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What Is Big Data? Characteristics, Applications & Processing Technologies

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Understanding what Big Data is

As digital transformation and artificial intelligence continue to advance, Big Data is becoming a crucial foundation that helps businesses leverage their data to make decisions and optimize operations. So what is Big Data, what are its characteristics, and how is it applied in practice? Let TOT walk you through the details in the article below. 

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Table of Contents

What is Big Data?

Big Data refers to datasets that are large in scale, generated at high speed, and diverse in type or source, making them difficult or inefficient to collect, store, manage, and analyze using traditional data processing methods. For this reason, Big Data is not defined simply by a fixed storage threshold. Whether a dataset is considered Big Data also depends on the characteristics of the data and the technological capabilities used to collect, store, and process it.

The concept of Big Data is closely tied to the growth of the Internet, mobile devices, social media, e-commerce, IoT, and digital systems. According to the IDC Global DataSphere Forecast, the volume of data created and replicated worldwide is projected to reach approximately 394 zettabytes by 2028, up from around 181 zettabytes in 2025. This figure represents the amount of data created and replicated globally, not the total volume of data currently stored.

It is worth noting that Big Data is not limited to tables with millions of rows. A single system may simultaneously hold transaction data, search history, images, video, server logs, sensor data, emails, and social media content. As the number and types of data sources grow, businesses need an architecture capable of collecting, storing, processing, and integrating these sources to serve specific business goals.

Real-world examples of Big Data

  • E-commerce: Platforms such as Shopee, Lazada, and similar services can generate data from search history, viewed products, clicks, shopping carts, orders, reviews, and post-purchase behavior.
  • Content platforms: Netflix, YouTube, and other video services can record data on when users start, pause, rewind, skip, or rewatch content.
  • Banking: Every transaction, login, transfer, or card payment generates data that can be analyzed to detect unusual transaction patterns.
  • IoT: Sensors in factories, vehicles, or smart devices can continuously send data on temperature, vibration, pressure, location, and operating status.

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Definition of Big Data
Big Data is a set of data with large scale, speed, diversity, or complexity (Source: Compiled)

What are the characteristics of Big Data? The 5V model

Big Data is often described through the 5V model, consisting of Volume, Velocity, Variety, Veracity, and Value. Of these, Volume, Velocity, and Variety are the three original attributes introduced by Doug Laney in the 2001 research paper 3-D Data Management: Controlling Data Volume, Velocity and Variety. Later, Veracity and Value became more widely used to emphasize the reliability of data and its ability to generate value.

Volume – Data volume

Volume reflects the scale of data an organization needs to collect, store, and process. This scale can range from gigabytes and terabytes to petabytes or more, depending on the industry and system. A retail chain with hundreds of stores, for example, may continuously generate data on invoices, products, inventory, customers, and interactions across multiple channels.

As data accumulates over long periods, traditional storage systems may face limitations in scalability and processing. Businesses can therefore use a Data Lake, Cloud Storage, or distributed storage systems to manage large data volumes more effectively.

Velocity – Data speed

Velocity reflects the speed at which data is generated, transmitted, and processed. Not all data needs to be processed instantly, but some use cases require real-time or near-real-time processing. For instance, a banking system can analyze transactions as they occur to detect signs of abnormal activity.

The ability to process data quickly helps businesses respond promptly to ongoing events. This is especially important in fields such as finance, e-commerce, manufacturing, and IoT, where the value of data can diminish if the information is processed too late.

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Variety – Data diversity

Variety describes the diversity of data origins and formats. Big Data can include structured data such as order tables, semi-structured data such as JSON from an API, and unstructured data such as emails, images, videos, or customer comments.

Combining multiple data types gives businesses a broader perspective on their customers and operations. For example, a business can combine transaction data with search history, interactions, and customer feedback to better understand the journey and needs of each customer segment.

Veracity – Data reliability

Veracity refers to the accuracy, consistency, and trustworthiness of data. In practice, data may contain duplicate records, incorrectly formatted information, missing values, or outdated information. If such data is used directly for analysis, the results can be distorted and affect business decisions.

Therefore, ensuring data quality is an important part of leveraging Big Data. According to Gartner, based on a 2020 study, poor data quality costs organizations an average of at least USD 12.9 million per year.

Value – Value from data

Value emphasizes the ability to turn data into useful information, insights, and business value. A customer’s purchase history on its own is just data; only when it is analyzed to identify shopping trends or groups of customers likely to be interested in a specific product does that data begin to create value.

