What is Big Data Analytics and How It is Being Used

Big Data is today, the hottest buzzword around, and with the amount of data being generated every minute by consumers, or/and businesses worldwide, there is huge value to be found in Big Data analytics.

Big Data analytics is fueling everything we do online—in every industry.

Take the music streaming platform Spotify for example. The company has nearly 96 million users that generate a tremendous amount of data every day. Through this information, the cloud-based platform automatically generates suggested songs—through a smart recommendation engine—based on likes, shares, search history, and more. What enables this is the techniques, tools, and frameworks that are a result of Big Data analytics.

If you are a Spotify user, then you must have come across the top recommendation section, which is based on your likes, past history, and other things. Utilizing a recommendation engine that leverages data filtering tools that collect data and then filter it using algorithms works. This is what Spotify does.

But, let’s get back to the basics first.

What is Big Data?

Put, Big Data is a massive amount of data sets that cannot be stored, processed, or analyzed using traditional tools.

Today, there are millions of data sources that generate data at a very rapid rate. These data sources are present across the world. Some of the largest sources of data are social media platforms and networks. Let’s use Facebook as an example—it generates more than 500 terabytes of data every day. This data includes pictures, videos, messages, and more.

Data also exists in different formats, like structured data, semi-structured data, and unstructured data. For example, in a regular Excel sheet, data is classified as structured data—with a definite format. In contrast, emails fall under semi-structured, and your pictures and videos fall under unstructured data. All this data combined makes up Big Data.

What is Big Data Analytics?

Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Big Data analytics provides various advantages—it can be used for better decision making, preventing fraudulent activities, among other things.

Let’s look into the four advantages of Big Data analytics:

Risk Management

Use Case: Banco de Oro, a Phillippine banking company, uses Big Data analytics to identify fraudulent activities and discrepancies. The organization leverages it to narrow down a list of suspects or root causes of problems.

Product Development and Innovations

Use Case: Rolls-Royce, one of the largest manufacturers of jet engines for airlines and armed forces across the globe, uses Big Data analytics to analyze how efficient the engine designs are and if there is any need for improvements.

Quicker and Better Decision Making Within Organizations

Use Case: Starbucks uses Big Data analytics to make strategic decisions. For example, the company leverages it to decide if a particular location would be suitable for a new outlet or not. They will analyze several different factors, such as population, demographics, accessibility of the location, and more.

Improve Customer Experience

Use Case: Delta Air Lines uses Big Data analysis to improve customer experiences. They monitor tweets to find out their customers’ experience regarding their journeys, delays, and so on. The airline identifies negative tweets and does what’s necessary to remedy the situation. By publicly addressing these issues and offering solutions, it helps the airline build good customer relations.

The Lifecycle of Big Data Analytics

Now, let’s review the lifecycle of Big Data analytics:

Stage 1 - Business case evaluation - The Big Data analytics lifecycle begins with a business case, which defines the reason and goal behind the analysis.

Stage 2 - Identification of data - Here, a broad variety of data sources are identified.

Stage 3 - Data filtering - All of the identified data from the previous stage is filtered here to remove corrupt data.

Stage 4 - Data extraction - Data that is not compatible with the tool is extracted and then transformed into a compatible form.

Stage 5 - Data aggregation - In this stage, data with the same fields across different datasets are integrated.

Stage 6 - Data analysis - Data is evaluated using analytical and statistical tools to discover useful information.

Stage 7 - Visualization of data - With tools like Tableau, Power BI, and QlikView, Big Data analysts can produce graphic visualizations of the analysis.

Stage 8 - Final analysis result - This is the last step of the Big Data analytics lifecycle, where the final results of the analysis are made available to business stakeholders who will take action.

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Different Types of Big Data Analytics

There are four types of Big Data analytics:

Descriptive Analytics

This summarizes past data into a form that people can easily read. This helps in creating reports, like a company’s revenue, profit, sales, and so on. Also, it helps in the tabulation of social media metrics.

Use Case: The Dow Chemical Company analyzed its past data to increase facility utilization across its office and lab space. Using descriptive analytics, Dow was able to identify underutilized space. This space consolidation helped the company save nearly US $4 million annually.

Diagnostic Analytics

This is done to understand what caused a problem in the first place. Techniques like drill-down, data mining, and data recovery are all examples. Organizations use diagnostic analytics because they provide an in-depth insight into a particular problem.

Use Case: An ecommerce company’s report shows that their sales have gone down, although customers are adding products to their carts. This can be due to various reasons like the form didn’t load correctly, the shipping fee is too high, or there are not enough payment options available. This is where you can use diagnostic analytics to find the reason.

Predictive Analytics

This type of analytics looks into the historical and present data to make predictions of the future. The predictive analytics uses data mining, AI, and machine learning to analyze current data and make predictions about the future. It works on predicting customer trends, market trends, and so on.

Use Case: PayPal determines what kind of precautions they have to take to protect their clients against fraudulent transactions. Using predictive analytics, the company uses all the historical payment data and user behavior data and builds an algorithm that predicts fraudulent activities.

Prescriptive Analytics

This type of analytics prescribes the solution to a particular problem. Perspective analytics works with both descriptive and predictive analytics. Most of the time, it relies on AI and machine learning.

Use Case: Prescriptive analytics can be used to maximize an airline’s profit. This type of analytics is used to build an algorithm that will automatically adjust the flight fares based on numerous factors, including customer demand, weather, destination, holiday seasons, and oil prices.[Source]-https://www.simplilearn.com/what-is-big-data-analytics-article?source=frs_category

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