Big Data is one of the most popular buzzwords in the IT industry today. Big Data analytics have many applications worldwide, and as technology evolves, Big Data’s impact will amplify across different sectors even further.

A study by McKinsey Global Institute revealed that there was a shortage of almost 1,90,000 data scientists and 1.5 million managers and analysts who could make sense of the Big Data that companies were collecting in the US alone. The study was conducted back in 2018, and the number has grown exponentially today. There are many jobs and growth opportunities in the field of Big Data analytics that you can capitalise on to advance your career. But, let’s answer a few big questions.

What is Big Data?

In today’s digital era, Big Data is the largest asset a business can own. But this data cannot be processed, stored or analysed with traditional tools. For a large corporation, millions of data sources around the world generate data at a very high rate. Social media platforms and networks are among the largest sources of this data. Consider Facebook, which produces over 500 TB of data every single day, consisting of pictures, videos, messages and more.

Data is also generated in different formats, such as structured data, semi-structured data and unstructured data. An Excel sheet is a good example of structured data generation. All the data is stored in a specified format. Emails can be categorised as semi-structured, while images and videos fall under unstructured data. All these different types of data combine to form Big Data.

The start of the Big Data revolution

Researchers and IT experts began to understand the role Big Data would play, long before Big Data came into existence. In 1944, Fremont Rider predicted an ‘information explosion’ in the years to come, based on his observation of the Yale Library. He speculated that by 2040, over 6,000 miles of shelves would be needed for all the volumes published till then.

In 2000, Francis Diebond presented a paper where he explicitly linked the term ‘Big Data’ to the way it is used today. Big Data was used to refer to the explosion in the quantity of available and relevant data due to unprecedented advancements in data recording and storage technology.

In 2005, Yahoo used Hadoop to process petabytes of data. Apache Software Foundation then made this data open-source. It was the year the Big Data revolution truly began.

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Why is Big Data Analytics Important?

Big Data analytics is used in pretty much every online interaction. From purchasing a new phone online to searching for something on Google to simply liking an image on your social media feed — it is used in every industry. Big Data analytics applies to real-time fraud detection, complex competition analysis, call centre optimisation, consumer sentiment analysis, intelligent traffic management and smart power grid management.

Three factors primarily characterise Big Data:

1. Volume – The amount of data that is too much to handle traditionally.
2. Velocity – The speed at which new data is generated and collected that makes it difficult to analyse.
3. Variety – The various types of data collected which are too great to assimilate.

Big Data can provide richer insight with the right kind of analytics since it leverages multiple sources of information to help identify patterns.

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Types of Big Data Analytics

Big Data analytics can be classified into four categories:

1. Descriptive Analytics

Descriptive analytics can be valuable while exploring and uncovering patterns that offer insight. They take collected data from the past and summarise it into a form that people can read and understand easily. It is crucial while creating reports about a company’s revenue, profit, sales etc. It also helps tabulate social media metrics.

One important use of descriptive analytics is to assess the credit risk of an individual or organisation. Determining a person’s creditworthiness requires going through their past data and identifying a borrowing or spending pattern. This then needs to be analysed to find out the level of risk involved in giving credit to this third party.

2. Diagnostic Analytics

Diagnostic analytics are used to determine the root cause of a problem, why something happened. A few techniques used in diagnostic analytics are drill-down, data mining and data recovery. Organisations use them to get in-depth insights into a particular problem.

E-commerce websites and social media platforms make use of diagnostic analytics regularly. Consider a brand that is seeing low sales on an e-commerce site for two months. There could be multiple reasons for this - their ad was not being seen on social media, the website interface was faulty, too many steps in the buying process, the cost too high and many more. Diagnostics analytics helps businesses identify where the problem lies so that they can fix it.

3. Predictive Analytics

Predictive analytics involves going through past and present data to find patterns and predict the future. This is a fundamental functionality with applications in Machine Learning (ML) and Artificial Intelligence (AI).

Well tuned predictive analytics are used to support sales, marketing and other types of complex business forecasts. Large companies also use them for sales lead scoring. IT giants and other MNCs use predictive analytics for the entire sales process to analyse the lead source, number and type of communications, social media, documents, CRM information, etc.

4. Prescriptive Analytics

Prescriptive analytics is highly valued but not really used. While Big Data gives you comprehensive information on a subject through various data points, prescriptive analytics give highly focused answers to particular questions.

There are many causes of obesity. By leveraging prescriptive analytics, the healthcare industry can determine how many of them are due to a poor lifestyle that can be fixed easily through exercise and diet. This will mean taking into account the entire population of overweight patients, eliminating those with more severe conditions such as thyroid and diabetes and then focusing on the others.

Features of Big Data

Big Data analytics help organisations curate their data and use it to generate new opportunities and insights. It leads to better business decisions that help you achieve improved efficiency and your business goals in a way that maximises profits. Tom Davenport, IIA Director of Research, interviewed more than fifty businesses to know how they use Big Data. He published his findings in his report Big Data in Big Companies. Here’s how Big Data analytics add value:

1. Cost reduction: Big Data technologies such as Hadoop and other cloud-based analytics enable businesses to achieve much higher cost-efficiency. They reduce the costs incurred in storing huge amounts of data and also help identify more efficient ways of carrying out business operations.
2. Faster decision-making: Big Data analytics allow businesses to analyse information and make decisions almost immediately based on developed insights. The speed of Hadoop and in-memory analytics combine with businesses’ ability to analyse new sources of data for better decision-making.
3. New products and services: Davenport pointed out that more businesses can create new products to cater to changing customer needs with Big Data analytics. Analytics gives companies the ability to gauge customer needs and deliver what they want exactly.

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How Big Data Analytics works?

In some instances, Hadoop clusters and NoSQL systems are used as landing pads and staging areas for data. After that, it gets loaded into a data warehouse or an analytical database for analysis. It is usually stored in a summarised form that is more conducive to relational structures.

Analytics is carried out in different stages:

Stage 1: Business case evaluation – This stage defines the reason and objective of the analysis called the business case.

Stage 2: Identification of data – A wide range of data sources are identified at this stage.

Stage 3: Data filtering – All the data identified in the previous stage is filtered for corrupt data removal.

Stage 4: Data extraction – Data is extracted and transformed into a form that is compatible with the analysis tool.

Stage 5: Data aggregation – Data with the same fields across different datasets are integrated with this stage.

Stage 6: Data analysis – This is the main stage of the process. Data is measured and analysed using statistical and analytical tools to derive insights that can be used for better decision-making.

Stage 7: Visualisation of data – Once the data has been analysed, the result comes out in statistics and figures that can be visualised. Big Data analysts create graphic visualisations of the data using tools such as Tableau and Power BI.

Stage 8: Final analysis result - This is the last step of the Big Data analytics lifecycle. It’s the stage where the final results are tabulated and presented to the business stakeholders for deciding subsequent steps.

Use of Big Data Analytics

Analytics is a top-most priority for almost every major business organisation across the globe. A typical example of Big Data analytics from everyday life is the Spotify algorithm. It observes your usage patterns and suggests music that you might like. But not only that, there are many applications of Big Data in several industries such as:

• Financial sector
• Healthcare industry
• Transportation industry
• Government sector
• Retail marketing
• Social media marketing
• IT sector

No matter how advanced technology gets, analytics tools cannot replace the need for human intervention or insight derivation. As technology evolves, the demand for trained professionals and analysts is also snowballing. Give your career a new direction as a Big Data analyst and enrol in a training course on Koenig today.

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