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Big Data Analytics – Fundamentals

Learn about the Big Data, Big Data Analytics, and Fundamentals of Big Data Analytics (Big Data Types, Big Data Characteristics).
Submitted by IncludeHelp, on December 27, 2021

Big Data

"The data which is big in size, variety, and velocity is known as big data. Big Data is a field that is dedicated to the analysis, processing, and storage of massive amounts of data that are frequently derived from a variety of different sources."

It is a branch of data science that investigates the use of a variety of tools, approaches, and strategies to analyze extraordinarily big and complicated data sets, deconstruct them, and systematically extract insights and information from them.

The real problem in a large business is to extract the greatest value from the data that is now accessible while also predicting what types of data will be collected in the future. How to take existing data and make it relevant so that it gives us correct insight into the past is one of the important conversation points in many executive meetings in organizations, and it is one of the most common topics of debate. Big Data is an extremely broad term with numerous actors - each with their own architecture, vendor, and technological preferences – to be fully understood.

Big Data has the potential to fundamentally alter the nature of a company. In fact, there are many businesses whose whole survival is predicated on their ability to provide insights that can only be obtained through the use of Big Data. The goal of every business and expert is the same: to extract the greatest amount of information from the data; however, the route and beginning point is different for each organization and expert, respectively. As organizations evaluate and construct big data solutions, they are also learning about the challenges and opportunities that come with big data management and analytics.

Businesses must realize that Big Data is about more than simply technology; it is also about how these technologies may help a business go forward in a positive direction.

The use of Big Data extends the capabilities of classic analytic approaches based on statistics by incorporating contemporary techniques that make use of computer resources and methodologies to run analytic algorithms. This transition is critical as databases continue to grow in size, diversity, and complexity, as well as becoming more streaming-centric. While statistical procedures have been used to approximate measures of a population since Biblical times, breakthroughs in computational science have made it possible to process whole datasets, hence eliminating the need for such sampling.

Big Data Analytics

The process of identifying, procuring, preparing, and analyzing large amounts of raw, unstructured data in order to extract meaningful information that can be used as an input for identifying patterns, enriching existing enterprise data, and performing large-scale searches is known as Big Data analytics.

Organizations of varying sizes and types employ data analytics tools and methodologies in a variety of ways. Take, for example, the following three industries:

In business-oriented organizations, the outputs of data analytics can be used to reduce operational expenses while also facilitating strategic decision-making processes.

When applied to the scientific arena, data analytics can aid in the identification of the underlying cause of an event, hence improving the accuracy of forecasts.

When applied to service-based environments, such as public sector organizations, data analytics can help to increase the focus on delivering high-quality services while simultaneously bringing costs down.

Big Data Types

Big Data can be divided into three categories, according to most experts. These are as follows:

  • Structured Data
  • Semi-Structured Data
  • Unstructured Data

Structured Data

  • Structured Data has its own data model, which is distinct from other data types.
  • It has a clearly defined organizational structure.
  • A consistent sequence of events occurs.
  • It has been created in such a way that it is simple to use and navigate.
  • It can be accessed by a person or by a machine.
  • Databases, as well as well-defined columns, are common places for structured data to be saved.

Semi-Structured Data

  • Semi-Structured Data can be considered as another form of Structured Data.
  • Although it shares certain characteristics with Structured Data, the vast majority of this type of data does not have a clearly defined structure.
  • It does not adhere to the formal structure of data models such as relational database management systems (RDBMS).

Unstructured Data

  • A completely separate sort of data, unstructured data does not have a structure and does not conform to the formal structural norms of data models; it is a completely new type of data.
  • It does not even have a consistent format, as it has been discovered to be constantly changing.
  • However, it is possible that it will contain information pertaining to data and time.

Big Data Characteristics

Big Data Characteristics explain the remarkable potential of Big Data. Following are the characteristics of Big Data

  • Volume
  • Velocity
  • Variety
  • Value
  • Veracity

Volume - The incomprehensible volumes of information generated every second by social media, cell phones, automobiles, credit cards, M2M sensors, photos, video, and other sources are referred to as "volume."

Velocity - The rate at which multiple sources generate large amounts of data on a daily basis is referred to as velocity. This stream of information is both continuous and vast.

Variety - Variety refers to the different types of data created by heterogeneous sources that are presented in different ways. It can be structured, unstructured, or semi-structured, depending on the situation. In contrast to traditional data such as phone numbers and addresses, the most recent trend in data is in the form of images, videos, and audios, among other things, resulting in around 80 percent of the data being fully unstructured (or unorganized).

Value - The amount of data that is helpful and understandable is referred to as its value. The most important issue that we must address is the issue of value. It is not only the volume of data that we keep or process that is problematic. To be more specific, it is the volume of valuable, dependable, and trustworthy data that must be collected, processed and evaluated in order to uncover insights.

Veracity - When it comes to data reliability, it refers to the trustworthiness of the data in terms of its quality and accuracy, as well as the level of reliability that the data has to give. Because a large portion of the data is unstructured and unimportant, Big Data must discover an alternative method of filtering them or translating them out, as the data is critical in the development of new commercial ventures.


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