Predictive analytics definition

Technology Dictionary


Your essential glossary of Big Data
and Artificial Intelligence terms.

What are Predictive Analytics?

This type of analytics answers the question “What might happen in the future based on what has happened in the past?”
It analyzes past data trends and model in order to try to predict how they will evolve in the future. For example, a company can predict its growth by extrapolating past behavior and assuming that there won’t be relevant changes in its environment. Predictive analytics can recommend improvements and provide answers to questions where Business Intelligence cannot.


To identify the causes of success or failure in the past in order to understand how they might affect the future. It can be very useful when establishing realistic business objectives, planning in a more effective way or setting reasonable expectations.


Using different statistical algorithms and Machine Learning Learning to predict the probability of a future result. The data that feeds the algorithms comes from CRMs, ERPs of HR systems. These algorithms are capable of identifying relationships between different variables within the dataset. They are also capable of filling gaps in existing information with the best possible predictions. Even though they are the best possible predictions, they are still predictions.


It is usually used for Sentiment Analysis. Data enters the learning models in the form of plain text. The model then assigns a value to them on a scale that indicates whether the emotion is positive, negative or neutral.


In financial environments, it is used to assign “credit scores” to clients (the value that predicts whether the client will pay on time). Many retail companies also use it to identify purchase patterns, make predictions about stock and identify products that are usually bought together (in order to offer personalized suggestions).


  • Predictive modeling: What would happen now if… ?
  • Predictions: What would happen if current trends continued?
  • Causal analytics: What led to this happening?
  • Monte Carlo Simulations: What might happen?
  • Data Mining: What correlations are there between datasets?
  • Identification of patterns and alerts: When should we take action to correct a process?