Today, Big Data is the hottest buzzword around. With the amount of data being generated every minute by consumers and businesses worldwide, there is significant value to be found in Big Data analytics.
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.
Why is big data analytics important?
In today’s world, 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?
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.
Let’s look into the four advantages of Big Data analytics.
Also Read: Data Science vs. Big Data vs. Data Analytics
Uses and Examples of Big Data Analytics
There are many different ways that Big Data analytics can be used in order to improve businesses and organizations. Here are some examples:
- Using analytics to understand customer behavior in order to optimize the customer experience
- Predicting future trends in order to make better business decisions
- Improving marketing campaigns by understanding what works and what doesn't
- Increasing operational efficiency by understanding where bottlenecks are and how to fix them
- Detecting fraud and other forms of misuse sooner
These are just a few examples — the possibilities are really endless when it comes to Big Data analytics. It all depends on how you want to use it in order to improve your business.
History of Big Data Analytics
The history of Big Data analytics can be traced back to the early days of computing, when organizations first began using computers to store and analyze large amounts of data. However, it was not until the late 1990s and early 2000s that Big Data analytics really began to take off, as organizations increasingly turned to computers to help them make sense of the rapidly growing volumes of data being generated by their businesses.
Today, Big Data analytics has become an essential tool for organizations of all sizes across a wide range of industries. By harnessing the power of Big Data, organizations are able to gain insights into their customers, their businesses, and the world around them that were simply not possible before.
As the field of Big Data analytics continues to evolve, we can expect to see even more amazing and transformative applications of this technology in the years to come.
Benefits and Advantages of Big Data Analytics
1. 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.
2. 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.
3. 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.
4. 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 Phases of Big Data Analytics
Now, let’s review how Big Data analytics works:
- 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
Here are the four types of Big Data analytics:
1. 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.
2. 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 e-commerce 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.
3. Predictive Analytics
This type of analytics looks into the historical and present data to make predictions of the future. 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.
4. 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.
Big Data Analytics Tools
- Hadoop - helps in storing and analyzing data
- MongoDB - used on datasets that change frequently
- Talend - used for data integration and management
- Cassandra - a distributed database used to handle chunks of data
- Spark - used for real-time processing and analyzing large amounts of data
- STORM - an open-source real-time computational system
- Kafka - a distributed streaming platform that is used for fault-tolerant storage
Big Data Industry Applications
Here are some of the sectors where Big Data is actively used:
- Ecommerce - Predicting customer trends and optimizing prices are a few of the ways e-commerce uses Big Data analytics
- Marketing - Big Data analytics helps to drive high ROI marketing campaigns, which result in improved sales
- Education - Used to develop new and improve existing courses based on market requirements
- Healthcare - With the help of a patient’s medical history, Big Data analytics is used to predict how likely they are to have health issues
- Media and entertainment - Used to understand the demand of shows, movies, songs, and more to deliver a personalized recommendation list to its users
- Banking - Customer income and spending patterns help to predict the likelihood of choosing various banking offers, like loans and credit cards
- Telecommunications - Used to forecast network capacity and improve customer experience
- Government - Big Data analytics helps governments in law enforcement, among other things
1. What is big data in simple words?
Big data is a collection of large, complex, and voluminous data that traditional data management tools cannot store or process.
2. What is the meaning of big data analytics?
Big data analytics refers to the complex process of analyzing big data for revealing information such as correlations, hidden patterns, market trends, and customer preferences.
3. Can I learn big data for free?
Yes. Simplilearn offers free big data courses ranging from hadoop to mongoDB and so much more.
4. Is it worth it to learn big data?
Learning big data will broaden your area of expertise and provide you with a competitive advantage as big data skills are in high demand and investments in big data keep growing exponentially.
5. Is coding required for big data?
Yes, learning how to code is essential for big data.
6. Is big data a good career?
This will depend on your education, skills, and position. Big data jobs overall are very high-paying. Big data salaries range between $50,000 - $165,000 per year.
Want to begin your career as a Big Data Engineer? Check out the Big Data Engineer Training Course and get certified.
Join the Big Data Analytics Revolution
Data touches every part of our lives today, meaning there is a high demand for professionals with the skill to make sense of it. If you want to learn more about Big Data analytics or want to jumpstart your career in Big Data, check out Simplilearn’s Big Data Engineer and Data Analytics Bootcamp in collaboration with IBM today!
Also, check out Simplilearn's video on "What is Big Data Analytics" curated by our industry experts to help you understand the concepts.