If your organization's predictive analytics aren't effectively anticipating and mitigating risks, here's what you can do:

General Guidelines / Steps

ü? Arrange a meeting with your analytics and other relevant professionals to discuss and evaluate the possible reasons and review and ensure the accuracy, completeness, and timeliness of the data being collected and used for the analysis.

ü? Make sure your predictive models are up to date and compatible with the latest used information and changing conditions, also try to find and use additional sources of information for better results.

ü? Use AI and ML based advanced analytics tools and cloud-based platforms to handle larger data efficiently with better predictions accuracy.

ü? Think about all possible scenarios which may happen and use different methods to assess risks. This can help to prepare for a range of possibilities.

ü? Have dedicated teams to perform and oversee the analytics process from data collection to model application and ensure that the clear policies and accountability for date management and model use.

ü? Communicate and collaborate with different departments and stakeholders about the data analysis and uncertainties that exist. Diverse perspectives can help to identify potential risks.

ü? Upon failure of prediction, conduct a review to understand the reasons and foster a culture of learning and improvement. Failure is a step stone to get succeeded.

ü? Consult with the experts outside the organization. Sometime an outside perspective can help you identify issues which might be overlooked. Also compare your practices to industry standards and best practices to find the areas for improvement. ?

ü? Provide regular staff training to keep their skills sharp and up to date and create a culture within the organization to understand the value of data for better decision making. ?

In general, by taking these steps, you can make your predictive analytics more effective, helping your organization to better anticipate and mitigate risks.