Big Data and Data Analytics Class 12 Notes

Big Data refers to huge and complex datasets that regular computer programs and databases cannot handle with any tools. In Big Data and Data Analytics Class 12 Notes we are going to learn what big data is, types of big data, advantages and disadvantages of big data, etc.

Big Data and Data Analytics Class 12 Notes

What is Big Data?

To understand Big Data, let us first understand small data.

• Small Data: Small data refers to datasets that are easily comprehensible by people as they are easily accessible, informative, and actionable. This will help the business to make useful information and make better choices in everyday tasks. For example, a small store might track daily sales to decide what products to restock.
• Big Data: Big Data refers to extremely large and complex datasets that regular computer programs and databases cannot handle. It comes from three main sources: transactional data (online purchases), machine data (sensor readings), and social data (social media posts). To analyze and use Big Data effectively, special tools and techniques are required. For example, companies like Amazon and Netflix use Big Data to recommend products or shows based on users’ past activities.

Types of Big Data

There are three different types of data:

a. Structured Data
b. Semi Structured Data
c. Unstructured Data

Advantages and Disadvantages of Big Data:

Advantage

• Enhanced Decision Making: Organisations can make data-driven decisions based on the insights derived from big data.
• Improved efficiency and productivity: Big data analysis can help the organisation to improve the efficiency and productivity of the business.
• Better Customer Insights: Big data can help the organisation to gain a deeper understanding of customer behaviour, preferences, and needs.
• Competitive Advantage: Leveraging big data analytics provides organisations with a competitive edge by enabling them to uncover market trends, identify opportunities, and stay ahead of competitors.
• Innovation and Growth: Big Data fosters innovation by facilitating the development of new products, services, and business models based on insights derived from data analysis, driving business growth and expansion.

Disadvantages

• Privacy and Security Concerns: It is difficult to collect, store and analysis the big data. Managing big data is also having a security and privacy concerns due to unauthorized access, data breaches, and misuse of personal information.
• Data Quality Issues: Ensuring the accuracy, reliability, and completeness of data can be challenging, as Big Data often consists of unstructured and heterogeneous data sources, leading to potential errors and biases in analysis.
• Technical Complexity: Implementing and managing Big Data infrastructure and analytics tools require specialized skills and expertise, leading to technical challenges and resource constraints for organizations.
• Regulatory Compliance: Organizations face challenges in meeting data protection laws like GDPR (General Data Protection Regulation) and The Digital Personal Data Protection Act, 2023. These laws require strict handling of personal data, making compliance essential to avoid legal risks and penalties.
• Cost and Resource Intensiveness: The cost of acquiring, storing, processing, and analyzing Big Data, along with hiring skilled staff, can be high. This is especially challenging for smaller organizations with limited budgets and resources.

Characteristics of Big Data

The “characteristics of Big Data” refer to the defining attributes that distinguish large and complex datasets from traditional data sources. These characteristics are commonly described using the “3Vs” framework: Volume, Velocity, and Variety. The 6Vs framework provides a holistic view of Big Data, emphasizing not only its volume, velocity, and variety but also its veracity, variability, and value.

• Velocity: Velocity refers to the speed at which data is generated, delivered, and analyzed. For example: Google alone generates more than 40,000 search queries per second.
• Volume: Every day a huge volume of data is generated as the number of people using online platforms has increased exponentially. Such a huge volume of data is considered Big Data. According to the surveys conducted in varuious organization estimates, 328.77 million terabytes of data are created each day.
• Variety: Big data encompasses data in various formats, including structured, unstructured, semi-structured, or highly complex structured data. These can range from simple numerical data to complex and diverse forms such as text, images, audio, videos, and so on.
• Veracity: Veracity is a characteristic in Big Data related to consistency, accuracy, quality, and trustworthiness. Not all data that undergoes processing holds value. Therefore, it is essential to clean data effectively before storing or processing it.
• Value: The goal of big data analysis lies in extracting business value from the data. Hence, the business value derived from big data is perhaps its most critical characteristic. Without obtaining valuable insights, the other characteristics of big data hold little significance.
• Variability: This refers to establishing if the contextualizing structure of the data stream is regular and dependable even in conditions of extreme unpredictability. It defines the need to get meaningful data considering all possible circumstances.

Big Data Analytics

Data analytics involves analysing datasets to uncover insights, trends, and patterns. Technologies commonly used in data analytics include statistical analysis software, data visualisation tools, and relational database management systems (RDBMS). Big data analytics uses advanced analytic techniques against huge, diverse datasets that include structured, semi-structured, and unstructured data from different sources and in various sizes from terabytes to zettabytes. Big Data Analytics emerges as a consequence of four significant global trends:

1. Moore’s Law: Moore’s Law has enabled the handling and analysis of massive datasets, driving the evolution of Big Data Analytics.
2. Mobile Computing: Due to smartphones and mobile devices, the vast amount of real-time data can be collected from anywhere.
3. Social Networking: Social media platforms use data sharing, interactions, and user-generated content for collecting massive datasets, which helps to analyse the data easily.
4. Cloud Computing: Cloud computing allows organisations to access hardware and software resources remotely via the Internet, which reduces the investments in software and hardware.

Working on Big Data Analytics

Big data analytics involves collecting, processing, cleaning, and analyzing enormous datasets to improve organizational operations. The working process of big data analytics includes the following steps –

Step 1: Gather data

Each company has a unique approach to data collection. Organizations can now collect data from various sources, including cloud storage, mobile apps, and IoT sensors.

Step 2: Process Data

Once data is collected and stored, it must be processed properly to get accurate result on analytical queries. Various processing options are available:

• Batch processing which looks at large data blocks over time.
• Stream processing looks at small batches of data at once, shortening the delay time between collection and analysis for quicker decision-making.

Step 3: Clean Data

All data should be clean for better results. Cleaning data helps to eliminate duplicate or irrelevant data.

Step 4: Analyze Data

Getting big takes time to analyse or make usable; advanced analytics processes can turn big data into big insights.

Mining Data Streams

A data stream is a continuous, real-time flow of data generated by various sources like sensors, satellite images, the internet, and web traffic, etc. Mining data streams refers to the process of extracting meaningful patterns, trends, and knowledge from a continuous flow of real-time data. For instance, a sudden spike in searches for “election results” on a particular day might indicate that elections were recently held in a region or highlight the level of public interest in the results.

Future of Big Data Analytics

• Real-Time Analytics: It will allow businesses to process real-time data for decision-making, such as live monitoring customer behaviour or tracking supply chain activities.
• Development of Advanced Models in Predictive Analytics: Predictive analytics will help to integrate more sophisticated machine learning algorithms to enable the forecasting of trends and behaviours with greater precision.
• Quantum Computing: Quantum computers will be able to solve complex problems much faster than classical computers.

Disclaimer: We have taken an effort to provide you with the accurate handout of “Big Data and Data Analytics Class 12 Notes“. If you feel that there is any error or mistake, please contact me at anuraganand2017@gmail.com. The above CBSE study material present on our websites is for education purpose, not our copyrights. All the above content and Screenshot are taken from Artificial Intelligence Class 12 CBSE Textbook, Sample Paper, Old Sample Paper, Board Paper and Support Material which is present in CBSEACADEMIC website, This Textbook and Support Material are legally copyright by Central Board of Secondary Education. We are only providing a medium and helping the students to improve the performances in the examination. 

Images and content shown above are the property of individual organizations and are used here for reference purposes only.

For more information, refer to the official CBSE textbooks available at cbseacademic.nic.in

cbseskilleducation