What Are The 5 V’s For A Business Analytics Tool?

Big Data has changed how organizations view their data, especially from a business analytic tool perspective. We must thoroughly examine the 5 V's connected with it—Velocity, Volume, Value, Variety, and Veracity—to comprehend when 'data' becomes 'Big Data' and what its essential components are. Understanding big data can help figure out the 5 V's for a business analytics tool. 

Let's first understand the concept of Big Data. 

Big Data is a relatively new area of data science that investigates ways to dissect and analyze big data sets to extract their knowledge and insights systematically. Until now, Big Data analysis, storage, and collection have not been well served by standard data processing technologies. 

As a result, businesses using conventional business analytics software systems cannot fully realize their potential. We must consider the five V's of a business analytics tool comprising Big Data to comprehend its complexities. 

The 5 V's to Keep in Mind 

Data collection and handling have seen much change throughout time in data analytics and a BI dashboard tool. The three 3Vs of 3D data—Volume, Velocity, and Variety—were first proposed to data scientists and analysts in 2001 by the analytics company MetaGroup (now Gartner). Data expanded so quickly due to this growth that it became known as Big Data. Gartner has added two more V's—Value and Veracity—to the data processing principles in response to the exponential expansion of data. 

1. Velocity 

Making timely and correct business choices and understanding what is Business Intelligence is directly impacted by how quickly data can be accessible. Multiple channels carry data, including computer systems, networks, social media, mobile phones, etc. 

Now, to have the correct information accessible when needed through the BI dashboard tool, this data should also be recorded as nearly in real-time as feasible. Even a little real-time data may provide better business outcomes than a vast volume of data that takes a long time to collect and process. The speed at which data is created, gathered, and processed is called velocity.

Thanks to several Big Data technologies, we can now gather and analyze data in real-time as it is being created.

2. Volume 

Big Data volume refers to the ‘amount’ of data generated. Data size has an impact on the data's usefulness as well. 

Due to the data explosion brought on by digital and social media, it is more difficult for businesses to store and interpret data using traditional business intelligence and business analytics software techniques. Today, organized and unstructured data are created from a variety of sources. Word and Excel documents, PDFs, reports, and media files like photographs and videos, are just a few examples of various data forms a business analytics tool can create. Businesses must deploy current business intelligence solutions to acquire, store properly, and interpret such an unprecedented quantity of data in real time. 

3. Value 

Even if data is created in huge piles, just picking it useless. Instead, data that yields business insights add value to the organization. Value in the context of big data refers to the data's potential to enhance a company's operations using a business analytics tool. 

Big data analytics is of great use in this situation. Many businesses have invested in building out storage and data aggregation infrastructure within their organizations, but they fail to realize that data aggregation does not equate to value addition. What counts is what you do with the information you get. Advanced data analytics and BI dashboard tool may be used to gain practical insights from the gathered data. The decision-making process benefits from these insights in turn. 

4. Variety 

A business analytics tool processes various data types obtained from many data sources, in addition to the volume and velocity of data, which are essential aspects that bring value to a company. Businesses may use both internal business units and external sources of data. Big data is often divided into three categories: structured, semi-structured, and unstructured data. Semi-structured data may partly comply with a particular data format, while structured data is one whose format, length, and volume are precisely determined. 

Unstructured data, on the other hand, is disorganized and doesn't follow the standard data forms. Images, videos, tweets, and different types of data created by digital and social media may be categorized as unstructured data. 

5. Veracity/Validity 

The surety of the quality or trustworthiness of the compiled data is the integrity of business analytics software, or validity as it is more frequently called. Can you trust the information you've collected? Is this information reliable enough to conclude from? Should we base our business choices on the conclusions drawn from this data's insights? When the accuracy of the data is established, it may resolve all these problems and more. 

Not all data gathered will be of high quality or accurate since Big Data is so extensive and contains so many data sources. As a result, verifying the data's legitimacy is crucial before continuing to process massive data sets. 

Summing Up-

Grow’s business analysts understand that data is the oil of the twenty-first century. While most businesses intend to utilize data today, many find it challenging to collect, store, process, or use information efficiently. Our business analytics tool helps companies base their entire decision-making to benefit from insights from high volume, high velocity, and verified data from many sources. 

With its array of business analytics software and BI tools, Grow aids medium and enormous enterprises in efficiently using data. We support businesses at every stage, from developing a solid BI strategy to implementing a data warehouse, integrating real-time data, and using sophisticated analytics.