5Vs of Data Analytics

An easy way to understand data-centric business challenges

Introduction

In the year 2001, data scientists and analysts were introduced to the 3Vs of data, which are Volume, Velocity, and Variety. Over a period of time, data analytics as a field saw a significant change in the way data is captured, processed and analysed.

Data is currently growing so rapidly in size that is now known as big data. With the enormous growth of data in recent time, two new Vs namely Value and Veracity have been added to the data processing concepts.

Volume — the data storage

When businesses have more data than they are able to process and analyze, they have a volume problem

• Today data is generated from various sources in different formats. Some of these data formats include word and excel documents, PDFs and reports along with media content such as images and videos. Due to the data explosion caused to digital and social media, data is rapidly being produced in such large chunks, it has become challenging to store and process it using conventional methods of business intelligence and analytics.
• Enterprises must implement modern data storage techniques and business intelligence tools to effectively capture, store and process such an unprecedented amount of data.

Velocity — the data processing

When businesses need rapid insights from the data they are collecting, but the systems in place simply cannot meet the need, there’s a velocity problem

• Velocity refers to the speed at which the data is generated, collected and analyzed. Data continuously flows through multiple channels such as computer systems, networks, social media, mobile phones etc. Now, this data should also be captured as close to real-time as possible, making the right data available at the right time. The speed at which data can be accessed has a direct impact on making timely and accurate business decisions.
• Even a limited amount of data that is available in real-time yields better business results than a large volume of data that needs a long time to capture and analyze.

Variety — the data structure and types

When your business becomes overwhelmed by the sheer number of data sources to analyze and you cannot find systems to perform the analytics, you know you have a variety problem

Data sources may involve external sources as well as internal business units. Generally, data is classified as structured, semi-structured and unstructured data.

• Structured data is hot, immediately ready to be analyzed.
• Semistructured data is lukewarm — some data will be ready to go and other data may need to be cleansed or preprocessed.
• Unstructured data is the frozen ocean — full of exactly what you need but separated by all kinds of stuff you don’t need.

While structured data is one whose format, length and volume are clearly defined, semi-structured data is one that may partially conform to a specific data format. On the other hand, unstructured data is unorganized data and doesn’t conform with the traditional data formats. Data generated via digital and social media (images, videos, tweets, etc.) can be classified as unstructured data.

Veracity — the cleansing and transformation

When you have data that is ungoverned, coming from numerous, dissimilar systems and cannot curate the data in meaningful ways, you know you have a veracity problem

• The veracity of data is the assurance of the quality or credibility of the collected data. Since big data is vast and involves so many data sources, there is the possibility that not all collected data will be of good quality or accurate in nature.
• Hence, when processing big data sets, it is important that the validity of the data is checked before proceeding for processing.

Value — reporting and business intelligence

When you have massive volumes of data used to support a few golden insights, you may be missing the value of your data

• Data from which business insights are garnered add ‘value’ to the company. In the context of big data, value amounts to how worthy the data is of positively impacting a company’s business. With the help of advanced data analytics, useful insights can be derived from the collected data.
• These insights, in turn, are what add value to the decision-making process.

To Conclude

Data is the oil of the 21st century and organizations today in different industries are realizing this quickly. Insights derived from high volume, high velocity and validated data collected from varied sources can add value to the overall decision-making of the company. While most organizations today do have the intent to use data, many are struggling to effectively capture, store, process or harness it.