Big Data – Descriptive, Predictive, And Prescriptive Analytics Explained


Descriptive, Predictive and Prescriptive analytics are the major parts of big data. Big Data gained huge acceptance from almost all the businesses in very less or no time. Due to its multiple benefits, over 49% of the companies make use of it in their businesses globally.

Many major companies are using big data for a detailed analysis of business functions. But only some of them are efficiently getting its benefits. However, companies can make use of it for their business growth through key insights. Big data analysis is further divided into three types based on the complexities.

The three terms are descriptive analysis, predictive analysis, and prescriptive analysis. Each of these types shows different levels of big data analysis.

In this article, we discuss the three types of big data analytics.

1. Descriptive Analytics: Gives insights related to past data

It describes past data for your understanding. As the name defines, it summarises the stored, collected or raw data. This analytics makes sense to you by its insights. The data may be of the past month, a year or a decade. It gives you a clear picture of the event that occurred and its results. It allows you to understand or learn from past behaviour or circumstances.

The best part is that you can understand how that past data can influence future events. Your huge statistics formulas and techniques will fall under this. It is one of the best techniques for data analysis, as it contains less or no code. The popular tools for descriptive analysis are Tableau, Qlikview and more.

Using descriptive analysis, you can represent graphically and analyze that. It is helpful to analyze things like year wise sales, the average money spent on customers and more. In general, it provides historical data representation of companies’ financial, production stats and more.

2. Predictive Data Analytics: Gives Future Predictions

Having a fair bit of knowledge regarding future proceedings is a good thing. The predictive analysis helps you to picturise future events and happenings by data. It gives you the idea of what might happen in the future.

It is entirely based on probabilities, as it deals with probable events that may happen in the future, but no statistical graph can give you a 100% prediction or estimation. You can use it to understand what can happen or what will be the result and many more things.

These methods use collected data and fill the missing variable with best guesses. It analyses past data and identifies the data pattern with specific algorithms. This analysis makes some predictions or estimations that makes sense. It will be very helpful in understanding customer behaviour, purchasing and sales patterns.

One popular application that you may have experienced is credit score prediction. It is one of the best uses of predictive analysis. Many fintech companies use this to generate the credit score of the user. It uses the probability to predict future payments of the customer.

3. Prescriptive Analysis: Gives an idea regarding future outcomes

This is an all-new field that prescribes the future outcomes to land on a definite solution. Prescriptive analysis analyzes the effect of future outcomes and throws some recommendations at you. It not only predicts the outcomes but also gives recommendations regarding future actions, which you are looking for.

This passes both descriptive and predictive analysis by giving the future course of action. It deals with multiple outcomes to choose the best one, one from which the company will make some profits. This is a combination of machine learning, algorithms, business, and computational modelling.

These techniques will be applied to collected data, past data, real-time data and more. It is a complex action, and that’s why many businesses are not using it in their daily courses. But if it is carried out properly, its impact on businesses is unmatchable because it is using the best tools, techniques, and data, but includes complexities as well.

Conclusion

It is all about how you carry out these analytics in your businesses. You have plenty of data, there are innovative analytic tools and techniques are ready to help you out. It’s your call now to implement these and get most out of it.