project management office (PMO) performance: The predictive analytics approach
FEATURED PAPER
By Dr Lalamani Budeli
South Africa
Abstract
Due to the amount of resources organizations invest in projects and programs, there is a growing emphasis on benefits management as a powerful tool to align projects, programs, and portfolios to the organization’s strategy. To boost performance, the organization often undergo business model restructuring as a results of constant environmental changes and competitive globalized markets. Project management office (PMO) and their performance in relation to improve organizational performance is important to achieve strategic goals and increase value of projects in organizations. Project-based information through analytics can permit project managers and executives to measure, observe, and analyze project performance objectively and make decisions and commitments based on facts.
In the 21st century , the high availability of analytical technology can enable project and program managers to use various analytical reports and drill-down charts to break down complex project data and predict their behavior and outcomes in real-time. The objective of this research is to investigate the application of data analytics , tools and technieques to improve project management office performance ensuring that the organisations achive its desired benefits that are mostly absent in today projects and programs. With the huge amount of data available , ensuing requirements for Artificial Intelligence and good machine learning techniques, new problems arise and novel approaches to feature engineering techniques are in demand.
Keywords: Data analytics, Project management office (PMO), Benefits realization
Introduction
According to Russom (2011-7), a data-driven analytics approach enables project teams to analyze the defined data to understand specific patterns and trends which can be used by executives , for analysis to determine how projects and resources perform and what strategic decisions they can take to improve the project and programs success rate. According to Zikopoulos and Eaton (2011-21), an effective project management involves operative management of uncertainty on the project which requires the project managers to use analytical techniques to monitor and control the uncertainty as well as to estimate project schedule and cost more accurately with analytics-driven prediction. Kambatla, Kollias, Kumar and Grama (2014-2562) said that analytics-based project metrics can essentially enable the project managers to measure, observe, and analyze project performance objectively. Tsai, Lai, Chao and Vasilakos (2015-22) said that analytics enables projects teams to analyze the captured data to understand certain patterns or trends. Effective management of projects necessitates efficient management of the uncertainties and risks on the project which requires today’s project managers to use analytical techniques to monitor and control the risks as well as to estimate project schedules and costs more accurately with analytics-driven prediction.
Research method
Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. The key characteristics to look for are specific research questions to be answered,definition of the population, behavior, or phenomena being studied, description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments. The figure below shows the empirical research method followed in this study.
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How to cite this paper: Budeli, L. (2021). Data analytics to improve engineering project management office (PMO) performance: The predictive analytics approach. PM World Journal, Vol. X, Issue III, March. Available online at https://pmworldlibrary.net/wp-content/uploads/2021/03/pmwj103-Mar2021-Budeli-data-analytics-to-improve-engineering-pmo-performance.pdf
About the Author
Dr Lalamani Budeli
South Africa
Dr Lalamani Budeli obtained his degree in Electrical Engineering at the Vaal University (VUT), BSc honors in Engineering Technology Management at University of Pretoria (UP), Master in engineering development and Management at North West University (NWU), Master of business administration at Regent Business School (RBS) and a Doctor of Philosophy in Engineering Development and Management at North West University (NWU), Potchefstroom, South Africa. Currently, he is a managing director of BLIT, an engineering, research, and project management company based in South Africa.
His research interests include project portfolio management, agile project management, plant life cycle management, advanced systems analytics, project early warning system, and the use of artificial intelligence in project management. Currently, he is spending most of the time on research that is looking at the development of system and application that uses the latest technology like block chain, internet of things (IoT), Big data, and artificial intelligence. Lalamani Budeli can be contacted at budelil@blit.co.za.
To view other works by Dr. Budeli, visit his author showcase in the PM World Library at https://pmworldlibrary.net/authors/lalamani-budeli/
