Have you ever come across the term “big data” or searched for it online? It is the most frequently used term in digital technology and business today. What we understand by the term ‘big data’ is a large amount of data. Big data is not just about size; it is just one piece of a larger puzzle.
Data is increasing at an extraordinary rate. If you want to understand how this rapidly growing data is handled and categorized, you need to explore and understand the 14 V’s of big data. These characteristics help organizations understand, manage, and derive value from large, complex data.
So, what exactly are these 14 V’s of big data, and why do they matter? Let’s find out in this blog.
What is Big Data?
Big data encompasses three types: structured, unstructured, and semi-structured. These datasets are often so large that traditional data management systems cannot manage them efficiently. As data volumes continue to grow, handling, managing, and processing them becomes increasingly complex for organizations. To address these difficulties, advanced big data tools are emerging.
14 V’s of big data, in this regard, are the key characteristics that help us to practically understand how data is generated, handled, stored, and analyzed from different sources.
What Are the 14 V’s of Big Data
Now that we have a basic understanding of big data, it’s time to take a closer look at the 14 V’s of big data and their importance. Before exploring these characteristics, it is worth noticing the massive scale at which data is being generated today. Global data generation has reached approximately 181 zettabytes, and around 2.5 quintillion bytes of data are produced every day. These figures highlight the growing significance of big data.
So, to make the topic more accessible, we’ve split it into two parts. The first seven V’s are explained in our previous blog with the title, 7 V’s of Big Data Explained with Infographic. The blog gives thorough explanations of the first seven Vs of big data, accompanied by a compelling infographic.
In the present blog, we will have a look at the remaining seven V’s and their roles in big data strategies.
1) Volatility
Sometimes, data remains valuable for years, while some data loses its importance very quickly. This is the time when volatility plays a vital role. It helps organizations decide which data should be kept for how long before it becomes obsolete. Understanding volatility is crucial because storing data indefinitely can lead to higher storage costs and greater complexity.
Example: On social media, a hashtag will only gain popularity for a few hours or days. The value of that particular data decreases once the trend ceases.
2) Validity
The process of making sure that data is properly formatted and satisfies a defined set of rules and guidelines before it is used is referred to as validity. It emphasizes verifying whether data meets the essential limitations and is suitable for its intended purpose. Valid data is very important in fields such as finance, where incorrect data can largely impact decision-making.
Example: Does the data follow the required format, rules, and constraints?
A person’s age entered as 28 is valid; “ABC” in an age field is invalid.
3) Vagueness
Vagueness is a characteristic of big data that outlines the presence of unclear or ambiguous information that lacks a well-defined context. If data is ambiguous, it can make interpretation difficult and affect the quality of analysis. To reduce any vagueness, organizations use advanced analytics and data standardization strategies. As a result, data clarity is improved.
Example: If somebody responding to an online survey says, “I use the app frequently.” The term “frequent” may be vague: does it represent once a day, once a week, or several times a day?
4) Vocabulary
Common terminologies, naming standards, and definitions used to describe data throughout an organization are referred to as vocabulary. Data is gathered from different sources; the same term can have different meanings. Vocabulary ensures that common definitions are stated clearly so that no confusion is caused. If the system lacks a common vocabulary for shared, gathered, or analyzed data, there is a high risk of inconsistent data.
Example: For storing gender information, one dataset stores gender as Male/ Female, while another might use M/F.
5) Viability
Big Data viability refers to how practical and economically feasible your data solution is in practice over the long term, delivering useful results. Viability examines whether you can use the system in an actual organization. Additionally, it inspects whether your organization has the capital and infrastructure needed to keep the system stable.
Example: For analyzing students’ performance, attendance records, test scores, and assignment grades are viable data, but students’ favorite color or food is not.
6) Venue
Venue typically refers to the data processing performed across different systems or platforms. In a modern data system, a venue is a computing environment where data workflows are carried out. For example, data can be processed on a cloud platform like AWS or Azure, and the same data can be stored on an on-premises company server. Each of these is a different venue.
7) Vulnerability
Vulnerability refers to a risk or weakness in the system that can lead to data loss or misuse. A system is considered vulnerable when it lacks basic data security, such as having unauthorized access, data leaks, or system failures.
Protecting data and controlling risk factors is essential because large systems handle significant amounts of sensitive information. Even a small risk or negligence can lead to major damage.
Example: A large company stores sensitive data in the cloud. If an employee uses a weak password, cyber attackers may be able to access it.
Simplifying 14 V’s of Big Data
To provide additional clarity on these characteristics of big data, we group them into four pillars based on their role in big data management.
As we know, big data is not just about the size; it is also about how it is analyzed, gathered, and processed. These four pillars do not follow any sequence and are not just independent concepts, but interconnected processes that work continuously throughout the data lifecycle. These four pillars are as follows:
Pillar 1: Managing data at scale– Generally, it focuses on speed, size, and its continuously changing nature. It consists of the following characteristics of big data:
1. Velocity
2. Variability
3. Volume
4. Variety
5. Volatility
Pillar 2: Ensuring Data Quality and Trust –Typically focuses on data accuracy, consistency, and reliability.
6. Validity
7. Vagueness
8. Vocabulary
9. Veracity
Pillar 3: Turning Data into Business Value -Focuses on deriving meaningful insights and outcomes
10. Value
11. Visualization
12. Viability
13. Venue
Pillar 4: Governing and Securing data – Focuses on protecting and controlling risk.
14. Vulnerability
Wrapping Up:
Big data integration allows businesses to adopt a data-driven business approach. However, successful integration can be challenging with limited knowledge of its complex dynamics.
Whenever you come across the word “Big Data,” remember it is not just about the size of the data; it is also about the characteristics that make the data complex as well as valuable. It is important to understand that these 14 V’s of big data influence how data is collected, processed, managed, and utilized. These characteristics help organizations to leverage the full potential of their data.
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FAQs
1. What is big data?
Answer: A collection of semi-structured, structured, and unstructured data generated in large volumes is known as big data. These datasets are growing rapidly and require advanced tools and technologies for storing and managing data.
2. What is the difference between Variability and Variety?
Answer: One of the major differences between variability and variety is that variety refers to different types of data, whereas variability refers to change in data flow, meaning, or structure over time.
3. What are the four pillars of Big Data?
Answer: The four pillars of big data are as follows:
- Managing data at scale
- Ensuring data quality and trust
- Turning data into business value
- Governing and securing data
4. List 14 V’s of big data
Answer: 14 V’s of big data are velocity, variability, volume, variety, volatility, validity, vagueness, vocabulary, veracity, value, visualization, viability, venue, and vulnerability.
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