Big data analytics is the process of analysing large, complex sets of data using specialized techniques in order to discover trends, patterns and correlations that can help businesses make informed decisions.
Big data analytics is used when your data becomes so vast, complex and voluminous that traditional systems cannot process it, so you require advanced computing capabilities to extract meaning out of it.
Because different organisations follow different approaches while processing data, big data analytics can be classified in 4 major categories:
Descriptive Analytics
This type of analytics is used for uncovering patterns in historical data. It summarizes the past data in a simple, easy-to-understand format.
It merely ‘describes’ what has happened in the past. It does not dig deeper to understand the underlying cause of the same.
Diagnostic Analytics
As the name suggests, diagnostic analytics helps us understand the root cause of a trend or pattern that has emerged in the analysis of a data set. Data scientists turn to this kind of analytics in order to drill down into the root cause of a problem affecting a business.
For instance, if the sales graph shows a dip in the current month, diagnostic analytics can help us identify why the sales dropped.
Predictive Analytics
As you might have guessed from the name, predictive analytics is concerned with predicting what might happen in the future. It relies on historical and present data to predict the possible outcomes related to market trends, consumer preferences, and so on.
It’s important to note that no analytics can tell you what ‘will’ happen in the future, they can only estimate the probabilities of certain outcomes.
Prescriptive Analytics
An underused form of analytics, prescriptive analytics helps us decide what should be done in the future. It explores several possible actions and suggests the most appropriate course of action depending on the results of descriptive and predictive analytics.