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Understanding Big Data

Big data is a term that is typically used to refer to huge volumes of structured, semi-structured and unstructured data that can possibly be mined for information and applied in machine learning projects and other high-level analytics applications. The data renders itself difficult to collect, manage, and process with ordinary databases due to its size.

Initially, big data was associated with the following characteristics: volume, variety, and veracity. More recently, big data has come to be described using three other characteristics: veracity, value, and variability.

Some things, like internet of things (IoT), artificial intelligence (AI), social platforms, and mobile phones, can be linked to the increasing data complexity, as the former are contributing to the latter through various new types and sources of data. For example, tons of data come from devices, web log files, transactional software, sensors, networks, and social media, among others. Most of the data from these sources is produced instantaneously and in humongous quantities.

Looking at the Vs of big data

The following are the characteristics that are used to describe big data:

Volume

Volume has to do with the amount of data generated and stored from a wide array of sources. The size of data defines its value and potential insight, and if it can be taken as big data or not. However, it depends on the organization of the data user, since what can be considered as big data by one user may not be seen as big data by another.

Variety

As earlier mentioned, big data is in various forms—structured, semi-structured, and unstructured—and the data is also of different natures. The varying types of data allow those who analyze them to make effective use of the insights drawn from them.

Velocity

Velocity has to do with the speed at which data is produced and processed to resolve issues and bring about development. Big data is often availed in real time, and unlike small data, it is produced more continually.

Veracity

The veracity of data is the extent to which the data can be relied on or trusted.

Value

This considers if the data collected has some value to the business or the users. Data has intrinsic value, but until the value is revealed, the data may not be of much worth.

Variability

This is the different ways in which data can be used and manipulated or formatted, mainly referring to transactional data that is less consistent than normal. The data may have several meanings or can be manipulated in different ways from one source to another.

Big data uses

Big data analysis enables users like researchers, analysts, businesspeople, and others to make well informed and faster decisions using data that was hitherto inaccessible or unusable. Businesses must employ cutting-edge analytic techniques, like machine learning, statistics, natural language processing, and more to acquire new understandings from non-utilized data. They can use such data individually or with other existing data from the business.

To make use of the data, these users need certain skills to do the analysis, but if they lack expertise, they can visit Active Wizard website to get in touch with big data experts, whom they can hire for their projects.

Some ways in which big data can be of use to a business include:

  • Using the data to improve customer experience by giving personalized services and offers, and acting on feedback
  • Analyzing equipment data to do predictive maintenance and avoid breakdowns
  • Identifying customer needs and developing new products to meet them
  • Streamlining efficiency by anticipating demand and customer traffic, and evaluating production, all of which help to have smooth operations
  • Detecting and preventing fraud through analysis of abnormal patterns and events that may show fraudulent activities

Conclusion

Big data can be a game changer for a business or organization, but only if proper analysis, interpretation, and implementation of data is done. The data can help make better and quicker decisions as well as reduce costs, improve customer loyalty, and streamline operations.

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