Big data comprises datasets that are so huge or complex that it’s difficult or impossible to process using traditional methods. Storing & having access to large amounts of information for analytics has been around for some time. Thanks to technological revolutions such as greater access to massive volumes of data, Big Data With 5 v’s has a bright future, allowing organizations to gain more insights, increase performance, generate revenue, and evolve more swiftly. By the end of this article, you’ll be equipped with everything you need to know about Big Data.
What is Big Data?
Big Data a popular term, has come to be defined as a large amount of data that cannot be stored by traditional data storage or processing equipment. Due to the colossal amounts of data produced by human & machine activities, the data is so complex and expansive that it can neither be interpreted by humans nor fit into a database for analysis. However, when suitably evaluated using state-of-the-art tools, these massive volumes of data equip organizations with valuable insights that improve their business by making informed decisions.
What are the 5 Vs of Big Data?
Initially, there were 3 V’s of Big Data; now, there are Big Data With 5 v’s (volume, velocity, variety, veracity, and Value) are the 5 innate & primary characteristics of big data. Knowing the 5 Vs has allowed data scientists to derive more value from their data while also allowing the scientists’ organization to become more customer-centric.
1. Volume:
The name ‘Big Data’ itself suggests that it’s related to its enormous size. Volume is a huge amount of data. The size of data plays a crucial role in determining the value of data. If the volume of a dataset is very large, it is considered ‘Big Data. Whether or not a particular data can be considered Big Data depends on the volume of data. So, while dealing with Big Data, it is important to consider a characteristic ‘Volume’.
2. Velocity:
It refers to the high speed of accumulation of data. In Big Data, velocity data flows from various sources like networks, social media, machines, phones, etc. There exist a massive and continuous flow of data. This determines the potential of data and how fast the data is generated and processed to meet the demands. Sampling data can help in dealing with issues like ‘velocity.’
3. Variety:
It is related to the nature of structured, semi-structured, and unstructured data. It also refers to heterogeneous sources. Variety is the arrival of data from sources both inside and outside an enterprise. It can be structured, semi-structured, and unstructured. These types are discussed in detail ahead.
4. Veracity:
It is concerned with inconsistencies and uncertainty in data; that is, available data can sometimes get messy, and quality and accuracy are difficult to control. Big Data is also variable because of the multitude of data dimensions resulting from multiple disparate data types and sources.
5. Value:
After considering the 4 V’s, one more V stands for Value!. The bulk of Data having no Value is of no good to the company unless you turn it into something useful. Data is of no use or importance, but it needs to be converted into something valuable to extract Information. Hence, you can state that Value! is the most important V of all the 5Vs.
What are the types of Big Data?
As the Internet age grows, we generate incredible amounts of data each second. This data comprises a lot of tweets, emails, selfies, purchases, blog posts, and any other digital information we can think of. This data can be categorized into the following types:
1. Structured data – Structured data has predefined organizational properties and is present in structured or tabular form, making it easy to analyze and sort. Additionally, due to its predefined nature, each field is discrete and can be accessed jointly or separately along with data from various other fields. This makes structured data valuable, making it possible to quickly collect data from various locations in the database.
2. Unstructured data – Unstructured data has information with no predefined conceptual definitions and is not easily analyzed by standard databases or data models. Unstructured data accounts for most Big Data and comprise information such as numbers, dates, etc. Big data with V’s examples of this type include audio/video files and satellite imagery, to name a few. Photos that people upload on Instagram and videos watched on YouTube contribute to the growth of unstructured data.
3. Semi-structured data – Semi-structured data is a hybrid of structured and unstructured data. This means that it inherits a few characteristics of structured data but contains information that fails to have a definite structure and does not conform with relational databases or formal structures of data models. For instance, JSON and XML are typical examples of semi-structured data.
Why is Big Data so important?
The importance of big data with 5V’s cannot simply be reduced to how much data you have. The value lies in how you put it to use by taking data from a source and analyzing it. By doing this, you can find answers that streamline management, optimize product development, increase operational efficiencies, and provide new revenue and growth opportunities enabling smart decision-making. Further, when we combine Big Data with data analytics means Big Data Analytics, you can accomplish high-end business tasks such as:
1. Getting to the root causes of failures, issues, and defects in near-real-time.
2. Sharpening deep learning models’ ability to classify accurately and react to changing variables.
3. Spot anomalies faster and more accurately than the human eye.
4. Recalculating entire risk portfolios in minutes.
5. Improving patient outcomes by rapidly converting medical image data into insights.
6. Detecting fraudulent behaviour before it affects your organization.
Thus, these benefits of big data allow it to be used virtually across all industries. From Agriculture to Finance, all industries use data science to streamline various processes.
Conclusion!
Big Data With 5 v’s is generally stored in computers and analyzed using software designed to handle large, complex data sets. Nearly every department in an organization can utilize findings from big data analysis, but handling clutter and noise can pose problems. So, learning about Big data can be a great opportunity for people interested in the domain as the future certainly looks bright.
