Enormous amount of data is being generated everyday, as companies focus on gathering information from users to provide a customized experience. Almost every website and app collects data today, whether it is in the form of inputs by users, or through indirect modes like cookies.

In 2018, Forbes reported that 2.5 quintillion bytes of data was being generated every single day, that amounts to 2.5 billion GB data per day. The pace at which the sector is increasing, this value is expected to be definitely around 5 quintillion bytes today. When there is so much data which needs to be processed, new methodologies need to be evoked. Today, the IT sector has developed the methods of Big Data Analytics to deal with this gargantuan challenge.

Big Data helps in developing ways to extract information from humongously large sets of data, which otherwise cannot be comprehended by conventional data solving methods.  It has emerged as a relief for companies which want to tap the potential of its large user base and also service them efficiently.

As per the Allied Market Research survey report, the Big Data industry is expected to reach $684 billion by 2030, growing at a Cumulative Annual Growth Rate (CAGR) of 13.5%. So let’s find out more about what this Big Data exactly is and why it is creating so much buzz everywhere around.

What is Big Data?

As the name goes, Big Data refers to the large chunk of data that is being generated every now and then. This data comes from multiple sources like Social Media handles, mobile applications, websites, online databases, IoT devices, etc. This data can be in any form, either Structured, Semi-structured or Unstructured.

This enormous data is of no use to a person if no proper analysis is performed on it to generate insights. That is why companies hire Big Data Analysts who can put this giant data to its actual use. Big Data Analysis can be performed in 4 major ways: Descriptive, Diagnostic, Predictive and Prescriptive.

1. Descriptive Analytics: This is the kind of analytics where already existing datasets are analysed and thus reports are generated to make that data comprehensible for the general audience.
2. Diagnostic Analysis: This is the kind of analytics where an analyst tries to find out the cause of a problem. Actions like Data Mining and Data Recovery fall in this category.
3. Predictive Analysis: This is the most common use of data, where analysts try to understand historic patterns and thus predict future actions. With the help of this, the companies target customers by focusing on what they prefer to buy. Predictive analysis is the perfect way for organizations to increase their revenue and sales.
4. Prescriptive Analysis: A combination of Descriptive and Predictive analysis, Prescriptive analysis helps in decision making. It analyses data and prescribes solutions for a particular problem or topic of concern.

Why is Big Data Analytics Important?

We have identified various kinds of analysis that can be performed on Big Data. Now let’s read about why this analysis is important and how it has been making life easier for multiple people.

Saves Cost and Time

• Big Data analysis is really helpful in the case of saving time and costs.
• In-memory analytics is done in real time, thus helping professionals in making quick decisions.
• Also, since the data is processed at a faster pace and provides quick results, it also helps an organization in saving a lot of costs.

Understanding Audience

• The major use of Big Data analytics is to gather customer insights in a better way.
• Companies like Amazon, Spotify, Apple, etc. assess previous behaviour of customers and thus give suggestions accordingly.
• When there are million users surfing on your website and you want to cater to all of them, then Big Data is your only go-to solution.

Better Acquisition and Retention

• Once you get to understand the preferences of your customers, you can serve them in a better way.
• Companies use the knowledge of audience behaviour to show them products which they might get tempted towards. This is a clearly evident strategy of websites like Amazon, Flipkart, Zomato and Swiggy.
• By offering customers what they want, companies move towards a substantial increase in their revenue.

Which are Some of the Best Big Data Analytics Tools?

There are multiple tools and softwares through which analysts can work on Big Data. Some of the popular tools used by Big Data Analysts are:

• Hadoop
• Spark
• Talend
• Cassandra
• Cloudera
• Qubole
• Pentaho
• Kafka
• MongoDB
• Storm
• Stats iQ
• CouchDB

The amazing thing is that most of these tools are open source, thus boosting learning and innovation.

What Skills are Required to Become a Big Data Analyst?

The skills of a Big Data Analyst are almost similar to that of a normal Data analyst, except a few additional skills which they need to learn. The bullets below talk about these skills in detail.

• SQL: Sequel or any other query language forms an essential component of all the new world IT concepts. It helps in managing large databases and helps you to get through your tasks easily.
• Excel: A very basic concept for someone already skilled in SQL, excel is still the top most searched and inquired skill in any professional. No matter where you go, Excel will always keep your back.
• Python: Python or any other script language is super essential for your analysis.
• Data Visualization: Even if you draw insights from data which you can understand, you need to present it in easy form which others can also interpret. Data Visualizations helps you in drawing graphs and charts to make data interactive.
• Machine Learning: Now this is a skill which a normal Data Analyst can skip, but not a person working on Big Data. When the data set is enormously huge, you need more than 2 hands and 1 brain to perform over demanding tasks.

Today, Big Data finds applications across multiple sectors and industries and is being exploited for good reasons. Some of the sectors where Big Data is already being used are e-Commerce, Marketing, Healthcare, Education, IT, Manufacturing, Banking, Governments, Social Communication, Telecommunication, Security Management, etc.

If you are also interested in becoming a Big Data Analyst, it is best to join an online course from educational websites like Simplilearn, Cousera, Udemy, edX, etc. These courses impart essential skills at a minimal cost, with some basic courses coming even free of cost.