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The digital era has been characterized by the transformation of project management through new tools and processes that are more effective, collaborative, and enable quicker decision-making. Once an organization enters the age of digitalization, its previously used methods of project management are adapted to incorporate technology. This paper focuses on investigating the effect of digital transformation on project management in terms of active practices and embedded processes.
Automation and Efficiency
One of the areas where digital transformation impacts project management policies and practices is through automated processes. Scheduling, task assignment, progress tracking, and reporting are examples of processes that can be automated using such tools. As a result, tedious manual efforts are alleviated, errors are reduced, and teams can focus on high-value activities. Some of these activities include:
Automatic Task Assignment: Project management systems like Asana and JIRA can execute pre-configured rules to automatically allocate tasks to team members once certain conditions are met. For example, in Asana, a task can be automatically assigned to the team member closest to the deadline, though assigning tasks based on the exact availability of team members is not part of the standard functionality; it relies more on user-defined rules.
Progress Reporting: Applications such as Monday.com offer dashboards that are updated in real-time and display information about tasks and projects in their current state without needing manual updates. This facilitates timely and accurate progress monitoring.
Reporting: Smartsheet, for instance, allows for the setup of recurring reports concerning the current progress of a project. The relevant documents can be preset and automatically sent to interested parties without requiring manual collection.
Data and Advanced Analytics
The digitalization process has not only resulted in significant changes in organizational structure but also enhanced capabilities for acquiring and utilizing data for analysis, which improves decision-making in project management.
Predictive Analytics: Tools such as Microsoft Power BI and Tableau help project managers analyze past data to avoid potential issues and plan ahead. For example, data can reveal trends in delays for certain projects.
Interactive Dashboards: Tableau offers interactive dashboards that enable managers to describe current project performance with the goal of identifying process bottlenecks and pinpointing problem areas needing improvement.
Trends: Analytical tools, such as Qlik Sense, can infer project trends and overall status, allowing for necessary changes to enhance effectiveness and prevent losses.
Collaboration and Communication
With the advent of various tools, team members can now work collaboratively in real time, regardless of their physical location. Communication tools such as Slack, Microsoft Teams, and Trello have facilitated seamless interaction and teamwork in organizational task management, enhancing coordination and productivity.
The digital age is reshaping the project management landscape, influencing both project managers and their projects. New tools and methods improve performance, cooperation, and decision-making among all project coordinators. Benefits reshaping project management include process automation, adoption of advanced analytical techniques, and the use of collaborative platforms. Embracing these technologies is crucial for organizations to remain relevant and successful in an ever-evolving business landscape.
References:
Kozarkiewicz, A. (2020). General and specific: The impact of digital transformation on project processes and management methods. Foundations of Management, 12(1), 237-248. https://doi.org/10.2478/fman-2020-0018
Hassani, R., El Bouzekri El Idrissi, Y., & Abouabdellah, A. (2018). Digital project management in the era of digital transformation: Hybrid method. In Proceedings of the 2018 International Conference on Software Engineering and Information Management (ICSIM '18) (pp. 98–103). Association for Computing Machinery. https://doi.org/10.1145/3178461.3178472
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