The Complete Beginner Guide To Big Data and Analytics
Big Data gained huge acceptance from almost all businesses in very little or no time. Due to its multiple benefits, over 49% of 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. 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 beginner's guide to big data, we discuss the characteristics of big data and three types of data analytics.
1. Descriptive Analytics: Gives insights related to past data
It describes past data for your understanding. As the name defines, it summarizes the stored, collected, or raw data. This analytics makes sense to you by its insights. The data may be from 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 behavior 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
Predictive analysis helps you to picture future events and happenings with data analytics services. It gives you an 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.
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 make sense. It will be very helpful in understanding customer behavior, 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
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 modeling.
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 uses the best tools, techniques, and data, but includes complexities as well.
Conclusion
So, this was our beginners’ guide to big data. It is all about how you carry out these analytics in your businesses. You have plenty of data, and different types of big data analytics and techniques are ready to help you out. It’s your call now to implement these and get the most out of them.