Uses for Data Science and Predictive Analytics in 2019

Computers have been designed to work faster than human beings. Today, technology is getting smarter, which enables machines to take more involved roles like predicting outcomes and delivering results many times faster than human beings. From this, workers can make various decisions based on facts rather than assumptions.

Data science has been reliable so far and many companies have adopted this technology. When combined with predictive analysis, it even becomes more useful in decision-making. Let us look at the different areas where this technology is used.

Detecting Fraud

Fraud is costly for any company. Insurance firms lose millions of dollars a year through fraudulent claims. If they try to investigate these fraudulent cases, they may spend a lot more. Thus, they can use data science and analytic prediction to determine all areas of fraud and close any loopholes. The data also becomes useful when dealing with such cases. Banks and many other industries have also adopted this to prevent costly fraudulent cases.

Managing Patient Data

Hospitals now receive enormous amounts of data every day. They can take advantage of this data to predict various things related to the patients for better services. For instance, this technology is heavily used to understand why patients are getting re-admitted to the hospital.

It may sound like a simple thing, but it is an area of concern for hospital management to address. They want to know whether there is something they are not doing right. Again, it is used to predict illnesses that affect people at various times of the year and come up with solutions for these situations.

Retail Shopping Trends

The ActiveWizards experts agree that retail outlets have now become bigger, and they want to be on top of their operations. Proper customer management requires them to understand shopping trends so that they can stock the products that are in demand, give offers during the festive seasons, and outshine their competitors.

But they need a solution that will analyze the shopping trends through data that is fed into their systems. For now, this has been a big success, and it is expected to get better by the day. If you have been a shopper at a certain outlet, you may realize that they have already customized your shopping experience.


There are many areas of research today ranging from medical research to machine research. As this is happening, innovators would like to reduce future risks like accidents and inaccuracies as much as possible. Thus, they have turned to data science and predictive analytics to succeed in this.

Medical research, for instance, looks at the possible outcome when a certain solution is rolled out. If there are too many risks and side effects, the medication in question is either abandoned or improved to remove the risks.


See, data science has now become very reliable and helpful. Its ability to be applied in many sectors is very impressive. Apart from the four use cases that we have discussed, there are many others with some in testing stages and others being perfected.