What is Big Data Analytics and Why It is Important?

What is Big Data Analytics, and how does it work? Big Data analytics is a strategy for mining vast volumes of data for hidden patterns, undiscovered correlations, market trends, and customer preferences. Big Data analytics has various benefits, including the capacity to improve decision-making and eliminate fraudulent behaviour.

What is the significance of big data analytics?

Big Data analytics is powering everything we do online today, in every business.

Take, for example, the music streaming service Spotify. Every day, the company's almost 96 million users generate a massive amount of data. The cloud-based platform uses this data to generate suggested music based on likes, shares, search history, and more, using a clever recommendation engine.

If you use Spotify, you've probably come across the top recommendation area, which is based on your preferences, previous listening history, and other factors. It is possible to use a recommendation engine that uses data filtering technologies to collect data and then filter it using algorithms. Spotify is a company that specializes in this type of service.

But first, let's go over the essentials.

What is Big Data, exactly?

Big Data refers to large amounts of data that can't be stored, processed, or evaluated with conventional technologies.

There are millions of data sources today that provide data at a quick rate. These data sources can be found all around the world. Social media platforms and networks are some of the most important data sources. Consider Facebook, which creates more than 500 terabytes of data per day. Pictures, videos, texts, and other types of information are included in this data.

Data is available in several formats, including structured, semi-structured, and unstructured data. Data in a standard Excel sheet, for example, is classified as structured data—that is, data with a defined format. Email, on the other hand, is considered semi-structured data, and photographs and videos are considered unstructured data. Big Data is the sum of all of this data.

Big Data Analytics Benefits and Advantages

1. Risk Management

Use Case: The Philippine financial business Banco de Oro use Big Data analytics to prevent fraud and irregularities. The organisation utilises it to cut down a list of suspects or fundamental causes of problems.

Also Read: Risk Management Certification Courses

2. Innovative ways of developing product

Use Case: Rolls-Royce, one of the world's largest manufacturers of jet engines for airlines and military services, using Big Data analytics to evaluate the effectiveness of engine designs and decide whether changes are necessary.

3. Faster and efficient organizational decision-making

For example, Starbucks uses Big Data analytics to make strategic decisions. The corporation, for example, utilises it to decide whether or not a given location is suitable for a news outlet. They will consider a number of factors, including population, demographics, geographical accessibility, and others.

4. Improved Customer Service

Delta Air Lines uses Big Data analysis to improve customer experiences. They read Twitter to learn about their clients' travel plans, delays, and other issues. The airline monitors negative tweets and takes proper corrective measures. It assists the airline in establishing positive customer relations by publicly addressing these concerns and suggesting solutions.

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The Big Data Analytics Lifecycle Phases

Let's have a look at how Big Data analytics works:

Stage 1- The Big Data analytics lifecycle begins with the examination of a business case, which specifies the reason and goal for the investigation.

Stage 2 - Data Source Identification - A wide range of data sources are identified in this stage.

Stage 3 - Data Filtering - All of the previously detected data is filtered here to remove any corrupt data.

Data that isn't compatible with the tool is extracted and then changed into a format that is.

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

Stage 6 - Data analysis - To identify important information, data is reviewed using analytical and statistical methods.

Stage 7: Data Visualization - Big Data analysts can create graphic visualizations of their analysis using technologies like Tableau, Power BI, and QlikView.

Stage 8 - Final analysis result - This is the final stage of the Big Data analytics lifecycle, in which the analysis' final results are made available to business stakeholders for action.

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Big Data Analytics Comes in a Variety of Forms

The four categories of Big Data analytics are as follows:

1. Analytical Descriptive

This summarises earlier information in an easy-to-understand way. This helps to create reports such as a company's income, profit, and sales, among other things. It also helps with the calculation of social media analytics.

Use Case: The Dow Chemical Company examined historical data to improve facility use in its offices and laboratories. Dow was able to discover underused space using descriptive analytics. The corporation was able to save roughly $4 million per year as a result of the space consolidation.

2. Analytical Diagnostics

This is done to determine what caused the problem in the first place. Techniques include drill-down, data mining, and data recovery. Businesses utilise diagnostic analytics because they give a deep insight of an issue.

Use Case: A report from an e-commerce company shows that sales have decreased, despite users adding items to their shopping carts. This could be due to a variety of factors, including a form that did not load successfully, a shipping price that is too expensive, or a lack of payment choices. This is where diagnostic analytics can help you figure out what's going on.

Also Read Top Providers Offering Big Data Analytics Certification Courses

3. Predictive Analytics

This type of analytics evaluates both historical and current data to make predictions. Predictive analytics uses data mining, artificial intelligence, and machine learning to analyse current data and produce predictions. It forecasts customer and market behaviour, among other factors.

Use Case: PayPal evaluates what safeguards they need to put in place to protect their customers from fraudulent transactions. The company employs predictive analytics to construct an algorithm that detects fraudulent activity based on all historical payment data and user behavior data.