This shows that the goal of Big Data is not simply to collect as much data as possible. More importantly, businesses need to leverage data to support decision-making, optimize operations, understand customers, and solve specific business problems.

>>> Reference: 

The 5V model of Big Data
The 5V model describes Big Data through five characteristics (Source: Compiled)

What types of data does Big Data include?

Data in a Big Data system is typically classified by structure into three groups: Structured Data, Semi-structured Data, and Unstructured Data. Each group is organized, stored, and processed differently, as summarized below.

Data typeCharacteristicsExamples
Structured DataClearly structured, typically organized into rows and columnsCustomers, orders, transactions
Semi-structured DataDoes not require a fixed table but contains metadata, keys, or tagsJSON, XML, some types of logs, API data
Unstructured DataNo fixed schema, usually not organized in table formImages, video, audio, email

Structured Data

Structured Data is clearly structured and typically organized into fields, rows, and columns. Each field has a predefined data type, making it easy for systems to store, query, and manage. Common examples include customer lists, invoices, bank transactions, payroll, and inventory data.

Structured Data is usually stored in relational databases and queried using SQL. It remains an important data type in Big Data systems because many analytical and business processes start from structured transaction data.

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Semi-structured Data

Unlike Structured Data, Semi-structured Data does not require a fixed table structure but still contains metadata, keys, tags, or structural markers that help systems recognize the components within. JSON, XML, and certain types of system logs are common examples.

Thanks to its flexible structure, this data type often appears when applications exchange information through APIs or capture data from multiple different systems. Records may have different structures or field sets, so the data often needs to be normalized and transformed before analysis.

Unstructured Data

While Semi-structured Data retains some structural markers, Unstructured Data has no fixed schema and is usually not organized in table form. Images, video, audio files, emails, text documents, and social media comments are all common examples. This is also the data group that businesses are increasingly creating and collecting from digital channels.

To leverage unstructured data, businesses can combine AI and Machine Learning. NLP supports text analysis, Computer Vision processes images and video, while Speech Recognition can convert audio data into text. Thanks to these technologies, unstructured data can be turned into information that can be analyzed at scale, serving use cases such as behavioral analysis, forecasting, and AI application development.

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How does Big Data work?

A Big Data system typically involves the steps of collecting, storing, processing, and analyzing data, then delivering the results to users or business systems. Depending on the architecture, some steps may run in parallel or in a different order, especially in real-time data processing systems.

Data collection

Data can come from many different sources such as websites, mobile apps, IoT, social media, CRM/ERP, transaction systems, APIs, and external platforms.

At this stage, businesses need to build a data pipeline to bring data from multiple sources into the processing system. The challenge lies not only in collecting data but also in connecting different sources, maintaining consistency, and ensuring the data is ready for subsequent processing steps.

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Data storage

Once collected, data can be stored in a Data Lake, Data Warehouse, Cloud Storage, or distributed storage systems, depending on the data type and intended use.

A Data Lake is generally suited to storing data in many formats, including raw data. A Data Warehouse, by contrast, is optimized for organized data and serves reporting and analytics needs. With Cloud infrastructure, businesses can flexibly scale storage resources and processing capacity on demand.

Data processing and analysis

Once data has been brought into the system, businesses can choose a processing method that matches their requirements for volume and response time:

  • Batch Processing: processes data in batches, suitable for periodic reports and tasks that do not require immediate results.
  • Stream Processing: processes data continuously as events occur, suitable for fraud detection, IoT monitoring, or systems that need to respond quickly.

Batch Processing and Stream Processing determine how data is processed over time, while Data Analytics and Machine Learning help extract information from the processed data. Businesses can use these methods to identify trends, segment groups, detect anomalies, or build forecasting models.

Visualization and extracting insights

Analysis results can be fed into dashboards, reports, or business systems so that users can easily access and use them. From there, data can be turned into insights that support businesses in decision-making and taking action.

The data value chain can be visualized as follows: Data → Insight → Decision → Action

The value of Big Data becomes clear when analysis results can help people or systems make decisions and take appropriate action.

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How Big Data works
The basic workflow of Big Data (Source: TOT)

The role of Big Data for businesses

Big Data helps businesses leverage data at scale to generate insights that support a wide range of activities, from understanding customers and supporting decision-making to optimizing operations, managing risk, and forecasting demand.

