Descriptive analytics is the analysis of historical data using two key methods – data aggregation and data mining – which are used to uncover trends and patterns. It is not used to draw inferences or make predictions about the future from its findings; rather it is concerned with representing what has happened in the past. Descriptive analytics are often displayed using visual data representations like line, bar and pie charts. Because it uses fairly simple analysis techniques, any findings should be easy for the wider business audience to understand. And exactly for this reason, descriptive analytics form the core of the everyday reporting.

Examples could be – Annual revenue reports, Inventory reporting, Warehousing and Sales data. Another widely used example is social media and Google Analytics tools, which summarize certain groupings based on simple counts of events like clicks and likes.

Predictive analytics is a more advanced method of data analysis that uses probabilities to make assessments of what could happen in the future. Like descriptive analytics, prescriptive analytics uses data mining – however it also uses statistical modelling and machine learning techniques to identify the likelihood of future outcomes based on historical data. To make predictions, machine learning algorithms take existing data and attempt to fill in the missing data with the best possible guesses. These predictions can then be used to solve problems and identify opportunities for growth.

Examples could be – Organizations are using predictive analytics to prevent fraud by looking for patterns in criminal behavior, optimizing their marketing campaigns by spotting opportunities for cross selling and reducing risk by using past behaviors to predict which customers are most likely to default on payments.

Another branch of predictive analytics is deep learning, which simulates human decision-making processes to make even more sophisticated predictions. For example, through using multiple levels of social and environmental analysis, deep learning is being used to more accurately predict credit scores and, in the medical field, it is being used to sort digital medical images such as MRI scans and X-rays to provide an automated prediction for doctors to use in diagnosing patients.