Predictive Analytics

Organizations around sectors have come to rely on the 2.5 quintillion bytes of data humans collect every day to understand their customers better, recognize behavioral trends, and make more meaningful and strategic decisions over the past decade.

These companies have evolved their data-related activities as the technologies used to capture and interpret data have advanced. Data analysts can also gain a degree of perspective beyond a summary of previous activity and instead looks ahead at potential opportunities by creatively using data.

This new application in data mining, known as predictive analytics, has effectively served several essential market needs.

When a doctor needs to estimate a new patient’s cholesterol-based solely on their BMI, a linear regression model will support (BMI). In this case, the researcher will know to enter the data the doctor collected from his 5,000 other patients into the linear regression model, including each of their BMIs and cholesterol levels. They’re attempting to forecast an unknown based on a collection of quantifiable data.

Whereas linear regression uses only numeric data, mathematical models can also make predictions about non-numerical factors. Text mining is a perfect example.

An analyst can use text mining modeling tools to comb datasets, find common symptoms in previous patients, and produce a hypothesis about what this current patient is “most likely” suffering from based on the data, particularly in complicated patient situations.

Organizations around the board, from banking to manufacturing to healthcare, are beginning to respond to this paradigm of proactive decision-making and leveraging it to their advantage:

Insurance providers will also predict whether a potential customer is a liability based on their age, medical background, and other factors. They will use this information to make an educated decision on whether or not to protect the individual.

In the banking industry, for instance, classification algorithms mostly used for binary classification problems are useful in predicting whether a credit card application was approved or not approved.

Based on customer demographics, retailers will estimate how new products or goods will do in their local market. Then they will make informed decisions about how much inventory to have on hand.

The recent advances in statistical modeling and the overall lean into data as a prescriptive source of insight have transformed the way companies work today, regardless of industry. Businesses may use statistical modeling to make data-driven decisions, helping them to reduce risk and increase income. These developments have resulted in a general trend in decision-making that will undoubtedly continue to grow and evolve in the coming years.