Understanding and personalizing the customer experience

One of the most visible benefits of Big Data is its ability to help businesses better understand customer behavior and needs. Businesses can combine purchase history, website behavior, app interactions, and customer support data to gain a more comprehensive view of their customers.

From there, the system can segment customers, recommend products, personalize content, or identify the right moments to engage. However, the effectiveness of personalization still depends on data quality and how the business designs the experience.

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Supporting data-driven decision-making

Beyond understanding customers, Big Data also allows managers to combine data from multiple departments rather than relying on isolated reports. Dashboards and analytics systems can help track revenue, inventory, customer behavior, or operational performance.

This approach is especially useful for decisions that require weighing many variables, such as adjusting the product portfolio, forecasting demand, or allocating resources. Combining multiple data sources gives businesses a stronger basis for assessing the situation and choosing the right course of action.

Optimizing operations and costs

At the operational level, data can help businesses detect bottlenecks, forecast demand, and use resources more efficiently. In manufacturing, sensor data can be combined with AI and Machine Learning to implement predictive maintenance, detecting signs of abnormality before equipment fails.

In logistics, data on orders, vehicle locations, and delivery history can be analyzed to optimize routes and transport times. These improvements can help reduce downtime, limit waste, and optimize operating costs.

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Fraud detection and risk management

Beyond operational efficiency, Big Data also helps businesses detect anomalies and manage risk. Analytics systems can combine multiple signals from transactions and customer behavior to identify unusual or risky patterns.

In banking, insurance, and e-commerce, this is an important application of Big Data Analytics. For example, a transaction may be flagged for review when several unusual signals appear simultaneously, such as location, time, device, transaction frequency, and account history.

Based on historical data and new signals, businesses can build models to forecast demand, sales, or consumer trends. These forecasts can support inventory planning, resource allocation, and adjustments to business operations in response to market fluctuations.

However, forecasts are not automatically accurate simply because there is a lot of data. Data quality, variable selection, analytical methods, and market context can all affect the results. Big Data should therefore be seen as a foundation that provides data and insights for the forecasting process, rather than a guarantee of an accurate forecast.

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The role of Big Data
The 5 key roles of Big Data for businesses (Source: TOT)

Applications of Big Data across industries

Big Data is applied across many industries, from finance and e-commerce to healthcare, manufacturing, marketing, and transportation. The common thread is that data is leveraged to address a specific need or problem, rather than simply being stored at scale.

Big Data in finance and banking

In the finance and banking sector, Big Data is used for fraud detection, credit scoring, risk management, and customer segmentation. Systems can analyze transaction history, payment behavior, and many other signals to detect unusual patterns.

Use case: A fraud detection system can analyze transactions in near real time and raise alerts when behavior differs significantly from a customer’s transaction history.

Big Data in finance and banking
Applications of Big Data in the finance and banking sector (Source: Compiled)

Big Data in e-commerce

In e-commerce, Big Data is leveraged for product recommendations, customer behavior analysis, demand forecasting, and building dynamic pricing models. Businesses can combine transaction data with search behavior, product views, and on-platform interactions.

Use case: A product recommendation system can combine viewing, search, cart, and purchase history to identify the products most likely to suit each user.

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Big Data in healthcare

In healthcare, Big Data supports the analysis of electronic health records, medical imaging data, drug research, and the tracking of epidemiological trends. Combining multiple data sources helps healthcare organizations and researchers extract additional information to support diagnosis, treatment, and research.

Use case: An AI system can analyze X-ray images to help doctors identify areas that need closer examination. The analysis serves as professional support and does not replace the judgment of medical staff.

Big Data in manufacturing and IoT

In a manufacturing environment, data from sensors, machinery, and operational systems can be combined with activity history to predict maintenance needs, build smart factories, and optimize the supply chain.

Use case: Sensors can monitor the temperature and vibration of a motor. The system analyzes these metrics and their fluctuations to detect signs of abnormality, issuing maintenance alerts before a failure can occur.

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Big Data in marketing

Marketing can leverage Big Data to segment customers, analyze the customer journey, evaluate the effectiveness of touchpoints, and personalize marketing activities across multiple channels. Combining data from multiple sources gives businesses a more complete view of customer behavior and interactions.

Use case: Businesses can combine data from advertising, websites, CRM, and orders to analyze the customer journey and assess the role of each touchpoint in the conversion process.

