Project risk management is a set of guidelines, typically organized into a framework, that is flexible enough to be used throughout the process while being specific to your work through the use of iteration. The various topics within project management have been following similar frameworks throughout this blogs history, risk management specifically involves going through processes of identification, evaluation, handling, and controlling. The figure below will act as a visual representation of the processes as it occurs at Nokia Siemens Networks, a large telecom firm.
This process is applicable very generally to teams in corporate environments, but more recently, the quantity of data required has outpaced this system in large projects. As a result, more teams are moving towards big data analysis (BDA) for their risk management requirements. Big data itself, while more complicated to implement, does allow various advantages that traditional data sets do not- these include trends hidden behind large amounts of data, information that provides bases for claims and action due to quantity of evidence, and elimination of partiality commonly found within smaller datasets.
Big data allows for a unique advantage from traditional datasets, visualizing trends with increased accuracy and allowing for preventative measures against both internal resource leaks as well as external conflict and fraud. Risk management is already one of the most critical components to managing a project, identifying these risks for both your team and your stakeholders remains paramount from the inception of your project to its conclusion.
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