One of the elements that make the prediction of program performance so difficult is that the number of factors influencing that performance is so vast. Further, the interaction of those factors creates additional complexity that confound traditional approaches. Unsupervised learning techniques like segmentation, anomaly detection and hotspot analysis accelerate the understanding of key features without the need to construct hypotheses manually. The result is a far superior understanding of the interactions in the data – and better prediction.
Ultimately, the goal is to understand the trajectory of a program well before it manifests itself as yellow or red. The ability to predict accurately depends on the ability to execute the discover step. Working together, one predicts program performance with exceptional accuracy, up to six months ahead of other techniques – providing ample time to remediate.
Programs are everywhere in an organization. From a department wide Windows upgrade to trillion dollar jet-fighters, programs define the modern organization. Creating an application that can be consumed by those closest to the field is imperative. As a result, Ayasdi is focused on applications. These UX friendly web-based applications enable a program management office to interact with powerful, complex data science constructs without having to understand the underlying math – but with the ability to understand the outputs.
Ayasdi’s Program Performance Intelligence solution is constantly looking at newly arriving data, identifying changing patterns. Some of those patterns may be specific to a program, whereas others will be symptomatic of larger changes in the business or regulatory environment. As a result, subtle patterns suggesting emergent behavior is constantly identified and presented for the subject matter experts to consider.