Introduction to Predictive Analytics


What is Predictive Analytics

Predictive analytics incorporates a variety of statistical techniques ranging from predictive modeling, machine learning to data mining that analyzes current and historical data and facts to make predictions about future or uncertain events, which are expected to occur in the business or organization. Predictive analytics is the use of data, statistics, algorithms and machine learning. The goal is to leave behind knowing what has happened already and then to provide a better assessment of what will happen in the future which happens to be in the favor of the organization.

In business, predictive models can capture connections among the various factors associated with a specific set of situations and can discover and use this newfound data on hidden patterns in historical data to its maximum potential. These models ensure that the actions which are being taken today by the stakeholders will directly and efficiently achieve the organization's goals tomorrow and in the end.

Predictive analysis helps companies in optimizing their existing planning and execution processes, a better understanding of their customer, stakeholders, external and internal publics and their behavior, identify unexpected opportunities, and anticipate problems and helps in managing crisis before they happen.

Although predictive analytics has existed for decades, it is a technology, which has started using it only recently.

With interactive and user-friendly software becoming more widespread, predictive analytics is no longer just available for the understanding and usage of mathematicians and statisticians. Business analysts and experts are using these technologies in their field of work as well.

Predictive analytics is used in various fields and has been of huge help in industries as diverse as finance, healthcare, pharmaceuticals, automotive, aerospace, and manufacturing.

We are all familiar with a very user-friendly predictive model that we use and see in our day-to-day life, the weather forecasting apps.

Applications of Predictive Analytics


1. E-commerce product recommendations

Amazon and Google ads display products we may like on our return visit the sites with the help of machine learning to track our previous likes or dislikes, preferences and purchase behaviors. Based on these understandings, the sites customize and personalize their recommendations.

2. Self-driving cars

Machine learning and artificial intelligence fuel the futuristic travel that learns road rules and safety measures in an ongoing manner based on the data regarding roads and accidents, which has been encrypted into the system. This enables the car to predict the worst-case scenarios in case of any sort of failure.

3. Business marketing


Oftentimes, advertisements show up right after we visit a site, but leave without buying or using the said good or service. These targeted ads are displayed using the data, which has been tracked from our previous interaction with any site. Such algorithms are capable of learning our preferences and purchase behaviors. This further enables such businesses and organizations to create manipulative marketing and sales campaigns, which are able to convert the buyers' decisions in their favor.

4. Healthcare
A very common example is breast cancer screening and detection. This kind of technology is being used in drug discovery with the help of previously collected data being used in identifying, scanning and screening diseases and effects in advance.