What Is Big Data Analytics And Why It Is Important?

Big Data has become the buzzword of the moment. Big Data analytics is a great tool to analyze the huge amount of data generated by businesses and consumers around the world.

What’s Big Data Analytics?

Big Data Analytics is a method that extracts meaningful insights. It can reveal hidden patterns, undiscovered correlations, market trends, customer preferences, and market trends. Big Data analytics offers many advantages, including the ability to make better decisions and stop fraudulent activity.

Why are big data analytics so important?

Big Data analytics are driving everything online in today’s digital world-in every industry.

Spotify is one example. Nearly 96 million people use Spotify every day, which generates a lot of data. This information is used to automatically generate suggested songs through a cloud-based platform. The recommendation engine uses likes, shares, search histories, and other data. The Big Data analytics results in the creation of tools, techniques, and frameworks which enable this.

Spotify users will have seen the top recommendation section. It is based on past history, likes, and other factors. It works by using data filtering software that collects data and filters it using algorithms. Spotify is an example of this.

Let’s go back to basics.

What’s Big Data?

Big Data refers to a large amount of data that can’t be stored, processed or analyzed with traditional tools.

There are many data sources today that produce data at an extremely rapid pace. These data sources can be found all over the globe. Social media networks and platforms are some of the most important data sources. Take Facebook for example. It generates over 500 terabytes per day. These data include messages, pictures, videos, and many other things.

There are also different types of data such as unstructured, semi-structured, and structured data. In an Excel spreadsheet, data can be classified as structured data with a specific format. Emails fall under semi-structured while your photos and videos are classified as unstructured. This data is called Big Data.

Big Data Analytics: Examples and Uses

Big Data analytics has many uses. Here are a few illustrations:

– Customer behavior analytics for optimizing customer experiences
– Forecasting future trends to help you make better business decisions
– Marketing campaigns can be improved by learning the best and worst practices.
– How to increase operational efficiency and identify bottlenecks.
– Early detection of fraud and other forms misuse

These are just some examples of Big Data analytics. There are many more. It all comes down to how you use it to improve your business.

Big Data Analytics History

Big Data analytics dates back to the beginning of computing, when data storage and analysis was first possible. Big Data analytics took off only in the late 1990s/early 2000s when businesses began turning to computers to analyze the increasing volumes of data that was being generated.

Big Data analytics is an indispensable tool for all organizations, regardless of size and across many industries. Big Data analytics can provide insights that are unimaginable for organizations.

We can expect even more transformative and amazing Big Data applications as the field of Big Data analytics evolves.

Read more: Aditya Shivam, an alumnus of software engineering, was intrigued by Data Science. Aditya wanted to explore new learning possibilities and then slowly transition into the field of data science. His Simplilearn Big Data Engineer Master’s Project is Shivam’s story.

Big Data Analytics: The Advantages and Benefits

1. Risk Management
Use Case: Banco de Oro is a Phillippine bank that uses Big Data analytics for fraud detection and discrepancies. It uses it to identify root causes and suspects.

2. Innovations and product development
Rolls-Royce’s use case is to analyse the efficiency of engine designs and identify areas for improvement.

3. Faster and better decision making within organizations
Starbucks uses Big Data analytics in strategic decision making To determine whether a certain location is suitable to open a new shop, for example, Starbucks uses Big Data analytics. They will evaluate many factors, including population, demographics and accessibility to the area.

4. Customers Experience Improved
Delta Air Lines makes use of Big Data analytics to improve customer experience. They track tweets to learn about their customers’ experiences regarding delays and journeys. Negative tweets are flagged by the airline, and the airline then takes appropriate action to fix them. These issues are addressed publicly and solutions are offered to improve customer relations.

Big Data Analytics’ Lifecycle Phases

Let’s look at Big Data analytics in action:

Stage 1 Evaluation of the business case – The Big Data analysis lifecycle begins with the development of a business-case, which establishes the purpose and goal for the analysis.
– Stage 2: Identification of data – This stage allows for the identification of a wide range of data sources.
– Stage 3, Data filtering. This stage removes corrupt data.
– Stage 4: Data extraction – Any data that is not compatible is extracted and converted into a compatible format.
– Stage 5 Data Aggregation – This stage integrates data from different datasets.
– Stage 6: Data analysis – Data are analyzed using statistical and analytical tools to find useful information.
– Stage 7 Visualization data – Big Data analysts can use tools such as Tableau and Power BI to create visual visualizations of their analysis.
– Stage 8 Final Analysis Results – This final stage of the Big Data Analytics lifecycle is where business stakeholders will be able to access the analysis’s final results.

Big Data Analytics Types

These are the types of Big Data analytics.

1. Descriptive analytics
This allows you to summarize past data in a format that is easy to understand. This makes it easy to create reports. For example, a company’s revenue and profit. It is also useful in tabulating social media metrics.

Use Case: Dow Chemical Company looked at its past data and decided to increase the facility utilization within its office. Dow was successful in identifying underutilized space through descriptive analytics. This helped Dow save approximately US $4,000,000 annually.

