what is big data

What is Big Data ?

Big Data, which has been rampant in the IT world since its inception, has become a hotly debated topic as AI begins to socialize rapidly. Like children’s books, artificial intelligence requires massive amounts of data for machine learning.

Scaling the height of the door and your doorbell in childhood made it possible to measure the height of your height. Examination of ripe haircuts for haircuts can help you determine how quickly you should marry one and two and see how thin and thin they are. Women can predict the next month’s menstrual cycle by marking the date of their menstrual cycle. All these are opportunities to see the future through data collection in everyday life.

If you want to look to the future as an exchange rate, those looking at stock market data can forecast their gains and gains, the key is to collect data. Big Data is its big opportunity.

What is Big Data

Big Data refers to information assets of high volume, high velocity and/or high variability. It is a cost-effective, innovative information processing system that can be used to better monitor, make decisions and automate processes.

Simply put, big data is broader, more complex data sets. It has three distinctive features:

  • Volume – The volume of data is very important. When dealing with big data, low-density, unstructured data can be processed. When one entity is a terabyte, another can be a petabyte. Sometimes the volume of these can be unlimited. Ex: Facebook / Twitter Feed
  • Velocity – Velocity is the high rate at which data is received and (sometimes) must be run on it. As such, they are usually sent to system memory before the hard disk. Real-time running devices may need to be run instantly.
  • Diversity – Diversity is the variety of data types available. Traditional datasets are structured and are relevant to relational databases. But big data is also available in unstructured or semi-structured text, audio, and video. Storing them requires pre-processing to support definitions and metadata.
    These datasets are so large that traditional data processing software cannot manage them. But these huge volumes of data can be used to solve business problems that were previously impossible to solve.

Use cases

big data

Big data helps to cover a range of business activities, from customer feedback to analytics.

Predictive models for new products and services can be developed by classifying key attributes of past and present products or services and demonstrating the relationship between those attributes and their commercial success. Data and analytics from research teams, research markets, social media networks are used to design, manufacture and launch new products.

Mechanical error forecasting can use unstructured data such as production year, equipment model, as well as millions of work logs, sensor data, error messages, and engine temperature. In the event of a machine malfunction, the entire organization can be prepared in advance to minimize the damage.

In machine learning, artificial intelligence programs can improve the level of intelligence available to the user. Data coding does not require any further coding, but the more experience it takes, the more data it will use to produce a more successful product.

Big data enables you to collect data from social media, phone calls, log entries, and other sources to enhance the interactive experience and maximize value in customer experience measurement. Any organization can go a long way toward satisfying the core customer base – customer satisfaction.

Studying the interdependencies of people, organizations, processes, etc. can help big data build new ways of doing it. Data analytics can be used to improve financial and planning decisions. Trends provide endless possibilities to test what customers need to offer new products and services, implement dynamic pricing, and so on.

How it works

Big data helps open the door to innovation and new business models. It requires three main actions:


Big data combines data from multiple sources and applications. Traditional Data Integration Mechanisms (ETL – Data Collection, Transformation, and Storage) are generally not compatible and require new strategies and technologies to analyze large data sets on a terabyte or petabyte scale.

When it comes to consolidation, the data must be brought in, processed and formatted in a way that business analysts can deal with it.


Big data storage needs to be done. This can be in a cloud, within an organization, or both. Data can be stored in any number of ways, and processes and processes can be applied to those data sets on demand.


The benefits of investing in big data come from analyzing the data and using it for business growth. This includes building visual analytics for data, exploring data for innovation, applying data models to machine learning and artificial intelligence.

Big data is a nutritious food business

Big data management can produce patterns and examples that are not normally detected and can be used to investigate how the business is performing at that time.

As a result of these findings, the business is able to predict when (good/bad) things will happen, and then change the requirements, ensuring continuity by changing the nature of the business relative to the trends. Then no more astrologers, feng shui statues, etc. will be the sole support of the business.

So if you have any issues or ideas related to this technology comment below. For reading other articles like this go through.

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