Big Data in transportation

At the urban and transportation scale, location data, travel history, timing, and vehicle density can be used to forecast traffic conditions, optimize routes, and develop intelligent transportation systems.

Use case: A transportation platform can combine real-time location data with historical traffic data to estimate travel times and support vehicle dispatching.

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Big Data in transportation
Applications of Big Data in transportation (Source: Compiled) 

Common Big Data technologies

A Big Data system can combine multiple technologies for tasks such as storage, processing, data transmission, and analysis, depending on the data scale and the needs of the business. These technologies serve different roles and can be combined within the same system.

Hadoop

Apache Hadoop is an open-source framework that supports distributed data storage and processing across multiple servers. Hadoop includes components such as HDFS for distributed storage and MapReduce for parallel data processing. It is one of the technologies that have played an important role in the development of the Big Data ecosystem.

Hadoop is well suited to problems that require processing large volumes of data on distributed systems. In modern architectures, Hadoop can be combined with many other technologies depending on storage and processing requirements.

Apache Hadoop technology in the development of the Big Data ecosystem
Apache Hadoop – an open-source technology supporting distributed data storage and processing in Big Data systems (Source: Compiled) 

Apache Spark

Apache Spark is a distributed data processing engine that supports both batch and stream processing. Spark also provides capabilities for SQL querying, data analysis, and Machine Learning at scale.

As a result, businesses can use Spark for a wide range of needs, from data processing and querying to analysis and building Machine Learning models.

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Apache Spark technology in the development of the Big Data ecosystem
Apache Spark – a distributed data processing engine (Source: Compiled) 

NoSQL Database

NoSQL is a group of databases that do not rely entirely on the traditional relational model. MongoDB, Cassandra, and HBase are some common examples.

NoSQL is generally suited to data with a flexible structure, systems that need to scale horizontally, or requirements for storing and retrieving data at scale. However, choosing between NoSQL and a relational database still depends on the characteristics of the data and the specific use requirements.

NoSQL technology in the development of the Big Data ecosystem
NoSQL – a flexible database often used to store and process data at scale (Source: Compiled) 

Apache Kafka

Apache Kafka is an event-driven data processing and streaming platform used to ingest, store, and distribute event streams across multiple systems. Kafka can process data as events occur and supports building systems that need to exchange data continuously.

For example, a transaction occurring in an application can be received by Kafka and routed to various systems such as fraud detection, data analytics, or the data warehouse.

Apache Kafka technology in the development of the Big Data ecosystem
Apache Kafka – an event-driven data processing and streaming platform in Big Data systems (Source: Compiled) 

Data Lake and Cloud infrastructure

A Data Lake allows businesses to store data in many formats within a flexible architecture, including raw data. A Data Lake can be deployed on on-premise or Cloud infrastructure, depending on the needs of the business.

When using the Cloud, businesses can flexibly scale storage resources and computing capacity on demand. This model suits systems with rapidly changing data volumes that need scalability without investing in all the physical infrastructure upfront.

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Data Lake technology in the development of the Big Data ecosystem
Data Lake – an architecture for storing structured, semi-structured, and unstructured data to serve analytics and Machine Learning (Source: Compiled) 

AI and Machine Learning

AI and Machine Learning help businesses extract value from data through predictive models, customer segmentation, anomaly detection, or processing text, images, and audio.

Big Data and AI have a mutually complementary relationship: large-scale data can provide diverse data sources for training and running AI models, while AI and Machine Learning help businesses identify patterns, trends, or information that are difficult to uncover using traditional analytical methods.

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Machine Learning technology in the development of the Big Data ecosystem
Machine Learning – a technology that uses algorithms and data models to learn, analyze, and make predictions (Source: Compiled) 

How is Big Data different from Data, Data Mining, and Data Analytics?

To better distinguish between Big Data, Data, Data Mining, and Data Analytics, the role of each concept can be summarized as follows:

  • Data: Data in general, which can exist in many forms and scales.
  • Big Data: Datasets with large scale, speed, diversity, or complexity that traditional management and processing methods struggle to handle effectively.
  • Data Mining: A data mining technique focused on finding patterns, relationships, or rules within data.
  • Data Analytics: The process of analyzing data to find insights, answer questions, and support decision-making.

