Practitioners in the field of data analysis usually break down their work into three genres of analytics, given as follows:
- Descriptive: Descriptive is the oldest field of analytics study and involves digging deep into the data to hunt down and extract previously unidentified trends, groupings, or other patterns. This was the predominant type of analytics done by the pioneering groups in the field of data mining, and for a number of years the two terms were considered more or less to mean the same thing. However, predictive analytics blossomed in the early 2000s along with the burgeoning field of machine learning, and the many of the techniques that came out of the data mining community proved useful for prediction.
- Predictive: Predictive analytics, as the name suggests, focuses on predicting future outcomes and relies on the assumption that past descriptions necessarily lead to future behavior. This concept demonstrates the strong and unavoidable connection between descriptive and predictive analytics. In recent years, industry has naturally taken the next logical step of using prediction to feed into prescriptive solutions.
- Prescriptive: Prescriptive analytics relies heavily on customer goals, seeks personalized scoring systems for predictions, and is still a relatively immature field of study and practice. This is accomplished by modeling various response strategies and scoring against the personalized score system.
Please see the following table for a summary:
|
Type of analytics |
Problem statement addressed |
| Descriptive | What happened? |
| Predictive | What will happen next? |
| Prescriptive | How should we respond? |
