Big Data
- Requires specialized tools and techniques to store, manage, and analyze beyond traditional methods.
- Characterized by size, variety (structured and unstructured), and high velocity from sources like social media, sensors, online transactions, and mobile devices.
- Applied in personalization, healthcare, transportation optimization, and public-service decision-making.
Definition
Section titled “Definition”Big data refers to large, complex sets of data that are difficult to process using traditional data processing tools. These data sets are often generated from a variety of sources—such as social media, online transactions, sensors, and mobile devices—and can be structured or unstructured in nature.
One key characteristic of big data is its sheer size, which makes it challenging to store, manage, and analyze using traditional methods. For example, a single day’s worth of data from Twitter alone can generate over 500 million tweets and 12 terabytes of data.
Big data also differs from traditional data in its variety and velocity. Traditional data is typically structured and generated from a single source (for example, a company’s transactional database). In contrast, big data is often unstructured and generated from multiple sources, and it can be produced at high rates—sensor data from a manufacturing plant can generate thousands of data points per second.
Explanation
Section titled “Explanation”- Size: The volume of big data can exceed the capacity of conventional storage and processing systems, requiring purpose-built tools and infrastructure.
- Variety: Big data includes both structured and unstructured information from diverse sources (social media posts, sensor readings, web logs, online transactions), which necessitates advanced analytics to integrate and interpret.
- Velocity: Data may be produced and collected at high rates, creating a need for real-time processing and analysis to extract timely insights.
- Tools and techniques: Because of these characteristics, organizations use specialized tools and techniques to handle, analyze, and extract value from big data.
Examples
Section titled “Examples”Retail
Section titled “Retail”Retailers may use data from online transactions, social media posts, and sensor data from in-store cameras to create personalized product recommendations and targeted marketing campaigns.
Healthcare
Section titled “Healthcare”Hospitals and healthcare organizations may use data from electronic health records, medical imaging, and genetic data to predict and prevent diseases, and to personalize treatment plans for patients.
Transportation
Section titled “Transportation”Transportation and ride-sharing companies may use data from sensors, GPS, and traffic cameras, as well as mobile-device data, to match riders with nearby drivers, predict demand, and adjust pricing in real-time.
Governments
Section titled “Governments”Cities and governments may use data from social media, census records, traffic sensors, and weather data to predict and respond to natural disasters, and to improve public transportation and emergency response.
Notes or pitfalls
Section titled “Notes or pitfalls”- The sheer size of big data can make storage, management, and analysis challenging with traditional methods.
- The diversity of sources and formats (structured vs. unstructured) requires advanced analytics techniques to extract meaningful insights.
- High-velocity data generation often demands real-time processing to capture and act on information promptly.
- Handling big data typically requires specialized tools and techniques.
Related terms
Section titled “Related terms”- Traditional data
- Structured data
- Unstructured data
- Real-time processing
- Advanced analytics
- Sensor data
- Social media data