Predictive analytics is about producing valuable business insights and accurate foresights or forecasts from large sets of data, to help users decide on the next-best course of action and provide better customer experiences.
Garbage In, Garbage Out:
Predictive analytics is only as accurate as the quality of the original sets of data sets mined. Ensure that you are constantly honing your analytics processes to get the most valuable insights and predictions for the future from the data you have.
Understand the Business Problem:
You need to understand both the data being used for a predictive analysis exercise and the business problems being addressed.
Find The Best Tools:
Once you understand the data and business problem find the right software and the right expertise to build and refine the predictive models.
Nobody Has Perfect Data:
There is a misconception that pristine data is a pre-requisite for using predictive data tools. Not so. No business has perfect data.
Iteration Is Key:
However sophisticated your raw data, predictive analytics offers a competitive edge. Start with what you have and improve your data set as you go.
This article originally appeared in cbronline.com. To read the full article, click here.