April 12, 2019in [ Engineering & Technology ]
Students earning their BS in Computer Science with an Emphasis in Big Data Analytics explore the creative applications of software development, utilizing critical thinking and practical project experience. Big data refers to large amounts of data that can inform analysts of trends and patterns related to human behavior and interactions. There are four major components of big data.
Table of Contents:
- Volume
- Veracity
- Velocity
- Variety
Volume
Volume refers to how much data is actually collected. An analyst must determine what data and how much of it needs to be collected for a given purpose. To imagine the possibilities, consider a social media site where people write updates, like photos, review business, watch videos, search for new items and interact in some way with just about everything they see on their screens. Each of these interactions generates data about that person that can be fed into algorithms.
Veracity
Veracity relates to how reliable data is. An analyst wants to ensure that the data they look at is valid and comes from a trusted source. This is determined by where the data comes from and how it is collected. Data collected from native sites rather than third-parties is necessary for reliable results. Additionally, testing measures must be properly designed to ensure that data results in the desired information and is not extraneous.
Velocity
Velocity in big data refers to how fast data can be generated, gathered and analyzed. Big data does not always have to be used imminently, but in some fields, there is a great advantage to receiving up to the second information about rates and being able to act accordingly. In other businesses, the data trend over time is more important to help make predictions or solve lingering problems.
Variety
Variety refers to how many points of reference are used to collect data. If data is collected from a single source, that information may be skewed in some ways. It will not represent a broad population or wide trend. In some cases, like with velocity, that is fine. A pet microchipping service, for example, may only want to target data from a neighborhood social networking site. A movie company, on the other hand, may want to target several social media sites and people of various age groups. So they would need more points of reference to decide on the best places to do business.
If big data sounds like an exciting world, consider becoming a part of GCU’s innovative College of Science, Engineering and Technology. For more information, visit our website or click the Request More Information button on this page.
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I'm well-versed in the realm of big data analytics, particularly in the context of computer science. The concepts outlined in the article you mentioned—volume, veracity, velocity, and variety—are fundamental pillars in understanding and harnessing the potential of big data.
Volume: This refers to the sheer amount of data collected. It's crucial for analysts to discern what data is necessary for a specific purpose. Think about social media platforms where every interaction generates a trove of data that can be utilized in algorithms. Determining the scope of collection is essential.
Veracity: This aspect delves into data reliability. It's imperative to ensure that the data is valid and sourced from trustworthy origins. Native sites tend to provide more reliable data compared to third-party sources. Properly designed testing measures are also essential to filter out extraneous or misleading information.
Velocity: The speed at which data is generated, gathered, and analyzed defines velocity. Real-time data can be crucial in certain fields for immediate decision-making, while in others, trends over time matter more for predictive analytics or problem-solving.
Variety: The diversity of data sources impacts the breadth and accuracy of insights. Collecting data from multiple points of reference is vital for a comprehensive view. Depending on the context, targeting data from a single source might suffice, as in the case of a neighborhood-specific service, while broader businesses require multiple references across demographics.
These concepts are foundational in the field of big data analytics. They empower analysts to not only process large volumes of data but also discern its quality, analyze it swiftly, and ensure a diverse and comprehensive dataset for meaningful insights. If diving into the exciting world of big data interests you, exploring programs like GCU's College of Science, Engineering, and Technology could be a great starting point.
