7 Vs of Big Data – what are they and why are they so important?

Big Data is a buzzword nowadays. But is this term really enough to express all that it includes? The word “big” is obviously relative, so in fact, the term itself gives us little information. Fortunately, some professionals dealing with Big Data have found the time to explain it a bit more with the “7 Vs of Big Data” (well, the number of V’s is still disputable). Read our article to learn more.

At the very beginning, it was chaos… Just kidding, but it is a fact that when it comes to Big Data, you have the right to feel confused sometimes. Why is that? The definition of big data has evolved through the years. If you google “Vs of big data” you will learn that there are 4, 5, or even 17 Vs. Actually, from the article “How Many Old and New Big Data V’s Characteristics, Processing Technology, And Applications” from 2020 we learn that there have been about 50 Vs mentioned by Big Data professionals. Some terms may be simply alternatives for others or might be more specific. Anyway, it can be really crazy. We are going to focus on the 7 Vs of Big Data we find most important when discussing the matter.

Read “How Many Old and New Big Data V’s Characteristics, Processing Technology, And Applications” by Abouelela Abdou to learn more.

7 Vs of Big Data – what are they?

Big Data refers to very vast, diversified collections of data. Because of its amount and variety, it is quite difficult to define it. That is why experts continue to invent and explain more Vs. You can think of them as various dimensions of Big Data. Each V provides us with a little bit more information about what Big Data actually is.

Many Vs have already been described, but the first seven are usually the same in most of the sources. There are: Volume, Variety, Velocity, Variability, Veracity, Visualization and Value. Allow us to tell you more about them.

1. Volume

You can’t imagine the unimaginable – maybe that is precisely why understanding Big Data may be so difficult for some people. Let’s try something easier than that. Can you imagine how much data is generated by Facebook users every day? Hundreds of terabytes. There are companies out there who process over a million transactions per hour.

These are numbers beyond most people’s imaginations. So, volume is exactly how much data we have to work with. Once, there were only Gigabytes of information. Now we have to handle Zettabytes (ZB) or even Yottabytes (YB), and business solutions and devices are generating more and more data at a frightening rate. To keep it short, we talk about Big Data when you have to deal with such crazy amounts of information.

2. Variety

Big Data is served in three types. These are unstructured, semi-structured and structured data, and they all can be leveraged in big data processing. In fact, the variety of data types (they come in different forms) is something very characteristic for Big Data. This “V” creates one of the biggest challenges in Big Data – it is not easy to organize such complex datasets in a sensible way.

Dealing with the variety of Big Data is a difficult task. It requires vast knowledge, experience in data science and a lot of algorithmic and computational power.

3. Velocity

Another of the Big Data 7 Vs is Velocity. This just means the speed at which data is processed and becomes accessible. Today, we generate new data very fast. Humanity is quite good with “producing” information, but how about processing it? Well, we have real-time processing now, right? Yes, but it requires more and more computing power to actually analyze these… Zettabytes of data.

Most data is stored in data warehouses before analysis – fortunately, in some cases, real-time analyses is not necessary. Still, the need for real-time processing of enormous volumes of information is increasing.

4. Variability

Two of the 7 Vs of big data – variety and variability – sound a bit similar, but it is important to understand the differences between them. Variability is about the fact that the meaning of some particular data changes all the time. This may sound confusing (again!). The real world meanings and interpretations of data depend on the context, so the meaning changes based on the changing circumstances. Moreover, when new meanings are created, the old ones get obsolete – and become invalid.

5. Veracity

High quality of data is essential for the success of an organization that bases its actions on the results of its analyses. There are multiple methods and metrics that are leveraged by data engineers in order to assess how good and reliable a dataset is.

In order to increase your company’s efficiency, you should work to ensure the highest quality of your business data. It is a big mistake to include inaccurate or incomplete information in your analysis. When it comes to Big Data, data streams always come from diverse sources – some more reliable than others. You will deal with duplicated, incomplete, inaccurate and totally useless data. This is a normal part of leveraging Big Data. The way to overcome this veracity-related challenge is to plan and perform an effective data cleaning process – with the right technologies and approach, you will be able to separate the wheat from the chaff.

6. Visualization

Let’s go back to the unimaginable volume, variety, veracity, and overall complexity of Big Data datasets – if they are indeed so unimaginable, how are they helpful for companies? One of the major tasks for those that work with Big Data is to process it and make it understandable – ready for human interpretation.

After analysis, data scientists leverage professional tools and software to convert the results of analytics into graphical formats for easier consumption. Still, commonly known spreadsheets and even three-dimensional visualization may not be enough to present multiple, complex relations between data and datasets. New business intelligence (BI) tools are introduced to the market all the time – you should think of choosing the most suitable ones for your company.

7. Value

If Big Data elements can come in huge volume, enormous variety and velocity, and are also characterized by variability and complexity, it is understandable that they may also have extraordinary value to an organization. And that is how we come to the last of the 7 Vs of Big Data – value.

Big Data has great business potential. Think of these countless datasets and patterns hidden in it – patterns that cannot be found by a human, but can be uncovered by machines. There is knowledge within the reach of those who invest in Big Data solutions and are ready to dive in and see the associations that remain invisible to others.

To sum up

Big Data offers more than new, effective methods of selling your products. It can provide you with information you can use to reshape your organization totally (by improving your business model, products and quality of service) and gain a competitive advantage. Big Data is utilized in many industries in order to increase efficiency and reduce the costs of running a business.

We encourage you to explore the world of enormous amounts of information further. Check out more than just the 7 Vs of Big Data we’ve mentioned in this article to understand it better. We understand that entering this domain may not be easy, so we will be happy to assist you. Contact us to learn more about Big Data. We can help you implement and leverage most of the modern business solutions that your company may require to become successful and outpace your competition.

We can help you make the most of your business data. Contact us for more information about our services.

Check out our blog for more details on Big Data:

• Real Time Big Data Analytics – definition, examples and benefits
• The difference between Big Data and Data Analytics
• The best data stream solutions and how to use them