There have been numerous discussions on big data, which most people are unaware of. Tech gigs often adopting various big data catchphrases or idioms into common use before fully absorb their meaning.
Today, in this blog, we will focus on the idea by delving further into the frequently-mentioned “five V’s of Big Data.”
What is Big Data?
Big Data refers to large amounts of data in any of these three forms: structured, unstructured, or semi-structured. All kinds of sources provide this seemingly endless stream of information. Although impressive, the volume of data is mostly useless without first being gathered, evaluated, and transformed into knowledge that can be used.
History of the Five V’s of Big Data:
Initially, it started in 2001 with three V’s: Volume, Velocity, and Variety. Then, Veracity was added as it progressed, giving it a total of four V’s. Then Value was added, bringing the total to 5V’s. From then onwards the number of V’s are growing according to the demand and necessity of the industry.
Evolution of 3V’s of Big Data Characteristics:
Traditional methods of data collecting, curation, and processing are becoming more inadequate as big data expands to include ever more data sets. Data sets are produced at an unprecedented rate, from every corner of the globe, every second. This means that the total volume of data will never decrease, but will instead grow exponentially.
Hence, the Industry analyst Doung Laney defined big data using the three V’s in 2001. The first 3 V’s of Big Data are:
- Volume: The amount of data generated
- Velocity: the speed of data generated
- Variety: Structured and unstructured data
The 4 V’s of Big Data:
Variability and Complexity are two new dimensions introduced to SAS (Statistical Analysis System). Additionally, Oracle defined big data using the four V’s, and iSAS (Statistical Analysis System) added two more. Variables, such as complexity and variability. Additionally, Oracle has outlined the four V’s of big data as follows:
- Volume
- Velocity
- Variety and
- Veracity
What Are the eight V’s of Big Data, and How Do They Work?
Once you have a firm grasp of the difficulties posed by Big Data and the business intelligence tools you can use to overcome them, you will be able to answer queries thaat were previously out of reach.
Hence, Big Data characteristics are best described by the famous five V’s of Big Data. Decision makers must be aware of its difficulties, often known as its five V’s, in order to maximize its value. The famous V’s of Big Data are:
- Volume
- Velocity
- Variety
- Veracity
- Value
- Visualization
- Value
- Validity
This blog will focus on the eight most crucial V’s of Big Data Characteristics:
- Volume:
The term “volume” describes the massive amounts of information that flood businesses nowadays. There is no longer a need for businesses to collect, organize, and keep their data on-premises. When I started working for companies 15 years ago, we managed terabytes of data.
As a result of things like transaction processing systems, emails, social networks, customer databases, website lead grabs, monitoring devices, and mobile applications, the amount of data that exists today may easily exceed a petabyte (1,000 terabytes).
Even Nevertheless, by 2025, the world’s data will be double every day, from 181 to 312 zettabytes. Managers use data management systems, data warehouses, and “data lakes” to deal with this deluge of information. They employ cloud storage or service providers like Google Cloud to keep their data safe.
- Velocity:
The volume of “big data” is expanding rapidly. To put this in perspective, every minute, there are around 3,400,000 emails, 4,595 SMS, 740,741 WhatsApp messages, roughly 69,000 Google searches, 55,000 Facebook postings, and 5,700 tweets, as reported by Zettaspere.
Until recently, data scientists used automated batch processing to scan big files and prepare reports to measure incoming data. The current state of affairs is such that batch operations cannot cope with the flood of real-time data from an increasing number of sources.
Data also quickly become outdated, which is of much greater importance. Businesses that want to remain competitive need reliable business intelligence (BI) technologies to help them make choices quickly.
- Variety:
The term “variety” describes the many forms of digital data that flood businesses and the methods used to analyze and draw conclusions from them. Once upon a time, companies relied heavily on the information they gleaned from internal databases like Excel and other spreadsheet programs that could accommodate organized data.
Emails, customer comments, text messages, social network postings, data from sensors, raw data, photos, and videos are just a few examples of unstructured information flooding the modern world and evading control. Businesses often have trouble with real-time data analysis, processing, and digestion.
- Veracity:
Since it is the foundation upon which a successful company may be built, Veracity is undoubtedly the most crucial of the five Vs. Only with complete and accurate data can a firm profit and effect positive change.
For businesses to benefit, data must be accurate and free of errors. That is, if the information is correct, precise, trustworthy, consistent, impartial, and comprehensive. Factors that contribute to contamination include:
- Statistics distort the truth of a certain market.
- Senseless details that skew the results
- Dataset items that are out of the ordinary and need special attention
- software flaws can result in skewed data
- Security flaws in computer programs that malicious users might use to steal information
- When humans make errors in reading, processing, or analyzing data, the outcome is often inaccurate.
- Value:
To get an edge in business today, you need access to big data. But only if you can turn your knowledge into actionable conclusions.
Insightful users may profit from the information by:
- By exposing their company data, they want to inspire confidence among their customers.
- Improving management’s decision-making by amassing more complete and reliable data about the company’s performance
- tailoring specific offerings to certain niches of consumers.
- Avoiding harm and discovering previously unknown information
- Making improvements for the future of goods and services
- Visualization:
In today’s environment, visualization is essential. Using charts and graphs to depict vast volumes of complicated data is a far more effective method of communicating meaning than using spreadsheets and reports filled with numbers and algorithms.
- Value:
The ultimate goal is value. You want to make sure your company is benefiting from the data after spending considerable time and money addressing its volume, velocity, variety, unpredictability, validity, and visualization.
- Validity:
Validity and Veracity have certain commonalities. Big data validity refers to the extent to which the data is suitable for its intended use, as suggested by the term itself. Dark data describes the large proportion of enormous data that is still useless. The remaining unstructured data is cleaned up before being analyzed.
What are the other V’s?
As time passed, according to the requirement and prominent advancements in Big Data, the characteristics are further divided into multiple V’s as mentioned below:
- Viscosity (complexity or degree of correlation),
- Variability (inconsistency in data flow),
- Volatility (durability or how long-time data is valid and how long it should be stored),
- Viability (Capability to be live and active),
and contains many other V’s of Big Data as time processed.
Conclusion:
Big Data will continue to rise in importance throughout the top V’s of Big Data. Customers from numerous services will reap substantial benefits as a result of the new advantages made possible by Big Data and AI.
