What are the 5 V's of big data, exactly? In this article, we’ll take a look at all five Vs, with examples and applications, as well as what you need to know about each one if you plan on using big data in your business or organization. First, let’s start with a definition. Volume, velocity, variety, veracity, and value which are the 5 V of big data will be seen in detail right after the definition. Each V includes examples of what it means when applied to big data. Let’s get started!
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What is Big Data?
Big data is a term used to describe large sets of data that can't be easily processed by on-hand database management tools or traditional data processing applications. The term big data has been around since at least 1998, but it was popularized in 2013 when Eric Schmidt said that Google’s infrastructure was “bigger than anyone else” and would become bigger than all the rest combined.
Big data has become such an important part of our lives today because it not only allows us to better understand our customers, but also allows organizations to make better decisions about their businesses and products through insight into consumer behavior trends. The reason why we call it big data is because it’s much larger than the traditional data that companies used to process. Big data is often defined as a set of data too large and complex for standard software tools to handle, but there are many different types of big data.
Now Let's see 5 V's of big data with examples.
V1 - Volume
Data volume is one of the most important aspects of big data. To give you an idea of just how much data is out there, it's been estimated that 2.5 quintillion bytes of data are created every day! That's a lot of data, and it can be tough to manage. But with the right tools in place, you can handle it.
One of the 5 Vs of big Data is Volume. Volume is the amount of data you have to work with. It’s the number of rows in your table or rows per second (RPS) for your streaming data. You can measure volume by looking at the number of records, or the total size of each record (the sum across all columns).
For example, if you have 100k records and each record contains 8 columns, then your total row count would be 8 * 100k = 800k. This would make up just under half a TB!
The volume of your data is important because it directly impacts the speed of analysis and your ability to answer questions. If you have only a few records that contain 100 columns each, then it will take longer to process than if you have several TBs of data with just one or two columns. In other words, the higher the volume of your data, the quicker you can get insights into consumer behavior trends.
V2 - Velocity
From the 5 Vs of big data, Velocity is the speed at which data is generated and collected, processed and analyzed, stored, or transferred to other systems. It’s important to consider the velocity of your data as it will affect how much value you can get from it. The faster you collect and process your data, the better chance you have at gaining insights from it before moving onto another piece of information or project. For example, if you’re a retailer and you want to gain insights into consumer behavior trends, then the velocity of your data is crucial. If your customers are constantly providing feedback on what they want or don’t want, then it’s important that this feedback is captured quickly so that you can make adjustments in your products and services accordingly.
V3 - Variety
Another V among the 5 V's of big data is variety. The variety of data is the third V of big data. Variety refers to the different types of data that you have, whether they are structured or unstructured. Structured data can be organized by rows and columns in a spreadsheet, while unstructured data is typically in text form such as emails or documents.
Big data is not just structured data, it can be unstructured data. Unstructured data is any kind of information that isn't organized in a structured format like a spreadsheet or database. This could include text, images and audio files.
Unstructured data has traditionally been difficult to analyze because it's difficult to search through and analyze on its own terms—you have to break down into smaller pieces before you can understand what makes up the whole picture. That said, big-data technologies are getting better at handling this challenge by integrating with other technologies (like computer vision).
The good news for businesses looking for insights from their big data assets is that even though unstructured data may be more difficult than structured ones when it comes down to processing large amounts of information quickly—it also requires less storage space because there aren't as many rows or columns being stored within each document type compared with traditional databases!
V4 - Veracity
Veracity in an important V of the 5 V's of big data. Veracity refers to the accuracy and trustworthiness of data—and it’s essential for any business that relies on its data assets. If a company isn't sure about the veracity of its data, then it can’t make decisions based on that information; which means that valuable insights could be missed. Veracity is the ability to trust a source. It's important for businesses to be able to trust their big data, since it's an asset that helps them make decisions about their business. If you can't trust your data, then its value is limited.
Verifying the quality of data is one of the most important aspects in big data. Several methods can be used to accomplish this, including:
Validation: Validation is used to determine if a piece of information is accurate or not. It involves checking its quality by comparing it with other sources and comparing its accuracy against their standards for accuracy. The main purpose here is to ensure that your sources are reliable and trustworthy so you don’t get any false information from them (e.g., if one source says something happened at 11am while another says it happened at 12pm).
Accuracy Verification: Accuracy verification checks whether all facts are correct based on multiple sources; this allows users to validate their own work better than just relying on one source alone! For example, imagine we're trying out an app where users take photos of themselves doing things like eating ice cream off each others' bodies... If there was no way for us to check whether these photos were actually taken by someone else then maybe some people would think they were their own creations too! With this method though we'd know exactly what kind would look like because they've been verified against multiple viewpoints instead."
"The same goes for videos and even written content, like blog posts or articles. You can check whether they're original or not by comparing them against multiple sources and seeing if they're identical. If you have a photo that looks like it's been taken off someone else's website then that means there's no way of knowing whether it was actually yours or not! So BE CAREFUL."
V5 - Value
Value is the most important of the 5 V's of big data. It is the only one that is truly measurable and quantifiable. Value is everything you can do with data. It's what you get out of it, which will be determined by your business goals and objectives. It's what makes companies collect data in the first place and store it for future use. The value that you get from your data depends on how well you can use it to make better decisions, but also on how many people are involved in making those decisions. If a business doesn't have enough information about its customers and how they're using their products or services, then there's no way for them to make good business decisions—and this will likely lead them down the wrong path with regards to their bottom line (or worse).
In order to maximize the value of big data, organizations need to ensure that they are collecting accurate and reliable data. They also need to have systems in place to effectively store, process, and analyze the data. Furthermore, they need to have the right people on staff who know how to interpret and use the data.
There are plenty of examples where Big Data has helped companies improve their business strategy: Google tracks what books people are looking for in online; IBM tracks manufacturing plants' efficiency levels through sensors installed throughout each facility; Amazon uses algorithms built into its website so that users don't have too much trouble finding items at low prices within moments after entering an item's name into search engines like Google Search!
Big Data can also help organizations identify trends more quickly than ever before—which means more accurate predictions about future events could be made based on past patterns observed during similar situations involving similar factors such as geography/regionality etcetera...
Conclusion
As we've seen, there are five main aspects of big data. 5 V's of big data which we have discussed above are Volume, velocity, variety, veracity and value The key takeaway from this article is that no matter the size or type of organization you're in, you'll need to consider all five factors in order to effectively leverage data. If any one of these areas is lacking then your efforts will be limited at best or ineffective at worst so make sure your company has everything it needs before making decisions about how best to use big data!
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