Predictive Analysis

Predictive analytics is the process of transforming data into future insights. It involves various statistical mechanisms such as data mining, data/predictive modeling, artificial intelligence, and machine learning. With proper analysis of past data and facts, it is possible to forecast the occurrence or non-occurrence of the future or unknown events. 

With our predictive modeling system, we study the historical transactions of your company in order to detect possible opportunities or otherwise. This would help in determining key factors that can serve as a guide towards making important decisions. 

There is a myth that predictive analytics tells the future. Well, it doesn’t. But we would tell you what it does. Predictive analytics involves the extraction of patterns and trends from a data set. With this, it is easy to forecast what events, trends or outcomes might occur in the future, in terms of risk assessment and business opportunities.

Predictive analytics is an offspring of the big data revolution. We are steadily transforming into a data-driven world. In actual fact, data is the new oil! With data mining, text analytics and statistics, we can create a form of predictive intelligence system. This would prove highly resourceful in unraveling the possible relationships existing between data trends, either structured or unstructured data. 

The process of making forecasts via predictive analytics is quite technical. However, it could be understood this way:

Understand what project/organization/agency/industry you are working on/with.

Data collection is the process of gathering informational resources on past records of historical transactions.

Data mining is the process of extracting information from a huge data set, and further, transform it into an understandable format for a specific purpose.

Statistical analysis is the process of collecting, organizing and analyzing a data set in order to enable its interpretation, presentation, and application. 

Predictive modeling involves the usage of already refined data to make predictions concerning a future or past occurrence.

Model deployment involves the actual implementation of the predictive analytics results into practical plans and development.