Innovations in modern technology are providing businesses with a wealth of useful information through big data. There is an endless amount of data available regarding everything from user behaviours online to the Internet of Things sensors tracking connected devices.
For organizations, this data can help them understand their customers and optimize all their company processes. There are five main characteristics of big data, and these are called the five Vs. By understanding these five Vs, you can better understand big data, and how you can use it.
What is big data?
Big data is data that is unable to be processed conventionally by humans due to the sheer volume of it. While there is no set amount of data required for it to qualify, if it’s impractical for it to be analyzed by humans, you can call it big data. By breaking down this data and analyzing it, organizations can gain tremendously valuable insights that they can implement in various ways, including programming, design, and marketing.
Not just anyone can look at big data and glean valuable information from it. You will still need an online data science masters (or an employee with one) in order to dissect the big data and make informed decisions based on your findings.
The five Vs are the best way to break down big data and show how it can be useful for all companies and organizations.
Volume refers to the actual amount of data that is accessible to collect, which is a lot. Volume is used to determine if a source of data is actually big data, or if it’s not. The value that a bank of data has is directly related to its volume. The more traffic that you have to a website, mobile site, or application, the more data there is available to collect. The more information you can collect, the more accurate insights you can gain from it. That is because you will have more user actions to analyze in order to paint a better picture of average user behaviours.
Velocity is the speed at which information is produced, gathered, and analyzed. Data is being generated at an immense speed since it is coming from so many different channels. You have data flowing in from your website, social media, networks, and mobile phones. The faster you can collect this data and analyze it, the better.
It ensures that your analysis is up to date, which is very important in a world where everything is moving and changing quickly. Even waiting a week to implement changes based on data could be too late. You can make more accurate decisions for your business by having an efficient data collection and implementation system in place.
For example, if you’re selling hoodies on your website and one colour is selling extremely quickly, you’ll want to restock it straight away to avoid selling out and missing out on additional sales. If there is a colour that isn’t selling at all, you can avoid restocking and wasting money on a product that isn’t in demand.
Variety is the type of data that is collected, and this breaks down into three further classifications: structured, semi-structured, and unstructured. Big data comes from so many different sources that not everything will come in the same format. Some data is easy to define, and in a format that is straightforward for your systems to collect and organize. Other forms don’t follow traditional data formats and therefore, cannot be organized.
The easiest way to describe the difference is that structured data can be separated into a row and column structure, while unstructured can’t. Around 80% of data is unstructured and includes formats such as videos, images, and social media posts. Semi-structured data falls between the two, and while it doesn’t have fixed fields, it does provide elements that will help separate the data from other data sets.
Validity or veracity describes the credibility of the collected data. The quality of the data will define how accurate and useful it is. If there are inconsistencies throughout a particular data set, then it is not very credible. With such vast amounts of collected data, there is both useful and accurate data and messy and unreliable data. You’ll need to have systems in place to process your big data and data science professionals who can analyze the data sets and identify when data looks inaccurate.
Low-veracity data contains a higher volume of meaningless information, while high-veracity data provides more value. Individual data sets will provide more valid and accurate data, while others might only contain small snippets, but all streams of data are worth analyzing.
Value is potentially the most important characteristic of big data. You can have a high volume of varied data, process it quickly, and it can be valid, but if it provides no value, then the rest of the steps are pointless. Using big data is only effective if the information provides valuable insights that are useful to your business. Big data on its own isn’t that helpful. It must be converted to a format that can be analyzed in order for you to be able to extract valuable information.
Organizations will often invest part of their budget into collecting data and creating an extensive data storage system. The issue is that merely having these large banks of data isn’t enough to provide value. It’s what the companies do with the important information that counts. The best way to establish whether big data is worth the corresponding investment is to calculate the cost vs. the benefits. Have a data science professional work out how much it will cost to process the big data compared to the company’s projected return on investment.
Big data has the potential to dramatically impact the success of businesses when used effectively. Regardless of which industry you’re in, there is so much information that you now have access to. By implementing big data collection and processing, you can learn a lot about your clients or customers. The most beneficial insights come from high volume, varied, and valid data, which are the catalysts for new processes that will help your business grow.