Chapter 2 discusses numeric models and I will have to be the first to confess they seem to be viable options to selecting a project. ?However, there are?some risks associated wit

Big Data revolution is at our door steps and expected to drive ‘Big Changes’ in the way businesses and societies go about their day-to-day chores. From health, education, finance, technology to defense, to name a few, no single sector of economy is spared from Big Data analytics and its implications. These implications, if exploited in a right manner, can bring about far reaching changes for improved decision making, customer experience improvements, profitability and overall economic development of societies.

Similar to across-the-board implications of Big Data analytics, project management (PM) knowledge and skills carry wide-scale influence as PM is used in almost every industry and economic sector. This commonality of pervasiveness raises the questions ‘how Big Data can help shape future of project management?’ and / or conversely ‘how project management can help in further developments in Big Data technologies?’ For the sake of parsimony of thoughts, let me address the former question in this article first.

How Big Data can help shape future of project management?

The volume, value, variety, veracity and velocity (commonly known as 5 Vs of Big Data) with which multi-layered data about different aspects of business and human lives are being collected on an ongoing basis offer significant opportunities for making use of data for the benefit of businesses and societies. Projects live and breathe within a context, be it business or non-business, therefore as part of the context a large amount of data about projects and management of projects is being collected on an ongoing basis too. Prima facie, the nature of data being collected about project management is of two types: (1) about the broader project eco-system which involves policies, regulatory environment, business context, project benefits realization procedures, project finance, and organizational project maturity, and (2) about activities, events, happenings and lessons learned during actual project management.

What it means is that the Big Data collection as described above can be utilized in at least two ways. First, the data can be used to develop new science and protocols for improved planning, control and delivery of projects. Secondly, the data can be analysed to shape the future of overall project ecosystem. To start the conversation on this critical issue, we discuss some of the ways in which Big Data analytics can influence the future or project management delivery.

1. Use of Big Data analytics to shape future of project planning, control and delivery

The use of Big Data analytics can shape the future of project management delivery in many ways, some of which are discussed as under.

a. Planning and delivery

Planning and delivery activities are often heavily documented providing opportunities for consolidation and analysis of information related to these. The increased use of technology in projects makes it easier to collect and analyse Big Data related to planning and delivery. For a large organization or a projectized organization, the data volume and variety could help in doing analysis and developing insights on how to redefine the internal planning processes and parameters so as to do things in innovative and creative ways.

Overall consolidation of planning and delivery related Big Data across business segments (e.g., IT, HR, Manufacturing, logistics, inventory etc), industries (e.g., construction, IT, defense, education etc.), economic sectors (e.g., mining, agriculture, transport etc.), regions (asia-pacific, Americas, Europe etc.) could provide insights at a wider scale leading to development of new planning and delivery frameworks and methodologies. As such, Big Data analytics could play a significant role in shaping the future of project and delivery activities.

b. Project team environment

A significant amount of data about the project team members is collected on an ongoing basis. That includes experience of working on projects within current and past organisations, skills, education, training programs that project team members may have attended, performance evaluation, and the size and configuration of teams that they have worked with. Additionally, some data on team working environment including conflicts, their resolution, team member attrition, leadership, and team performance is also recorded. These sets of data when consolidated and analysed using Big Data technology could provide insights on how to form teams more effectively, how to develop optimum size and configuration of teams, the skill sets needed by teams for managing projects of future, development of scalable leadership, and capability building within the organization for management of complex projects of future. We believe that Big Data analytics and associated technologies could play a critical role in shaping team developments and providing new thoughts on team formation processes, and the mix of skills team members need to deal with complexity of future projects.

c. Knowledge management

In an era of ubiquitous use of internet and associated technologies a significant amount of information is collected as part of knowledge management efforts, both in project and business organizations. Such information is processed and transformed in the form of knowledge on an ongoing basis. This knowledge could be in the form of logs, lessons learned, best practices, troubleshooting and firefighting information on issues as they happen. However, often it is the case that this knowledge is not used to its maximum value ending up in archives. Given the dynamic nature of project management, people move on to new projects and archival information remain buried in data stores. Big Data analytics and associated technologies can process this vital information for suggesting future developments and growth of discipline. By using Big Data technology, we could find new ways of dealing with issues and problems, develop new best practices, and new technologies to work more effectively. This is all the more important as future projects will involve complex artificial intelligence, and Internet of Things (IoT) developments, necessitating the need for learning from present to shape the future.

d. Risk and Issues management

Project management is very dynamic and affected by various internal and externally imposed events. Risks when become ‘Issues’ need to be dealt with to minimize their negative impacts on project delivery outcome. Prudence suggests that project teams should actively identify risks and manage them on an ongoing basis. That would mean all risk events need to be documented. Similarly, when risks actually happen and become Issues, the firefighting and troubleshooting activities to deal with the emerging issues are thoroughly documented as well. Such an approach results in creation of a large amount of data that can be analysed for enhancing risks and issues management. Big Data analytics can be a critical tool in analyzing Risks and Issues related data to develop new techniques and procedures for identifying, analyzing, prioritizing monitoring and creating risk response strategies. Given that the research suggests that risk management is not given a priority and rightful importance, there is a real need for developing new methods, procedures and techniques that can receive wider acceptance from people working on projects to apply the newly developed procedures to deal with risks and issues more effectively. Big Data analytics can help achieve these objectives.

e. Quality management

Quality management involves considerable amount of work during planning, designing, construction and testing phases of project management. Hence a lot of data is prepared, processed, captured and analysed during the project delivery. This data is related to quality planning including developing policies, decisions on using quality criteria and the criteria thresholds, use of quality standards such as ISO quality standards and so on so forth. In quality assurance part, considerable amount of data is collected to ensure qualities processes are being implemented, standards and requirements are being met. Similarly, the data on the use of quality control tools and parameters, the results of these procedures and handling of issues arising out of quality control procedures are also collected.  Consolidating the above-mentioned data for multiple projects could become sizable data that can be analysed to develop enhanced quality processes and solutions. Big Data analytics can be used to analyse the quality management data to develop new quality standards and frameworks, new quality control techniques and procedures, new dashboard technology to monitor quality during project execution, and new thresholds, criteria and parameters for measuring quality against the baseline standards

f. Resource management

Resources in a project context include human inventory, infrastructure, technologies, financial, tacit and explicit knowledge, procedures and organizational process assets. Similar to other aspects a large amount of data is collected on use of resources, types of resources, unit of measure, the amount required, the amount used, the unsed resources and control mechanisms for resource utilisation. The resources are typically convertible into cash and hence analysis of resource data can provide insights for better management of them possibly resulting in savings in costs. Big Data analytics could play a vital role in developing new procedures for resource procurement, allocation and management. Big Data analytics can help in creating new software applications for resource management.

Concluding thoughts:

The fast-paced technological advancements require keeping up with the speed of these developments. Project management being a vibrant profession has little to no option but to leverage upon the technological advancements to remain relevant and fresh. Big Data analytics is one such technology that seems to have the potential of creating value for business and project management alike. The preliminary thoughts discussed in this article clearly point to the usefulness of Big Data analytics for shaping future of project management. It seems that the time is ripe for project management as a profession to cease upon the Big Data analytics opportunity to usher into an era of 21st life.