What is Big Data Analytics: An Ultimate Guide From Data Scientist

What is Big Data Analytics: An Ultimate Guide From Data Scientist

What is Big Data Analytics: An Ultimate Guide From Data Scientist

Last Edited

Last Edited

February 15, 2024

Feb 15, 2024

Feb 15, 2024

Ali Osman Kaya

Data Scientist

Technical Insights

Technical Insights

7

7

min reading

min reading

We can define ''Big Data Analytics'' as the execution of analytical processes on large and rich datasets. These processes range from simple mathematical operations to complex analyses such as tracking trends, correlations, retention, funnel analysis, and user flow analysis. This allows us to extract insights about our large dataset, discover patterns, and determine prescriptive actions accordingly.

4 Step of Big Data Analytics

A diagram that shows big data analytics 4 steps

Data Collection:

It refers to the collection and processing of raw data from various sources through systems. The raw data collected can be user data, event data, or data collected from sensors. The collection process occurs through various computing systems at specific intervals or triggered events. The collected data is sent to centralized or distributed databases through these systems and processed there.

Data Processing

After the raw data is collected from various sources and stored in databases, it needs to be optimized and organized efficiently. This process is called data processing. Data processing optimizes database capacity, reduces costs, and ensures that data is organized systematically. This makes the analysis process easier to progress.

Data Cleaning

The collected data may contain errors caused by system or human errors. These errors make it difficult for the data analysis process to proceed accurately. Therefore, data cleaning refers to the identification of errors and anomalies, and the elimination of these values using appropriate statistical methods. With cleaned data, we can perform accurate analyses and obtain results.

Data Analysis

In the final step of Big Data Analytics, we perform analyses using cleaned data through statistical methods and data visualization. With these analyses, we can find answers to our questions and capture insights to take actions.

The Importance of Big Data Analytics

With the increase in data collection channels, improvements in data collection processes, and the increase in the processing power and capacities of computers, data sizes have also increased exponentially. This phenomenon, known as Big Data, has directly contributed to the analytical processes conducted by data-collecting organizations. With Big Data, more information about the past and present has been obtained, allowing for better representation of systems or populations. Consequently, companies prefer to base their analytical processes on Big Data Analytics and make decisions accordingly.

On the other hand, with Big Data, artificial intelligence (AI) studies have also gained momentum and progressed to their current state. Especially, subfields of AI such as Machine Learning and Deep Learning have gained momentum and enabled the training of advanced complex models.

Machine Learning involves teaching computers about events through data. This teaching process is done by digitizing and expressing the data numerically. Then, a cause-and-effect relationship is created from the data, and the computer is made to understand this relationship.

This process is also called model training. Afterwards, the model is trained to predict future events based on the learned relationship. The goal of model training is for computers to mimic human intelligence by learning from past data.

One of the most important aspects of model training is data. Data is fuel for the model, and therefore, its quality and volume are crucial. Quality lies in the data being modelable and explainable for the given problem.

Additionally, organizations should clean the data and ensure it is free from errors and anomalies. Another important aspect is the volume of the data. This is where Big Data comes into play and exposes models to a large amount of historical data. Thus, the model can capture patterns in the data and achieve high scores in predicting future events after training.

Tools for Big Data Analytics

Processing Big Data poses many challenges such as the complexity of data storage and management structures, infrastructure inadequacies, and insufficient storage space. Various tools help avoid these challenges and efficiently manage Big Data in databases for analytical processes.

Apache Hadoop: A Java-based data processing and storage platform. With its Hadoop Distributed File System (HDFS) and MapReduce systems, it enables efficient and distributed storage and processing of large data.

Apache Spark: Similar to Apache Hadoop, it is a tool used for processing large data and enabling distributed work. By processing data in memory rather than writing it to disk, Spark allows for faster processing compared to Hadoop.

Apache Cassandra: A NoSQL database solution for large-scale and high-access data sets. It provides structure-independent storage for large data sets and enables distributed and optimized read/write operations for handling Big Data quickly.

Elastic search: Searching Big Data can be challenging. It can take hours or even days to scan all data and find the desired information, slowing down analysis and processing.

Elastic search is a system for searching and analyzing Big Data, providing real-time data analysis, search, and visualization. By representing large data sets across multiple nodes for scalability, it speeds up data access and facilitates obtaining information through filters.

