Stages of Predictive Analytics

[subornaakter20]

Predictive analytics is based on big data. Big Data is an array of information that is impossible to work with without special programs. Modern IT companies are developing special tools that are designed to process data and display it in the form of convenient tables, reports, and graphs. One of the most popular dashboards today is visual statistics on the number of coronavirus victims, prepared by the Johns Hopkins University Center for Science and Engineering in the United States.

Data sets are constantly growing. They are chiropractor email address constantly collected by organizations and all the devices we use in everyday life, that is, our computers, tablets and smartphones. It is very easy to obtain such data. For example, it is easily read from our plastic cards, which have almost completely replaced cash.

As an example, we can cite the following data arrays:

information collected from the Internet about visited websites, online payments, and likes on social networks;

information from CRM systems, i.e. data on the number of clients, calls and transactions;

sensor readings, including telemetry;

business indicators.

The above examples have already become classics of big data. And more recently, systems have begun to successfully collect and analyze more complex information, such as the incomes of famous American athletes, movie plots, and the exact geolocation of lightning strikes.

The forecast is made in several steps:

Define the purpose of predictive analytics. This influences the choice of parameters to collect.

Data generation. For correct analysis, all indicators must be identical and accurate. Analysts are required to translate the entire array of information into a readable form, since there are failures in the program during collection.

Data analytics. Special tools are used for this. They can be standard or developed for a specific company.

Building a model. This is done using machine learning or other AI tools. Experts determine the relationship between factors and indicators and then model the forecast.

Practical use. At this stage, you can finally understand whether the forecast was correct. As it works, the model collects data again, retrains, and makes adjustments to the forecast.

Predictive analytics can be wrong, and that's okay. After all, if it were possible to predict the outcome with 100% accuracy, no stock exchanges would exist, and we would know in advance what would happen to shares in each specific case. In life, everything is much more complicated, any economic indicator depends on many parameters. A predictive model can learn, and its forecasts become more accurate.