Big Data – Basics of Big Data Analytics – Day 18 of 21

In yesterday’s blog post we learned the importance of the various components in Big Data Story. In this article we will understand what are the various analytics tasks we try to achieve with the Big Data and the list of the important tools in Big Data Story.

When you have plenty of the data around you what is the first thing which comes to your mind?

“What do all these data means?”

Exactly – the same thought comes to my mind as well. I always wanted to know what all the data means and what meaningful information I can receive out of it. Most of the Big Data projects are built to retrieve various intelligence all this data contains within it. Let us take example of Facebook. When I look at my friends list of Facebook, I always want to ask many questions such as -

  • On which date my maximum friends have a birthday?
  • What is the most favorite film of my most of the friends so I can talk about it and engage them?
  • What is the most liked placed to travel my friends?
  • Which is the most disliked cousin for my friends in India and USA so when they travel, I do not take them there.

There are many more questions I can think of. This illustrates that how important it is to have analysis of Big Data.

Here are few of the kind of analysis listed which you can use with Big Data.

Slicing and Dicing: This means breaking down your data into smaller set and understanding them one set at a time. This also helps to present various information in a variety of different user digestible ways. For example if you have data related to movies, you can use different slide and dice data in various formats like actors, movie length etc.

Real Time Monitoring: This is very crucial in social media when there are any events happening and you wanted to measure the impact at the time when the event is happening. For example, if you are using twitter when there is a football match, you can watch what fans are talking about football match on twitter when the event is happening.

Anomaly Predication and Modeling: If the business is running normal it is alright but if there are signs of trouble, everyone wants to know them early on the hand. Big Data analysis of various patterns can be very much helpful to predict future. Though it may not be always accurate but certain hints and signals can be very helpful. For example, lots of data can help conclude that if there is lots of rain it can increase the sell of umbrella.

Text and Unstructured Data Analysis: unstructured data are now getting norm in the new world and they are a big part of the Big Data revolution. It is very important that we Extract, Transform and Load the unstructured data and make meaningful data out of it. For example, analysis of lots of images, one can predict that people like to use certain colors in certain months in their cloths.

Big Data Analytics Solutions

There are many different Big Data Analystics Solutions out in the market. It is impossible to list all of them so I will list a few of them over here.

  • Tableau – This has to be one of the most popular visualization tools out in the big data market.
  • SAS – A high performance analytics and infrastructure company
  • IBM and Oracle – They have a range of tools for Big Data Analysis

Tomorrow

In tomorrow’s blog post we will discuss about very important components of the Big Data Ecosystem – Data Scientist.

Reference: Pinal Dave (http://blog.sqlauthority.com)

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