Four types of Analytics, their definitions and differences.
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
What is Descriptive Analytics? Descriptive Analytics aims to explain what happened. Descriptive analytics looks at data statistically to tell you what happened in the past. This can be in the form of data visualizations, like graphs, charts, reports, or dashboards. In a trading setting, this can is simply an illustration of patterns in the data.
What is Diagnostic Analytics? Diagnostic Analytics to explain why something happened. Diagnostic analytics takes descriptive data a step further and provides deeper analysis to answer the question: Why did this happen? We may be able to explain some price movements.
What is Predictive Analytics? Predictive Analytics aims to predict what will happen. Predictive analytics takes historical data and feeds it into a Machine Learning model. The model learns from some parts of the historical data. The model is then tested to predict what will happen next on other part of the historical data. Predictive analytics may statistically predict a price decline in a financial risk asset based on patterns in a set of alternative data.
What is Prescriptive Analytics? Prescriptive Analytics aims to recommend actions. Prescriptive analytics takes predictive data analytics to the next level. It suggests various courses of action and outlines what the potential implications would be for each. Trading models that use advanced machine learning are optimisation tools producing prescribed actions – typically LONG and/or SHORT signals – based on return, variance, and other arbitrary parameters, such as liquidity, market impact, and trading cost.
SUMMARY: Whereas Descriptive Analytics explains what happened, Diagnostic Analytics explains why it happened, and whereas Predictive Analytics predicts what is most likely to happen, Prescriptive Analytics recommends what actions to take.