To see the differences in nature, purpose, and use more clearly, below is a comparison table of Data, Big Data, Data Mining, and Data Analytics:

CriteriaDataBig DataData MiningData Analytics
NatureData in generalData with large scale, speed, or complexityA data mining techniqueA data analysis process
PurposeServes as a basis for storing, processing, and using informationManaging and leveraging data at scaleFinding patterns, relationships, and rulesFinding insights and supporting decision-making
Common
tools		Excel, SQL, DatabaseHadoop, Spark, NoSQL, Data LakeStatistical algorithms, Machine Learning, clusteringBI, dashboards, analytics tools
ResultsRecords, datasetsLarge-scale datasets that can be processed and analyzedPatterns, clusters, associationsReports, insights, a basis for supporting decisions

How are Big Data and Data different?

Data can be a spreadsheet of a few hundred rows, a transaction, a text file, or information that is collected and stored for use. Big Data is still data, but with a scale, speed, diversity, or complexity that makes conventional management and processing methods struggle to keep up effectively.

In other words, all Big Data is data, but not all data is Big Data.

How are Big Data and Data Mining different?

Big Data describes the characteristics and scale of data, while Data Mining is a technique for mining data to find meaningful patterns, relationships, or rules.

For example, a business can store billions of transactions in a Big Data system and then apply Data Mining to discover which products are frequently bought together. However, Data Mining can also be performed on smaller datasets and does not necessarily require Big Data.

How are Big Data and Data Analytics different?

Data Analytics is the process of analyzing data to find trends, relationships, or insights that answer questions and support decision-making. This activity can be carried out on a small dataset or on a Big Data processing system.

Simply put, Big Data concerns the scale, characteristics, and management of data, while Data Analytics focuses on extracting information from data. Data Analytics can use many different methods, among which Data Mining is one technique that can be applied to search for patterns in data.

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The difference between Big Data and Data, Data Mining & Data Analytics
Distinguishing the differences between Big Data and Data, Data Mining & Data Analytics (Source: TOT)

Challenges in deploying Big Data

Alongside its benefits for analysis and operational optimization, Big Data also presents a number of challenges in terms of cost, data quality, security, talent, and integration.

ChallengeCauseGeneral solution
Infrastructure and storage costsRapidly growing dataData tiering, Cloud
Data qualityMissing, duplicate, inconsistent dataValidation, cleaning, and normalization
Security and privacyLarge amounts of sensitive dataAuthorization, encryption, access control
Talent shortageLack of data expertiseTraining, recruitment, expert partnerships
Data integrationData spread across many systemsAPIs, data pipelines, data normalization

Infrastructure and storage costs

The larger the volume of data, the higher the storage and processing requirements, which in turn drives up infrastructure costs. Businesses should classify their data, establish lifecycle policies, and choose Cloud or on-premise options that fit their needs.

Data quality

Missing, duplicate, or inconsistent data can affect reports and analysis results. Businesses need to build processes for validating, cleaning, and normalizing data rather than only addressing errors as they arise.

Security and privacy

Big Data can contain personal information, transactions, and internal data. Businesses need to control access, encrypt data, and clearly define which data may be collected, stored, and used.

Talent shortage

Building and operating a Big Data system may require expertise such as Data Engineers, Data Analysts, or Data Scientists. Businesses can develop their in-house team step by step and partner with technology providers when needed.

Integrating data from multiple sources

CRM, ERP, websites, mobile apps, and sales systems may use different data structures. Businesses therefore need processes to integrate and normalize data before deploying large-scale analytics.

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Big Data is shifting from a focus on storing large volumes of data toward the ability to leverage data quickly, flexibly, and in a controlled way. Some notable trends include:

AI-driven Analytics

AI is increasingly integrated into data analysis, allowing users to ask questions in natural language, generate queries, or explore trends without directly interacting with the data system. However, AI-generated results still need to be checked for accuracy and data sources.

Real-time Big Data

Transaction systems, IoT, fraud detection, and personalization have an increasing need for near-real-time data processing. Instead of waiting to aggregate data in batches, businesses can process events as they occur to respond more quickly.

Cloud Big Data

The Cloud continues to be widely used in Big Data architectures thanks to its flexible scalability in storage and computing capacity. Businesses can adjust resources on demand rather than investing in all the infrastructure upfront.

Edge Computing and IoT

As more and more IoT devices generate data at the point of use, Edge Computing enables part of the data to be processed close to its source. This approach can help reduce latency and the amount of data that must be transmitted to the central system, making it suitable for factories, vehicles, and systems that require fast responses.

