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Big Data – How Beneficial it is in Today’s Competitive Era

Big Data – How Beneficial it is in Today’s Competitive Era

 

 

Data has always been the core system of any organization, be it small, medium or large. Be it intelligent business decisions, market planning, or campaigns to run a business needs sound and accurate data to be successful.

Today, data is everywhere. Data is generated from many sources, at various time frames with different types of data. While traditional data stores have been growing in size over time, as technology has advanced and become lower cost making it accessible to all organizations.The advent of this phenomenon brought on the age of Big Data!

History of Big Data?

To understand the advent of Big Data we need to visit the time when Big Data came into existence.

While Big Data is something that is not completely new, its existence came into being in the early 1990s by John R Mashey who coined the term Big Data. The unfathomable amount of data in the world which is expected to rise by a magnitude over the next several years is sparked by the many Internet of Things connected devices. With this rising volume of data sets, it has become impossible to analyze this humongous volume of data which requires new methods and techniques to harness its value.

What is Big Data?

Big Data is the large volume of data that collects at the backstage of any organization over time and is significant for any successful management of any business. Big Data is mostly semi-structured or structured data, but mostly semi-structured data which means it is a loosely-coupled collection of texts and information that does not have any pre-defined data model. In one word, they are full of ambiguity, which brings the need to understand and analyze- hence the rise of Data Scientists in the world.

The Three Vs of Big Data

 

 

Big Data has three distinct characteristics that define it. This is also known as the three ‘Vs’ of Big Data – Volume, Velocity and Variety. While there have been a few other Vs that have been developed in recent times in association to Big Data, these three Vs continue to describe Big Data in its true sense. These three Vs are discussed in detail below to provide a better understanding of Big Data.

Volume: 

Volume has been the key force that has given Big Data its name. Volume is the exponential size in which Big Data is emerging in today’s time. The emergence of social media has been a significant and silent contributor to this exponential increase in volume of Big Data.

Velocity:

As the term suggests, velocity is the pace with which Big Data is being generated and accumulating each day.  In times before social media, data was generated manually with many businesses. In other words, this V refers to how fast data is created.

Variety:

Variety is the type and form in which data is being generated. Both structured and semi-unstructured form of data is generated through three main types. Text, audio and video which is mainly due to the advent of new technology, accessibility to the technology and the participation of the population.

The Value and Veracity of Big Data:

While the core three ‘Vs’ of Big Data, there are two more Vs that have aligned themselves with the characteristics of Big Data. Value and Veracity.

There is no doubt that data holds immense importance but the real significance lies when true value is generated from those streams of structured and semi structured data sets. And this value may be realized when the accuracy or the veracity of Big Data is achieved.

Benefits of Big Data

 

 

Companies and organizations have overcome the hurdles of technology and high costs over time and have arrived at an era where they are able to store anything and everything that they can generate. This has become the foundation and blueprint of successful  businesses. For every business decision made, you need Data to validate and confirm decisions which define the future steps and plans for competitive advantage.

Risk Identification:

Risks are an integral part of every business, especially because it often involves a third party. While no business can operate eliminating all risks, data enables better visibility into the risks reducing the exposure to bad outcomes. Big Data and analytics play an important role in risk identification and insight for any business to overcome the potential risk.

Fraud Prevention: 

In financial services, Big Data predictive analysis enables identification of fraud and money laundering. By analyzing large volumes of data real time and comparing this data to historical patterns enables analysts the ability to trace transactions from many sources to many targets.  This approach can be used to now identify fraudulent patterns and reduce / eliminate fraudulent activities.

Reduces Churn Rate of an Organization:

There is nothing more painful than losing customers. While there is always a possibility for every business to lose its customers at some point in time, Big Data Analysis provides insight into previous history to identify these patterns.

Credit Management:

Credit is a real time decision that best in class business use as a competitive advantage. Big Data with its real time analytics and detailed study of prevalent customer behavior around credits can help in credit management thus, reducing the loss.

Operational Efficacy:

Operational efficacy is one field in which Big Data is earning its utmost importance. Analyzing production efficacy, customer feedback, customer returns, Big Data makes its way into the operational system effectively and helps in constructing effective operational processes for business to anticipate future demand and act accordingly.

How challenging is Big Data Analytics?

While Big Data has emerged as a big contributor to organizations, it also holds certain challenges.

It is Complicated Process:

Big Data Analytics is a complicated process that needs highly technical and preferred skills for the engineers to extract, translate and load data for initial analysis.  Often, it is unknown whether data quality is valid.  

It is Time Consuming: 

Big Data Analytics requires time to analyze and requires resources like compute and storage. This may become challenging especially if results are needed immediately.  

Expensive to Maintain:

The expense to maintain the data and the infrastructure may increase over time  which may increase the investment of any business. Cloud databases may be an alternative, however it is important to determine the value of data and determine if the amount of resources to manage and maintain are justified.

Endnote :

As Daniel Keys Moran had rightfully said, “you can have data without information but you cannot have information without data”. With the constant rise in the volume of data in the world, the term Big Data is not a misnomer anymore. Especially when we look into the fact that the world has 94 zettabytes of data as of 2021 which is equivalent to 94 trillion gigabytes. With more and more data being generated, it has become immensely important for any organization to be able to analyze and find insights that enable a business to make decisions rapidly and achieve competitive advantage.

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