mining Alignment Method for Building Big Data Analytics Capabilities

Pfahlsberger, Lukas; Mendling, Jan; Eckhardt, Andreas

doi:10.24251/hicss.2021.688

Cited by 3 publications

(7 citation statements)

References 34 publications

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“…By analyzing structured and unstructured data, such as historical records, sensor reports, real-time data, and client feedback, patterns, trends, and correlations can be identified that provide a comprehensive understanding of project status and performance. These results are presented in a manner consistent with those presented by several authors, including [89,90,93]. Another aspect to consider is that big data require a significant amount of care in the handling of information, which is primarily associated with cybersecurity.…”

Section: Discussionsupporting

confidence: 88%

“…[54,71,78] The use of these technologies to integrate data from multiple sources and facilitate project and team management is also evidenced. Finally, the use of artificial intelligence as a way to simulate and review coordination scenarios and thus evaluate their impact on the project emerged [88,89,92,93].…”

Section: Question 2-what Are the Most-used Methodological Routes And ...mentioning

confidence: 99%

“…Cost control, resource optimization, projection based on synchronized information, and contract management in the AEC projects are also processes that can be optimized using big data [75]. A method for data mining and resource management in project management, based on big data, is proposed as a strategy to optimize organizational processes [93].…”

Section: Question 1-what Are the Technologies Most Used In The Articl...mentioning

confidence: 99%

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Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review

Zabala-Vargas,

Jaimes-Quintanilla,

Jimenez-Barrera

2023

Buildings

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The high volume of information produced by project management and its quality have become a challenge for organizations. Due to this, emerging technologies such as big data, data science and artificial intelligence (ETs) have become an alternative in the project life cycle. This article aims to present a systematic review of the literature on the use of these technologies in the architecture, engineering, and construction industry. A methodology of collection, purification, evaluation, bibliometric, and categorical analysis was used. A total of 224 articles were found, which, using the PRISMA method, finally generated 57 articles. The categorical analysis focused on determining the technologies used, the most common methodologies, the most-discussed project management areas, and the contributions to the AEC industry. The review found that there is international leadership by China, the United States, and the United Kingdom. The type of research most used is quantitative. The areas of knowledge where ETs are most used are Cost, Quality, Time, and Scope. Finally, among the most outstanding contributions are as follows: prediction in the development of projects, the identification of critical factors, the detailed identification of risks, the optimization of planning, the automation of tasks, and the increase in efficiency; all of these to facilitate management decision making.

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Section: Discussionsupporting

confidence: 88%

Section: Question 2-what Are the Most-used Methodological Routes And ...mentioning

confidence: 99%

Section: Question 1-what Are the Technologies Most Used In The Articl...mentioning

confidence: 99%

See 1 more Smart Citation

Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review

Zabala-Vargas,

Jaimes-Quintanilla,

Jimenez-Barrera

2023

Buildings

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“…(1) Internal – reason for a process improvement is business perspective-driven, such as process improvement, or redesign; and (2) external – change becomes necessary due to legal requirements, strategic business adjustments or other external factors. We saw similar findings in an approach of Pfahlsberger et al. (2021) who discuss the realignment of certain IT resources in relation to process mining based on the external or social context in which the business process is embedded.…”

Section: Discussionsupporting

confidence: 65%

Enhancing decision-making of IT demand management with process mining

Novak,

Pfahlsberger,

Bala

et al. 2023

BPMJ

Self Cite

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PurposeDigitalization, innovation and changing customer requirements drive the continuous improvement of an organization's business processes. IT demand management (ITDM) as a methodology supports the holistic governance of IT and the corresponding business process change (BPC), by allocating resources to meet a company's requirements and strategic objectives. As ITDM decision-makers are not fully aware of how the as-is business processes operate and interact, making informed decisions that positively impact the to-be process is a key challenge.Design/methodology/approachIn this paper, the authors address this challenge by developing a novel approach that integrates process mining and ITDM. To this end, the authors conduct an action research study where the researchers participated in the design, creation and evaluation of the approach. The proposed approach is illustrated using two sample demands of an insurance claims process. These demands are used to construct the artefact in multiple research circles and to validate the approach in practice. The authors applied learning and reflection methods for incrementally adjusting this study’s approach.FindingsThe study shows that the utilization of process mining activities during process changes on an operational level contributes to (1) increasing accuracy and efficiency of ITDM; (2) timely identification of potential risks and dependencies and (3) support of testing and acceptance of IT demands.Originality/valueThe implementation of this study’s approach improved ITDM practice. It appropriately addressed the information needs of decision-makers and unveiled the effects and consequences of process changes. Furthermore, providing a clearer picture of the process dependencies clarified the responsibilities and the interfaces at the intra- and inter-process level.

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“…Corporate governance refers to the processes and structures that control and direct an organization and the accompanying rules that govern the relationships between the management, owners, and stakeholders (Abdullah & Valentine, 2009;Pfahlsberger & Mendling, 2021). The passage of the 2002 Sarbanes-Oxley Act caused a re-evaluation of existing CG and the adoption of more stringent and transparent architectures (Lankhost, 2009).…”

Section: Defining Dgmentioning

confidence: 99%

Data Governance. Enablers, Inhibitors, Practices, and Outcomes

Henninger

2024

Preprint

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<p>Data governance (DG) is a framework to manage data as a strategic enterprise asset. It is used to define and communicate an organization's accountability for data as well as decision rights, policies, standards, procedures, and compliance. As an emerging research topic, there is limited empirical research and theory development. Accordingly, this thesis builds upon Tallon, Ramirez, and Short's (2013) formative qualitative research that proposed the Theory of Information Governance (TIG). This thesis extends the TIG to develop the Refined Theory of Data/Information Governance, theorizes and tests hypotheses, and develops five lower-order constructs and two higher-order constructs. Exploratory factor analysis, confirmatory composite analysis, and structural equation modeling are used on survey data (N = 126, N = 227) from two separate groups of financial professionals. The findings quantitatively ascertain the composition of DG, offer a matrix of DG enablers, and identify DG as a source of competitive advantage and increased performance.</p>

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Design of a Process Mining Alignment Method for Building Big Data Analytics Capabilities

Cited by 3 publications

References 34 publications

Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review

Big Data, Data Science, and Artificial Intelligence for Project Management in the Architecture, Engineering, and Construction Industry: A Systematic Review

Enhancing decision-making of IT demand management with process mining

Data Governance. Enablers, Inhibitors, Practices, and Outcomes

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