Understanding Big Data Analytics Types and Tools | iCert Global

Big Data is one of the most talked-about topics today. With so much data created every minute, there is a lot to discover through Big Data analysis. People and companies worldwide are generating it.

What is Big Data Analytics?

Big Data analytics finds key info, like hidden patterns, trends, and customer preferences. It helps businesses make better decisions and prevents fraud, among other things.

Why is Big Data Analytics Important?

 Big Data analytics plays a key role in everything we do online, especially in many industries.Take Spotify, for example. It has almost 96 million users who create a huge amount of data daily. Spotify's system uses this data to suggest songs. It looks at your likes, shares, and search history. This is possible because of Big Data tools and techniques. If you're a Spotify user, you’ve probably noticed the song recommendations that appear. These suggestions are based on your past actions and preferences. This system uses algorithms and data filters to recommend things. This is Big Data in action.

Now, let’s understand what Big Data is.

What is Big Data?

Big Data refers to massive sets of data that are too large to be processed with regular tools.Data is being generated from millions of sources worldwide. For example, Facebook's social media sites create over 500 terabytes of data daily. This data includes photos, videos, messages, and more.

Data has three types: structured (like Excel), semi-structured (like emails), and unstructured (like photos and videos). Together, all this data makes up Big Data.

Uses and Examples of Big Data Analytics

Big Data analytics can help businesses in many ways:

• Understanding customer behavior to improve their experience.
• Predicting future trends to make better decisions.
• Improving marketing campaigns by knowing what works.
• Boosting efficiency by identifying issues and fixing them.
• Detecting fraud or misuse earlier.

These are just a few examples of how Big Data analytics can help. The possibilities are endless, depending on how you use the data to improve a business.

History of Big Data Analytics

• Big Data analytics began in the early days of computers. Businesses used them to store and process large amounts of data. However, it wasn’t until the late 1990s and early 2000s that Big Data analytics truly became popular. During this time, businesses saw they needed computers to handle their growing data.
• Today, Big Data analytics is a key tool for organizations of all sizes in many industries. Big Data lets companies understand their customers and their business. They can even understand the world better than before.
• As Big Data continues to grow, we can expect even more incredible ways to use this technology in the future.

Benefits and Advantages of Big Data Analytics

1. Risk Management Example: Banco de Oro, a Philippine bank, uses Big Data to find fraud and other issues. The company uses it to identify possible suspects or problems in their services.

2. Example of Product Development and Innovations: Rolls-Royce makes jet engines. They use Big Data to check their engine designs and see if they need improvements.

3. Example: Starbucks uses Big Data to decide where to open new stores. They consider factors like population, nearby businesses, and access. These help them decide if a location is a good fit.

4. Improving Customer Experience: Delta Air Lines uses Big Data to boost service. They track social media posts to understand how their customers are feeling. By fixing issues, the airline can keep customers happy and build trust.

The Lifecycle Phases of Big Data Analytics

Big Data analytics follows a structured lifecycle to make sense of large datasets. Here are the key stages:

Stage 1: Business Case Evaluation The lifecycle starts by defining the analysis's purpose. This step ensures that the analysis aligns with business goals.

Stage 2: Identification of Data At this stage, a variety of data sources are identified. These sources provide the raw data necessary for analysis.

Stage 3: Data Filtering. The previous stage found some data. This data is filtered to remove any corrupt or irrelevant parts. Only useful information is kept.

Stage 4: Data Extraction Incompatible data is extracted and transformed for analysis tools.

Stage 5: Data Aggregation. Here, we combine data from different datasets with similar fields. This gives a complete view.

Stage 6: Data Analysis. Use statistical tools to analyze data. This uncovers useful insights, trends, and patterns.

Stage 7: Visualization of Data. Tools like Tableau, Power BI, and QlikView create graphs of the analyzed data. This makes the data easy to interpret.

Stage 8: Final Analysis Result In the final stage, we present the results to stakeholders. They will use the insights to make informed decisions.

Different Types of Big Data Analytics

Here are four key types of Big Data analytics:

1. Descriptive Analytics This type summarizes past data to create understandable reports. It helps to understand a company's revenue, profit, or social media performance. Example: Dow Chemical used analytics to optimize office space, saving $4 million a year.
2. Diagnostic Analytics This type is used to understand the cause of problems. It involves techniques like drill-down and data mining to dig deeper into issues. An e-commerce company uses analytics to find why sales have dropped. Many items are being added to the cart.
3. Predictive Analytics Predictive analytics analyzes historical and current data to make future predictions. It uses techniques like AI and machine learning to forecast trends. Example: PayPal uses predictive analytics to stop fraud. It analyzes user behavior and transaction data.
4. Prescriptive Analytics This type recommends solutions to problems. It combines descriptive and predictive analytics and often uses AI to optimize decision-making. Airlines use prescriptive analytics to set flight fares. They adjust prices based on demand, weather, and oil prices to maximize profits.

Big Data Analytics Tools

Some of the important tools used in Big Data analytics include:

• Hadoop – A tool used for storing and analyzing large amounts of data.
• MongoDB – Used for datasets that change frequently.
• Talend – Helps in managing and integrating data from different sources.
• Cassandra – A system for managing large chunks of data across many servers.
• Spark – A tool for real-time data processing and analyzing huge data sets.
• STORM – An open-source tool for real-time data processing.
• Kafka – A system used for storing and processing data in real time.

Big Data Industry Applications

 Here are a few industries where Big Data is used:

• Ecommerce – Predicting customer trends and setting prices based on Big Data.
• Marketing – Helps create marketing campaigns that bring in more sales.
• Education – Used to improve courses based on what the market needs.
• Healthcare – Analyzes patient data to predict health issues they may face.
• Media and Entertainment – It recommends movies, shows, and music based on users' preferences.
• Banking - It predicts which customers will want loans or credit cards, based on their spending.
• Telecommunications – Used to predict the need for network capacity and improve customer service.
• Government – Helps with law enforcement and other important services.

How to obtain Big Data certification? 

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Conclusion

Join the Big Data Analytics Revolution. Data is everywhere. So, there is a growing demand for professionals who can use it. To learn more about Big Data analytics, check out iCert Global's web site It can help you start a career in it.

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