Features of Predictive Analytics
We are concluding this blog series by looking at predictive analytics, the tools that use structured and unstructured data as the basis for making impactful decisions. Predictive analytics enable key decision-makers, whether automated or human, to interpret data and use it to forecast outcomes.
Predictive analytics are distinguished from other types of analytics by the actionable element. For instance, a credit card company might extend a credit limit to an approved customer in real time by reviewing historical data trends for purchases, payments or fraud reports. Enabling this type of analysis, businesses are able to save money on personnel costs and use those resources for enhancing the matrix of the predictive analytics platform.
Another example of how this type of analytics might be used is a transportation company that uses automated systems for rerouting shipments based on traffic conditions. Or imagine a hospital information system that scans thousands of records to suggest a treatment plan for a patient based on those with a similar diagnosis.
During development of a predictive analytics platform, business and IT professionals need a higher than average aptitude for data analysis and tools. However, final systems usually have user-friendly interfaces. This is good news because management buy-in is a must for success, as the data sources can potentially be located across departments.
Often, predictive analytics systems struggle with issues of high complexity, a deficit of skilled practitioners, lack of standardization, and demand for stakeholder input. On the positive side, these same factors help engaged businesses to potentially realize a bigger competitive advantage. This is because large companies can afford the higher costs related to hardware, software, and expertise. However, this does not mean medium-sized businesses cannot take advantage of predictive analytics, especially in the areas of financial services, advertising, law, marketing, transportation, research, and manufacturing. It is not unusual these days to see data-centric companies offering predictive analytics services to Fortune 500 clientele.
The table below summarizes the activities of Predictive Analytics: