What is Big data?
Big data is a tremendous amount of data sets that cannot be stored, processed, or analyzed using traditional tools.
Today, millions of data sources generate data at a swift pace. These data sources are present all over the world. Some of the most significant data sources are social media platforms and networks. Let’s use Facebook as an example – it generates more than 500 terabytes of data daily. This data includes images, videos, messages, and more.
Data also exists in different formats, such as structured, semi-structured, and unstructured data. All these data together constitute big data.
History of Big data analysis
The history of big data analytics is from the early days of computing, when organizations started using computers to store and analyze large huge of data. But big data analytics didn’t take off until the late 1990s and early 2000s, when organizations massively turned to computers to help them make sense of the rapidly growing volumes of data they generated. By controlling the power of big data, organizations can gain insight into their customers, businesses, and the world around them.
Now the field of big data analytics continues to evolve, we can expect even more unique and various applications of big data analytics in the future.
How does Big Data Analytics work?
Analysts, predictive modelers, data scientists, statisticians, etc., collect raw data from various sources, process and clean this data using data analysis techniques into formats understood by computing systems, and then analyze this massive volume of data, which is structured transactional data. Analyze it for helpful information using algorithms that contain ML and AI. The big data analysis process usually has 4 phases.
Data Collection: Raw or uncleaned data is a collection of big data from heterogeneous sources and can be structured, semi-structured, or unstructured. Some sources are internet data, web servers, cloud and mobile applications, and social media like Facebook, Instagram, etc.
Data processing: Data is cleaned from data warehouses, configured, formatted, organized, partitioned, etc., in a computational language and ready for analytical queries.
Data Cleansing: This is where data is cleaned using enterprise software and/or scripting software, and inconsistencies, errors, duplications, and formatting errors are addressed.
Data Analysis: The data is then analyzed using tools such as relational data mining tools, ML deep learning tools, predictive tools used in modeling, BI, AI, visualization tools, etc., in what is Big Data Analytics.
Importance of big data analysis
Big data analysis is truly a revolution in information technology. The primary focus of companies is on customers. Therefore, this area is booming in Business Consumer (B2C) applications. We divide analytics into different types according to the nature of the environment. We have three big data analytics divisions: Prescriptive Analytics, Predictive Analytics, and Descriptive Analytics. This area offers enormous potential; discuss four perspectives to explain why big data analytics is so important today?
Big Data Analytics and Data Sciences
Analytics involves applying advanced techniques and tools to data from various sources. Big data has a wide variety, volume, and speed characteristics. Data files come from various online networks, websites, audio and video devices, social media, logs, and many other sources.
Big data analysis involves using analytical techniques such as machine learning, data mining, natural language processing, and statistics. Data is extracted and prepared to provide analytics for businesses. Large businesses and multinational organizations use these techniques in various ways.
Data analytics includes qualitative and quantitative techniques to improve business productivity and profits.
Special analytical tools use these techniques to analyze data sources for fresh insights. The data is typically real-time data produced at a significant scale. Big data and business analytics tools are, therefore, very advanced.
There are two types of these tools: Storage and Analysis and Big Data Analytics. These tools include Apache Hadoop, Hive, etc.
Enterprises and Big Data Analytics
As the use of big data in enterprises increases, the demand for big data analysis tools and techniques is increasing. Organizations can find new opportunities and gain insights to effectively manage their business. These tools help provide meaningful information for better business decisions.
Companies can improve their strategies by keeping customer focus in mind. Big data analytics effectively help streamline operations. This helps to increase the profits of the company.
Big data analytics tools like Hadoop help reduce storage costs. Data analysis is becoming more accessible and faster with the latest analytical tools. This, in turn, leads to faster decision-making, saving time and energy.
Many tools, technologies, and frameworks are used to support big data analytics. The most important of these is enhancing analytical techniques.
-
Hadoop is an open-source processing and storage framework widely used for structured and unstructured big data processing and big data applications.
-
Spark open-source cluster computing framework used for data streaming and batch data processing.
-
In-memory data structure for data distribution.
-
Predictive analytics software and hardware that uses ML, AI, statistical algorithms, etc., on complex data to provide predictions, forecasts, etc., used in marketing, risk management, fraud detection, and more.
-
NoSQL databases, data lakes, and data warehouses are used to store raw data.
-
Stream analytics tools aggregate, analyze, filter, and later store big data across platforms and in various formats.
-
Data mining and knowledge discovery tools.
-
Data for distributed storage typically comes from a non-relational database and is replicated to prevent data corruption, node failures, etc.
-
Big Data analytics tools for data virtualization, integration, quality, preprocessing, software, etc.
Applications of Big Data Analytics
-
Improved decision-making in the data analysis process.
-
Customer retention, service, and retention.
-
Product and process development.
-
Target markets, strategies, and ads.
-
Optimization of costs and prices.
-
Risk reduction and safety management.
-
Supply chain, trade, and channel analysis.
Advantages of Big data Analytics
-
Social data from various websites and search engines like Twitter, Facebook, etc., help with Big Data benefits like customer retention, product decision making, and marketing or business strategy from data insights.
-
Business Intelligence is readily available from insights, forecasts, and forecast formulas.
-
Customer service issues can be quickly resolved using NLP (natural language processors) capabilities to assess customer satisfaction, resolve problems, provide simple product information, etc.
-
Better operational efficiency results are achieved when vast volumes of data are well analyzed and used to fine-tune products. Services, mitigate risks, security issues, etc.
-
Early identification of errors reduces the risk to the offered services or products.
-
Big data analytics create data warehouses to integrate multiple sources, technologies, and processes using data differently.