Big Data Use Cases

Today, there is data generation in every industry in various forms. This data can range from sensors monitoring machine operations to customer records in hotel reception systems. Advances in population growth and processor technology have led to significant progress in information processing systems, resulting in an unprecedented increase in data collection sources and volume. In this context, every industry has based its action decision mechanisms on data due to the benefits provided by the use of these data.

These industries include healthcare, technology, aviation, manufacturing, and banking.

Healthcare:

With the widespread use of medical devices and the data obtained from these devices, the course of diseases can be monitored daily. In fact, models trained with this data can predict how a disease will progress in the coming days. Moreover, by maintaining patient records, personalized treatment methods can be achieved by better understanding the patient. Additionally, Deep Learning models are used in the discovery of new drugs, opening the way for new treatment methods.

Technology:

One of the biggest problems for technology companies is customer churn. In this regard, by subjecting customer data to analytical processes, companies can better understand their customers and make campaign and action processes accordingly.

Aviation:

Airlines optimize many aspects of their operations using Big Data obtained from the systems they manage.

Airlines analyze factors like day, time, and conditions to make flights more efficient. This is called flight optimization. To improve, you can analyze the needs of each flight and adjust the food and drink supplies accordingly. This will help reduce waste and ensure that there is enough stock.

Related blog: The Ultimate Guide to Reduce Cost in the Aviation Industry

Manufacturing:

In the manufacturing industry, where production is at the forefront, the most efficient working plans for machines and workers can be created, and high returns can be achieved with minimum investments.

Big Data obtained from sensors monitoring machine operations and workers' activities can be analyzed to identify and rectify operational flaws and errors, create schedules, and even identify areas for investment to increase productivity.

Banking:

In the banking industry, where large amounts of money are managed and the smallest mistake can have significant consequences, Big Data Analytics is paramount. By tracking all transactions through systems and advancing analytical processes in banks where Big Data is generated, all action plans are executed in a data-centric manner.

One such process is fraud detection, where all bank transactions are monitored, and if an anomaly-containing transaction is detected, the bank takes action to prevent possible fraud.

Conclusion

Big Data Analytics is a powerful tool. It helps industries use large and diverse datasets. It helps them gain insights, make decisions, and improve operations.

As we collect more data and develop better tools, we are witnessing the growing possibilities for using Big Data. Big Data Analytics is used in various industries like healthcare and banking to improve treatments, customer experiences, operations, and risk management.

As organizations navigate the complexities of the digital age, embracing Big Data Analytics becomes not just a competitive advantage but a strategic imperative for driving innovation and sustainable growth in today's data-driven world.

We can define ''Big Data Analytics'' as the execution of analytical processes on large and rich datasets. These processes range from simple mathematical operations to complex analyses such as tracking trends, correlations, retention, funnel analysis, and user flow analysis. This allows us to extract insights about our large dataset, discover patterns, and determine prescriptive actions accordingly.

4 Step of Big Data Analytics

A diagram that shows big data analytics 4 steps

Data Collection:

It refers to the collection and processing of raw data from various sources through systems. The raw data collected can be user data, event data, or data collected from sensors. The collection process occurs through various computing systems at specific intervals or triggered events. The collected data is sent to centralized or distributed databases through these systems and processed there.

Data Processing

After the raw data is collected from various sources and stored in databases, it needs to be optimized and organized efficiently. This process is called data processing. Data processing optimizes database capacity, reduces costs, and ensures that data is organized systematically. This makes the analysis process easier to progress.

Data Cleaning

The collected data may contain errors caused by system or human errors. These errors make it difficult for the data analysis process to proceed accurately. Therefore, data cleaning refers to the identification of errors and anomalies, and the elimination of these values using appropriate statistical methods. With cleaned data, we can perform accurate analyses and obtain results.

Data Analysis

In the final step of Big Data Analytics, we perform analyses using cleaned data through statistical methods and data visualization. With these analyses, we can find answers to our questions and capture insights to take actions.

The Importance of Big Data Analytics

With the increase in data collection channels, improvements in data collection processes, and the increase in the processing power and capacities of computers, data sizes have also increased exponentially. This phenomenon, known as Big Data, has directly contributed to the analytical processes conducted by data-collecting organizations. With Big Data, more information about the past and present has been obtained, allowing for better representation of systems or populations. Consequently, companies prefer to base their analytical processes on Big Data Analytics and make decisions accordingly.

On the other hand, with Big Data, artificial intelligence (AI) studies have also gained momentum and progressed to their current state. Especially, subfields of AI such as Machine Learning and Deep Learning have gained momentum and enabled the training of advanced complex models.

