So exactly what is predictive analytics of data and why is it so important?
In very basic terms, predictive analytics of data is the process of using statistics or facts gathered from large amounts of digital data (big data) that can help predict a future issue/event. It can help identify trends and patterns that can be critical in decisionmaking, and it’s applications are widespread across many industries including healthcare, marketing, manufacturing and legal.
Predictive analytics and enhanced data management have rapidly emerged as important topics in the legal, insurance, and private sectors. Viewed as a new competitive way to put big data to work intelligently, clients, policyholders, and industry leaders quickly understood the benefits and wanted to hear more. So whether you are an attorney, insurance professional, or risk manager for a business, you should be comfortable and well versed on the topic.
In the case and claims management space enhanced data management technology can be used in many different exposure areas and the results can lead to real cost savings and efficiencies. Modern data management systems can help generate insightful intelligence from ordinarily static and very claim specific data. Data intelligence such as patterns and trends can be readily used to identify and help reduce risk factors. Mitigation of risk is perhaps the most single important driver of exposures and claims and all tools supporting that end are worth serious consideration.
Using data in such a proactive manner is an attractive value enhancer to attorneys, risk management professionals and carriers and results can be multi dimensional, providing clear benefits for the insurance carrier and its policyholder, the legal client, and the corporate company.
Some examples of areas that benefit from predictive analytics of big data include:
Safety and loss control
The most important point to begin this series is to state perhaps the obvious; to offer some degree of predictive data analytics you must have a data management system that 1. can capture historical and current data and 2. have the capacity to organize and categorize it a meaningful way. I suggest taking a new look at the depth of how your organization uses data and be open to improving it.
My future posts will explore how enhanced data management and predictive analytics offer value to each of the above exposure areas and how technology can help deliver improved results.