Predictive Analytics, 2019: What, When & How?

Even if you have heard of predictive analytics in the past, you may not be sure about what exactly does it mean. A prediction will always remain a prediction with all its shortcomings as well as its advantages.

Basically, predictive analytics is a process where a piece of software analyzes the previously collected data which allows us to anticipate the future and make the right decisions. It extracts information from data sets to discover complex relationships, recognize unknown patterns, forecast actual trends, etc.

Predictive analytics is the domain of data scientists which follow the analytical workflows represented by the following categories:

  • Identify business outcomes
  • Determine data required to train
  • Determine types of analysis
  • Validate results
  • Test predictions on performance

There are numerous applications of predictive analytics and business intelligence, some of which are:


Risk assessment allows the users to analyze or predict the possible problems associated with a particular business. The goal behind such evaluation is to build decision support systems that can accurately predict the profitable operations for a company.


The analysis of market surveys helps the companies to accurately address the customer requirements, increase the profits and therefore reduce the attrition rate.


Sales forecasting can be applied to short-term, medium-term or long-term forecasting. Evaluation of prior history, seasonality, market events, etc results in the realistic prediction of sales which is the keystone of a company’s planning. The improved forecast accuracy has led to better decision making for the future.


The hospitals can make use of various predictive analytics software to analyze and predict the health parameters for future preventions. Health Catalyst in Salt Lake City, founded in 2008, claims to provide predictive analytics services, especially for the healthcare domain by collaborating with the hospitals or health care companies.

According to a definition from SAS, predictive analytics uses statistical analysis and machine learning to predict the probability of a certain event occurring in the future. Analytical solutions ultimately aim to provide better and effective decision-making capabilities for humans. A combination of AI, big data analytics, and data science techniques seem to be a growing trend in many industry sectors, with predictive analytics being one of the most well-known.