Machine Learning involves teaching computers about events through data. This teaching process is done by digitizing and expressing the data numerically. Then, a cause-and-effect relationship is created from the data, and the computer is made to understand this relationship.

This process is also called model training. Afterwards, the model is trained to predict future events based on the learned relationship. The goal of model training is for computers to mimic human intelligence by learning from past data.

One of the most important aspects of model training is data. Data is fuel for the model, and therefore, its quality and volume are crucial. Quality lies in the data being modelable and explainable for the given problem.

Additionally, organizations should clean the data and ensure it is free from errors and anomalies. Another important aspect is the volume of the data. This is where Big Data comes into play and exposes models to a large amount of historical data. Thus, the model can capture patterns in the data and achieve high scores in predicting future events after training.

Tools for Big Data Analytics

Processing Big Data poses many challenges such as the complexity of data storage and management structures, infrastructure inadequacies, and insufficient storage space. Various tools help avoid these challenges and efficiently manage Big Data in databases for analytical processes.

Apache Hadoop: A Java-based data processing and storage platform. With its Hadoop Distributed File System (HDFS) and MapReduce systems, it enables efficient and distributed storage and processing of large data.

Apache Spark: Similar to Apache Hadoop, it is a tool used for processing large data and enabling distributed work. By processing data in memory rather than writing it to disk, Spark allows for faster processing compared to Hadoop.

Apache Cassandra: A NoSQL database solution for large-scale and high-access data sets. It provides structure-independent storage for large data sets and enables distributed and optimized read/write operations for handling Big Data quickly.

Elastic search: Searching Big Data can be challenging. It can take hours or even days to scan all data and find the desired information, slowing down analysis and processing.

Elastic search is a system for searching and analyzing Big Data, providing real-time data analysis, search, and visualization. By representing large data sets across multiple nodes for scalability, it speeds up data access and facilitates obtaining information through filters.

Big Data Use Cases

Today, there is data generation in every industry in various forms. This data can range from sensors monitoring machine operations to customer records in hotel reception systems. Advances in population growth and processor technology have led to significant progress in information processing systems, resulting in an unprecedented increase in data collection sources and volume. In this context, every industry has based its action decision mechanisms on data due to the benefits provided by the use of these data.

These industries include healthcare, technology, aviation, manufacturing, and banking.

Healthcare:

With the widespread use of medical devices and the data obtained from these devices, the course of diseases can be monitored daily. In fact, models trained with this data can predict how a disease will progress in the coming days. Moreover, by maintaining patient records, personalized treatment methods can be achieved by better understanding the patient. Additionally, Deep Learning models are used in the discovery of new drugs, opening the way for new treatment methods.

Technology:

One of the biggest problems for technology companies is customer churn. In this regard, by subjecting customer data to analytical processes, companies can better understand their customers and make campaign and action processes accordingly.

Aviation:

Airlines optimize many aspects of their operations using Big Data obtained from the systems they manage.

Airlines analyze factors like day, time, and conditions to make flights more efficient. This is called flight optimization. To improve, you can analyze the needs of each flight and adjust the food and drink supplies accordingly. This will help reduce waste and ensure that there is enough stock.

Related blog: The Ultimate Guide to Reduce Cost in the Aviation Industry

Manufacturing:

In the manufacturing industry, where production is at the forefront, the most efficient working plans for machines and workers can be created, and high returns can be achieved with minimum investments.

Big Data obtained from sensors monitoring machine operations and workers' activities can be analyzed to identify and rectify operational flaws and errors, create schedules, and even identify areas for investment to increase productivity.

Banking:

In the banking industry, where large amounts of money are managed and the smallest mistake can have significant consequences, Big Data Analytics is paramount. By tracking all transactions through systems and advancing analytical processes in banks where Big Data is generated, all action plans are executed in a data-centric manner.

One such process is fraud detection, where all bank transactions are monitored, and if an anomaly-containing transaction is detected, the bank takes action to prevent possible fraud.

Conclusion

Big Data Analytics is a powerful tool. It helps industries use large and diverse datasets. It helps them gain insights, make decisions, and improve operations.

As we collect more data and develop better tools, we are witnessing the growing possibilities for using Big Data. Big Data Analytics is used in various industries like healthcare and banking to improve treatments, customer experiences, operations, and risk management.

As organizations navigate the complexities of the digital age, embracing Big Data Analytics becomes not just a competitive advantage but a strategic imperative for driving innovation and sustainable growth in today's data-driven world.

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