Big data analytics encompass diverse methods to extract valuable information through data set examination by using various tools and processes. The method observes unknown patterns, hidden correlations, trends, and other insights that are wrapped inside statistics.
Big data analytics is such a buzzword among business leaders and industry stakeholders. Today, we’re having an unprecedented huge amount of raw data as we live in a digitally-driven environment. From websites to social media, we can derive statistics that can help our business.
Kinds of Big Data
There are three kinds of big data; structured, unstructured, and semi-structured data. Big data can be calculated in terabytes or more. The structured data refers to all the data that can be kept in a tabular column. The unstructured one means the data that can’t be put inside a spreadsheet, whilst the semi-structured data means something that doesn’t exactly look the same with the structured data model. Examples of unstructured data are audio and video data. While the instances of semi-structured data are XLM data, JSON files, and others.
Factors Surrounding Big Data Analytics
There are three elements that make up big data analytics; volume, velocity, and variety. To analyze big data, we need to deploy tools to make the huge volume of data reasonable then turn it into beneficial business insights.
The task for analyzing big data can be very daunting given the fact that we are generating data at extremely high speeds. Every organization needs to study the data. Only then will the organization can come up with conclusions.
There are four types of big data analytics. Number 1 is prescriptive analytics which is based on the rules and recommendations before coming to a certain analytical path for the organization.
Also read: How to Choose Big Data Service Provider in the BDaaS Industry
The second type is predictive analytics which aims at ensuring the projected path will benefit for the future course of action. The analytics applies diagnostic analytics to search for the patterns and see how these will turn out in the future. It’s possible that the organization uses Machine Learning to examine each new pattern that comes out.
The third one refers to descriptive analytics. It centers on the incoming data to produce a description. Many organizations work on the type to answer a question on “what happened?”. It yields priceless information that’s top-notch, a rear-view mirror of the business performance. Some call this type as behavioral analytics.
The last one is diagnostic analytics. This one takes into account events in the past and come up with certain factors that led to the events. Applying the strategy will assist the organizations in formulating scientific actions that need to be taken.
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