IPMA International Project Management Association
18 October 2017 / 5:30

Project Eco-system: Designing future developments through Big Data analytics (Part B)

The fast pace of technological and business dynamics require organizations to develop what can be called TALK capabilities (T= Technology readiness, A=Awareness, L=Leadership competence, K =Knowledge). Data analytics and associated technologies are expected to play a key role in helping organizations doing exactly that, Talk.

In a project-savvy business world, achieving project management efficiencies through use of Big Data analytics could be a game-changer. In an earlier article, we explained how Big Data analytics and associated technologies are expected to shape the future of the delivery part of project management and the science behind it. The next question is now – what role does Big Data have in shaping the future developments of broader project eco-systems.

This is because projects are not isolated beings that exist in a vacuum. They breed and live in an eco-system with their delivery affected by the dynamics of that system. This means that an understanding of the enablers of a project eco-system is critical to shape the ultimate aim of improved project delivery outcome. Big Data analytics could help achieve that.

We define a project eco-system as “an envelope of conjoined enablers surrounding a project including: a variety of actors (e.g. internal and external stakeholders), resources (human, physical, communication, information technology and financial), culture (e.g. organizational, industry, and national culture), regulations (e.g., national/state/local government policies and regulations), industry or economic sector standard requirements and guidelines, political dispensations (local, regional and national), pressure groups and non-governmental organizations, professional bodies, geographical location and natural climate”. All these dynamically interact to define the outcome of project delivery.

The large number of enablers that make-up a project eco-system offers a fertile ground for collection of large amounts of assorted data. It is therefore reasonable to suggest that Big Data analytics can shape the future developments of project eco-systems through analysis of characteristics of the systems and identification of triggers that influence improvement in project delivery outcomes. We discuss the possible developments across six eco-system enablers below:

Eco-system enabler 1: Refinement of current – and creation of new – standards

One of the key contributions of Big Data technologies will be to analyse data on the use of project management methodologies, frameworks and systems, in order to develop new standards or refine existing ones. Organisations use either bespoke or tailored methodologies, or ‘as-is’ or modified versions of established standards. The use of standards involves adopting processes, tools and techniques defined in these standards, whether these are tailored ones or established ones.

As part of organizational efforts and project lessons learned, organizations can document events as they unfold. The documentation of the project-related matters can vary from thin to heavy documentation, depending on the project management maturity levels. If an organizations is large, then a lot of data could be available to analysis. If not, then industry-based data can be a good source from which to start understanding the use patterns, issues, challenges, and benefits/disadvantages of using current project management standards, methodologies and frameworks. Such an analysis then can prove to be a basis for refining the existing standards or developing new standards.

Given the current state of standards, which still effectively date from the mid-20th century, it seems vital that Big Data analytics is used to freshen up and redefine project management to meet the demands of overall eco-system developments.

Eco-system enabler 2: Development of new and enhancements in existing processes, tools and techniques

The current processes and techniques drawn from best practices to some extent lack flexibility and lead to adoption of a set-mould approach to project work. It is important that organizations using the set of processes, tools and techniques are able to tailor them to their environment for the maximum benefits.  To this end, using Big Data analytics can help in providing a starting point to discover:

  1. How effective are the current set of processes, tools and techniques?
  2. How easy (or difficult) it is to tailor these to a project context (across assorted industries)?
  3. How effective are these in their application towards successful delivery of projects?
  4. What type and nature of new processes, tools and techniques could be added to the existing pool of processes, tools and techniques?
  5. What knowledge areas can be further added to enhance the existing set of knowledge areas?

Similar to discussed above, the project data available at an organization or industry level can be used for analytics purposes. Where, data is not readily available, technology can be used to collect the data over a period of time and analyse it using Big Data analytics.

Eco-system enabler 3: Improvements in use of technology

Big Data analytics can be very useful in enhancing current set of available project management technologies and development of new applications for improved project management capabilities. Social media technologies can be used to enhance existing project management technologies. Other areas where new technological developments are needed iinclude project dashboards, virtual project environments, 3-D project management training applications, and mock-scenario project management applications to prepare organizations to deal with disaster recovery, business continuity, aid and relief projects, and emergency situation projects, to name a few.

Considerable data is available at organization or industry levels on technologies being used to do Big Data analytics. Where it is not available, the data on usefulness and areas of improvements in relation to the current set of project management software can be captured over a period of time and analysed using BD analytics.

Eco-system enabler 4: Improvements in Skills Inventory

Projects are knowledge-intensive entities. With the fast pace of technological developments, project management will become more and more knowledge-critical. To prepare for such a scenario, organizations need to develop skills inventories to be able to cope with traditional to complex, easy to complicated, and face-to-face to virtual projects. It would not be an exaggeration to say that skills inventories development has not received its due attention as organizations tend to focus on bottom line, thinking training and development is an ad-hoc cost that reduces profit margins.

Certainly, considerable project-based data is available on people and their skills. That data can be put to Big Data analytics to find out answers to some of the following questions:

  1. What are the most often used skills in project management?
  2. What are the areas where there is a lack of skills availability?
  3. What new skills sets are needed for managing projects of future?
  4. What kind of skills developments occur during project executions?
  5. What are the industry-specific skills inventories needed for management of projects?

Eco-system enabler 5: Improvements in benefit realization

Benefit realization should be the core of project management activities, however, it has not received the deserved attention. Given the nature of tangible and intangible benefits, once again considerable data is available at the organization level on how much an organization has invested in projects and what benefits they expect. In some cases, data will also be available to link project investments to benefit realization.

Big Data analytics can be used to analyse data on project investments, planned benefits, and benefit realization to help further enhance knowledge and develop benefit realisation standards and processes.

Eco-system enabler 6: Improvements in knowledge management

Project knowledge management is becoming the focus of attention in recent years. However, we do lack specifics on project knowledge management, the knowledge bandwidth of project teams and how to measure and enhance knowledge bandwidths of project teams. To ensure that project management is attractive enough for the younger generation to adopt as a profession, it is important to initiate development of new-age project management.

Typically, project management activities are well documented in the form of issues, risks, change management and lessons learned. Such data available at organization and industry level can be analysed using Big Data analytics to enhance knowledge management standards.

Concluding thoughts:

It goes without saying that Big Data capture and analytics is already a reality. The extraordinary presence of project based activities in every sector of an economy naturally creates opportunities to capture data and use benefits of Big Data technologies to integrate innovations and new ideas into the project management profession. The time is ripe for the project management profession to come out of its mould and evolve.


Special thanks to post-write-up inputs by Roger Tagg.


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Author of this post

Jiwat is a Professor in Project Management. He has considerable experience of working internationally in diverse cultures and business environments such as Hong Kong, China, Singapore, and Australia, among others. Over his career, he has provided leadership in establishing, designing, and delivering Executive education / Master’s, Training, and Research programs.

Jiwat is currently serving on the Editorial Board of International Journal of Project Management.

Jiwat actively contributes to project management community by speaking at various events and writing on emerging issues. His work has been published in top scientific journals and Four of his published papers have remained in Top25 most downloaded papers. Additionally, two of his papers have been ranked as the Most Cited article published since 2012, one in the International Journal of Production Economics and the other in Journal of Engineering and Technology Management. More recently, he has published a number of articles on some of the issues confronting project management in various industry based outlets.