RC: What are some of the key challenges facing project teams, in terms of handling the volume and complexity of data generated by major/mega projects?

Gilge: For many major/mega projects, a majority of project information is not consolidated or coordinated from a centralised data storage, or a data sharing standpoint. This is often due to the complex nature of contractual arrangements, but may also be due to a lack of agreed upon systems or tools of record among the project stakeholders. This means project teams must often piece together project information using a combination of systems and tools. For example, many contractors and subcontractors utilise different tools to track and manage everything from RFIs to change orders and schedules. If these systems and tools are not compatible from a data sharing standpoint, project teams must either manually input data from one system to another or rely on information that is simply not integrated and may contain discrepancies.

RC: To what extent are traditional monitoring and compliance techniques outdated? How can they hinder the efforts of project teams?

Gilge: While projects have grown increasingly complex over the past decades, many project teams have continued to rely on manual, outdated review processes that are not designed to provide the necessary level of oversight required for early identification and mitigation of risks and issues. Take the area of cost management. A typical major/mega project will have hundreds of thousands, and in some cases millions of transactions. These transactions are incurred by various contractors, consultants, equipment vendors, and material suppliers. A skilled project controls or audit team may be able to test 5 percent of the cost transactions over the course of a project. Less skilled teams in many cases test less than half a percent due to time and resource constraints. This low level of coverage might provide comfort regarding the integrity of the overall review process, however it will not provide comfort that all major cost issues are being ‘caught’ and addressed in a timely manner due to the extensive lag time of this approach and limited ability to leverage predictive analytics.

Oct-Dec 2015 Issue