Data analytics in business play a vital role in decision making. This article focuses on the different types of data analytics – Descriptive, Diagnostic, Predictive, Prescriptive. These types of analytics determine what an organization needs to know, right from what is happening to how the business plans should be. In addition, there is a relationship between these analytics types. Consequently, the implementation of different data analytics solutions go through staged phases.

To put it in simple words, the below defines the four types of data analytics:

1) Descriptive Analytics: Summarizing data using business intelligence tools to understand what has happened and what is going on.

2) Diagnostic Analytics: Analyzing data history to find what happened and why it happened.

3) Predictive Analytics: Guessing the expected outcomes using machine learning techniques and statistical models.

4) Prescriptive Analytics: Recommending a set of actions based on the results of data analytics.

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1) Descriptive Analytics

This is the most basic form of analytics. Analyzing lots of data requires more time. So, it is good to analyze smaller chunks of data. For this reason, the data findings are summarized first to understand what is going on.

This type of analytics encompasses descriptive statistics on the existing data. It helps to put the raw data in an understandable format. Therefore, it becomes easy to identify the strengths and weakness of a business and form organizational strategies.

The two main methods in descriptive analytics include data aggregation and data mining. For example, companies can analyze the consumer behaviors and business engagements by mining historical data. In addition, it can help in service improvement, targeted marketing, etc.

2) Diagnostic Analytics

This kind of analytics is used to find why something happened. It includes methods such as data discovery, data mining, drill-down and correlations. Taking a deeper look at data helps to understand the root causes of outcomes. The analysis include factors such as likelihoods, probabilities, and outcome distribution. However, this analytics helps understanding only sequences and causal relationships while looking backward.

Some of the methods that apply diagnostic analytics include principal components analysis, attribute importance, conjoint analysis, and sensitivity analysis. Training algorithms for data classification and regression also come under this analytics type.

3) Predictive Analytics

It is important to note that predictive analytics is used only to forecast the probabilities of a future event. However, predictive models depends on descriptive analytics to derive the probabilities of future events.

Predictive analytics involves building data models and extrapolating the future outcomes. It is mostly applied in sentiment analysis to predict a person’s sentiment on a specific topic. Moreover, the opinions could be positive, negative, or neutral. Sentiment analysis usually involves gathering the person’s opinions from social media.

This type of analytics involves building data models and validating them to gain better predictions. It is dependent on machine learning algorithms such as Support Vector Machine (SVM), Random Forests, and statistical models for learning and validating the data. The prediction purely depends on the present data. The analytics model lies a step ahead of standard BI tools to provide better predictions.

4) Prescriptive Analytics

Prescriptive analytics is based on predictive analytics. It goes a step ahead of the other 3 analytics types. This type of analytics is used to suggest the favorable outcomes following a specific course of actions. For this purpose, a strong feedback system is used to continuously learn the relationship between the outcome and the action.

The calculations involved in this type of analytics include optimizing functions related to the necessary outcome. For example, recommendation engines and cab hailing apps use prescriptive analytics. This analytics type is considered the final stage of advanced analytics as it provides favorable suggestions.

Types of Data Analytics – Conclusion

The four types of analytics may project that all of them need to be implemented sequentially. But most of the organizations proceed straightaway with prescriptive analytics. Many companies are already employing descriptive analytics, but that may not be enough. Prescriptive analytics is necessary to reach the desired goals. However, it is still at the initial stage and not many businesses have completely used its potential. Besides, advancements in predictive analytics would impact the development of prescriptive analytics.