Everything You Need To Know About Big Data

The term ‘Big Data’ was practically unknown in early 2010, but it was widely reported in mid-2011 that this was an exaggerated current phenomenon. From product vendors to large-scale outsourcing and cloud service providers, the term has been adopted by everyone like before ‘cloud computing’.

What is Big Data?

In a nutshell, business data is rapidly gaining new and emerging data sources, including location data, online public information and sensor data embedded in vehicles, buildings and other products created by big data, social media channels, smartphones and other roaming devices.

Defining Big Data: The 3 V Model. Many experts use the 3V model to define Big Data.


The three V’s represent Volume, Velocity, and Variety.

Volume

Big data involves analyzing relatively large amounts of information, usually starting in the tens of thousands of terabytes.

Velocity

Velocity ​​represents the sheer speed at which data is generated and transferred. For example, data related to the Twitter hashtag often have high speeds. Tweets fly in blur. In some cases, they move so fast that the information in them is not easily stored, but still needs to be analyzed.

Variety

Variety describes tells that Big Data can come in many different formats and structures. For example, social media platforms and sensor networks generate an ever-changing stream of data. Text and other types, which include geographical information, images, videos and audio.

Value

The fourth key V 3V model is a useful way to define Big Data, and here we will focus on the fourth key V – value. It makes no sense for companies to implement a big data solution that can see how they can increase business value. This does not just mean using data within their own organization – value may come from selling it or providing access to third parties. This drive is a key business imperative to increase the value of Big Data.

Big Data can provide new ways to create value for companies. For example, although traditional business analytics systems need to run weeks or months of outdated historical data, a large data solution can analyze information generated in real-time (or at least in real-time).

It can have significant benefits for companies rapidly responding to market trends, threats, and improvements. Additionally, Big Data Applications can add value by evaluating data feeling rather than looking at raw facts (for example, they can understand how consumers feel about a particular product). It’s called ‘semantic analysis’.

Growing artificial intelligence methods can be used to conduct complex ‘fuzzy’ searches and to uncover new, previously unseen, data-driven market insights.

In a nutshell, Big Data offers companies the opportunity to gain added value by using a combination of existing data, volatile data, and externally available data sources:

  • Improved business insights can lead to more informed decision-making
  • Processing data as an asset that can be traded and sold.

Therefore, companies need to monitor both Big Data’s long-term goal – to integrate multiple data sources to unlock more potential value – and their current technology is accuracy, immediacy and flexibility.

Big data is not new in many respects. It is a logical extension of many data analytics systems and concepts, including data warehouses, knowledge management (KM), business intelligence (BI), business insights and other areas of information management.

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