2. Diagnostic Analytics
This helps to identify the root cause of the problem. These techniques include data mining, drill down, and data retrieval. Diagnostic analytics are used by organizations to gain a deeper understanding of a problem.

Use Case: An online company’s report shows a drop in sales, yet customers continue to add products and services to their shopping carts. There could be a variety of reasons, such as the site not loading correctly, excessive shipping costs, or insufficient payment options. Diagnostic analytics can help you pinpoint the problem.

3. Predictive analytics
This type analytics examines the past and future data in order to make predictions about the future. Predictive analytics is a combination of AI, data mining, and machine-learning to analyze and predict the future. It predicts market trends and customer trends.

Use Case: PayPal decides which precautions they need to take to protect clients from fraudulent transactions. The company builds an algorithm using predictive analytics that uses historical payment data as well as user behavior data to predict fraudulent activity.

4. Prescriptive analytics
This type is used to solve a problem. Perspective analytics is a combination of predictive and descriptive analytics. Most often, it depends on AI or machine learning.

Use Case. Prescriptive analytics can help maximize the profit of an airline. This type is used to develop an algorithm that adjusts the flight fares automatically based on many factors, such as customer demand.

Big Data Analytics Tools

These are the top big data analytics tools:
Hadoop is a data storage and analysis tool that helps you store and analyze your data.
MongoDB is used to manage datasets that are frequently changing
Talend is used to integrate data and manage it
Cassandra: A distributed database for handling large data chunks
Spark is used to analyze large data sets and real-time process them.
STORM is an open source real-time computing system.
Kafka – A distributed streaming platform for fault-tolerant storage

The Big Data industry has a variety of applications.
These are just a few of the areas where Big Data is being used.
Ecommerce – Big Data Analytics is used to optimize prices and predict customer trends.
Marketing – Big Data analytics is a powerful tool to increase ROI in marketing campaigns. This results in higher sales.
Education – Develop new or improve existing courses according to market needs
– Healthcare – Big Data analytics can be used to predict the likelihood of patients developing health problems.
– Entertainment and media – Use this to identify the needs of its users and to offer a personalized list of recommendations.
– Banking. Customer income and patterns of spending help predict which offers customers might choose, such as loans or credit card.
Telecommunications – Useful for forecasting network capacity and improving customer experience
– Government Big Data Analytics helps governments with law enforcement, among others

Join The Big Data Analytics Revolution

Data has a profound impact on our daily lives. There is a high demand to be able to understand it. Simplilearn and IBM have partnered to offer Big Data Analytics Bootcamps, which provide information on Big Data analytics.

Simplilearn also has a video on “What’s Big Data Analytics?” that was curated by industry professionals to help you understand the concepts.

What are the most frequently asked questions?
1. What is big-data analytics?
Analytics using big data is the difficult task of analysing large quantities of data to uncover patterns, correlations and market trends. This information can help businesses make educated business decisions.

Companies can use data analytics tools to analyse large amounts of data and get new insights. BI queries can provide fundamental information about company operations, performance, and other aspects. Analytics using big data, which uses statistical algorithms, predictive models, and what-if scenario to analyze complex data, is called Big Data Analytics.

2. What is the importance of big data analytics?
Organizations can harness their data to identify new opportunities and use big data analytics to do so. The result is smarter business decisions that lead to higher profits and happier customers.

3. What is big data analytics?
Collect information. It is important to organize the data once it has been saved. Clean up the data, and then analyze it.

4. Who uses big-data analytics?
Big data analytics is used in many industries, including banking and securities, healthcare providers, communications, media and entertainment, education, government, retail and wholesale trade, manufacturing natural resources, insurance, and other services.

5. What are the 5 types of big-data analytics?
There are five types: Prescriptive Analytics. Diagnostic Analytics. Cyber Analytics.

6. What are the different types of big-data?
There are three types, structured data, unstructured data, and semi-structured data.

7. What are big data’s benefits?
Big data allows businesses to personalize products to their customers and save money on advertising. Big data can be used by businesses to track POS transactions and online purchases in order to understand consumer habits.

8. Big data analytics: Why are we so important?
Companies can harness their data to uncover new opportunities. This helps companies make better decisions, improve their operations and profitability, and makes clients happier. Big data and advanced analytics can be a boon for businesses in many ways.

9. In simple terms, what is big data?
Big data refers to a large and complex collection of data that is difficult or impossible to store or process using traditional data management tools.

10. What does big data analysis mean?
Big data analytics is the complicated process of analysing large amounts of data in order to uncover information like correlations, hidden patterns and market trends.

11. Do I have to pay for big data?
Yes. Simplilearn provides free courses in big-data, from Hadoop through MongoDB to more.

12. Is learning big data worth it?
Because big data skills and investments are increasing exponentially, you will have a wider range of knowledge and competitive advantages.

13. Does big data require coding?
Yes, it is important to learn how code for big data.

14. Is big-data a career that is rewarding?
Your education, your skills, and your position will determine the pay scale. All jobs in big data are highly-paid.
The salaries for big data professionals range from $50,000 to $165,000 annually.