A data analyzer for use in predicting a wind turbine component failure, said data analyzer comprising: a database including operating data and anomaly alerts for a plurality of wind turbines and a processing device coupled to said database; the processing device configured to: determine a parametric profile for a component of a wind turbine from the operating data by calculating a Z-score for the relationship between the operating data and expected operating data for the component over time, wherein the anomaly alerts are generated when a component parameter falls outside of an expected range, and the parametric profile defining at least one parametric event associated with the component prior to failure of the component; determine an anomaly profile for the component of the wind turbine from the anomaly alerts, the anomaly profile defining at least one anomaly associated with the component prior to failure of the component; determine a probability of failure for the component of the wind turbine based on the parametric profile and the anomaly profile; characterized in that the processing device is further configured to: identify anomaly alerts associated with the component; validate the accuracy of the anomaly profile based on the anomaly alerts; and select the anomaly profile that exceed a threshold accuracy in order to determine the anomaly profile; and validate the operating data used to determine the parametric profile by discarding a parametric profile if a desired accuracy is not achieved or by weighting a parametric profile according to its accuracy when determining the probability of failure. a database including operating data and anomaly alerts for a plurality of wind turbines and a processing device coupled to said database; the processing device configured to: determine a parametric profile for a component of a wind turbine from the operating data by calculating a Z-score for the relationship between the operating data and expected operating data for the component over time, wherein the anomaly alerts are generated when a component parameter falls outside of an expected range, and the parametric profile defining at least one parametric event associated with the component prior to failure of the component; determine an anomaly profile for the component of the wind turbine from the anomaly alerts, the anomaly profile defining at least one anomaly associated with the component prior to failure of the component; determine a probability of failure for the component of the wind turbine based on the parametric profile and the anomaly profile; characterized in that the processing device is further configured to: identify anomaly alerts associated with the component; validate the accuracy of the anomaly profile based on the anomaly alerts; determine a parametric profile for a component of a wind turbine from the operating data by calculating a Z-score for the relationship between the operating data and expected operating data for the component over time, wherein the anomaly alerts are generated when a component parameter falls outside of an expected range, and the parametric profile defining at least one parametric event associated with the component prior to failure of the component; determine an anomaly profile for the component of the wind turbine from the anomaly alerts, the anomaly profile defining at least one anomaly associated with the component prior to failure of the component; determine a probability of failure for the component of the wind turbine based on the parametric profile and the anomaly profile; characterized in that the processing device is further configured to: identify anomaly alerts associated with the component; validate the accuracy of the anomaly profile based on the anomaly alerts; and select the anomaly profile that exceed a threshold accuracy in order to determine the anomaly profile; and validate the operating data used to determine the parametric profile by discarding a parametric profile if a desired accuracy is not achieved or by weighting a parametric profile according to its accuracy when determining the probability of failure.