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Data Lakehouse

The Data Lakehouse aims to combine the flexible storage of a Data Lake with the governance and analytics features typically found in a Data Warehouse. This architecture helps businesses reduce data fragmentation and simplify large-scale data use.

Generative AI and Big Data

Generative AI opens up new ways to leverage unstructured data such as documents, emails, and text content. Businesses can build question-answering systems over internal data, summarize documents, or search for information using natural language. When AI accesses enterprise data, access rights, data sources, and security still need to be tightly controlled.

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Data Governance and Privacy

As data is increasingly used together with AI, Data Governance becomes an important part of data management. Businesses need to define data provenance, ownership, access rights, intended use, and retention periods to ensure that data is leveraged for the right purposes and kept secure.

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Exploring future Big Data trends
Future trends in Big Data applications (Source: TOT)

What do businesses need to deploy Big Data effectively?

Deploying Big Data should not start with the question “which technology should we use?” but rather “what problem does the business need to solve?” From that objective, the business can determine the appropriate data, architecture, and technology.

1. Define business objectives

Businesses need to clearly define the problem they want to solve, such as reducing customer churn, optimizing inventory, detecting fraud, or forecasting demand.

2. Identify data sources and types

Next, businesses need to identify the available data sources, the type of data, and the quality and completeness of each source. This provides the basis for assessing which data can be used for the defined problem.

3. Build the data architecture

Based on scale and needs, businesses choose appropriate ways to store, connect, and manage data. Components may include a Data Lake, Data Warehouse, Cloud, and data pipeline.

4. Build the processing and analytics system

Technology should be chosen according to the actual requirements of each problem. Not every business needs Hadoop, Spark, Kafka, or Machine Learning from the very beginning.

5. Establish data governance and security

Businesses need to define access rights, data standards, storage policies, security, and the responsibilities of the relevant departments. These elements should be built in parallel with the system rather than addressed after deployment.

A practical approach is to start with a use case that has clear value, measure the results, and then expand step by step. TOT can accompany businesses throughout the process of building their technology systems, from data integration and custom software development to applying AI in operations.

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Conclusion

From the analysis above, it is clear that what Big Data is lies not only in the scale of data but also in how businesses collect, process, and leverage data to create value. To deploy it effectively, businesses need to start from a specific problem, choose the right architecture and technology, and pay close attention to data quality and security. When data is turned into insights and supports decisions, Big Data can become an important foundation for business operations.

Frequently asked questions

What is Big Data? What does Big Data (large-scale data) mean?

Big Data refers to datasets whose scale, speed, diversity, or complexity makes traditional storage and processing methods struggle to keep up effectively. Big Data can include structured transaction data, JSON and certain semi-structured logs, as well as unstructured images, video, audio, and text. The concept is not only about storage capacity but also about the ability to collect, store, process, and leverage data at scale.

Why is Big Data important?

Big Data helps businesses leverage data from customers, transactions, operational systems, and devices to support experience personalization, demand forecasting, operational optimization, fraud detection, and decision-making. Big Data also plays an important role in AI and Machine Learning, where suitable data is used to train, evaluate, and run models. However, more data does not mean better results if the data lacks quality or does not fit the problem.

What is Variety in Big Data?

Variety is one of the characteristics of Big Data, referring to the diversity of data types and sources. Data can be Structured Data such as transaction tables; Semi-structured Data such as JSON, XML, and certain logs; or Unstructured Data such as images, video, audio, emails, and social media content. Variety requires a Big Data system capable of ingesting, storing, and processing many different data types. Businesses can use a Data Lake and appropriate data processing tools to integrate and leverage these data sources.

What field is Big Data?

Big Data is not a single profession but an interdisciplinary field that combines information technology, data science, and data management to collect, store, process, and analyze data at scale. Positions commonly associated with this field include Data Engineer, Data Analyst, Data Scientist, and Data Architect. Each role carries a different responsibility, from building data systems and analyzing information to developing models and designing data architecture.

What are the three main elements of Big Data?

The three original elements commonly used to describe Big Data are Volume, Velocity, and Variety, referring to the volume, speed, and diversity of data, respectively. The 3V model was introduced by Doug Laney in the 2001 research paper 3-D Data Management: Controlling Data Volume, Velocity and Variety. Later, many sources expanded this model with elements such as Veracity and Value. The 5V model is therefore a common approach to describing Big Data, in which Volume, Velocity, and Variety are the three foundational attributes.

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