Big Data Analytics

Big Data Analytics Details


The definition of big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs.

Put simply, big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.

Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques—like clustering and regression—and apply them to more extensive datasets with the help of newer tools.

Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.

1. Collect Data

2. Process Data

3. Clean Data

4. Analyze Data


The three Vs of big data



The amount of data matters. With big data, you’ll have to process high volumes of low-density, unstructured data. This can be data of unknown value, such as Twitter data feeds, clickstreams on a web page or a mobile app, or sensor-enabled equipment. For some organizations, this might be tens of terabytes of data. For others, it may be hundreds of petabytes.


Velocity is the fast rate at which data is received and (perhaps) acted on. Normally, the highest velocity of data streams directly into memory versus being written to disk. Some internet-enabled smart products operate in real time or near real time and will require real-time evaluation and action.


Variety refers to the many types of data that are available. Traditional data types were structured and fit neatly in a relational database. With the rise of big data, data comes in new unstructured data types. Unstructured and semistructured data types, such as text, audio, and video, require additional preprocessing to derive meaning and support metadata.




Big data benefits


Improved decision-making

Big data is the key element to becoming a data-driven organization. When you can manage and analyze your big data, you can discover patterns and unlock insights that improve and drive better operational and strategic decisions.


Increased agility and innovation

Big data allows you to collect and process real-time data points and analyze them to adapt quickly and gain a competitive advantage. These insights can guide and accelerate the planning, production, and launch of new products, features, and updates.


Better customer experiences

Combining and analyzing structured data sources together with unstructured ones provides you with more useful insights for consumer understanding, personalization, and ways to optimize experience to better meet consumer needs and expectations.


Continuous intelligence

Big data allows you to integrate automated, real-time data streaming with advanced data analytics to continuously collect data, find new insights, and discover new opportunities for growth and value. 


More efficient operations

Using big data analytics tools and capabilities allows you to process data faster and generate insights that can help you determine areas where you can reduce costs, save time, and increase your overall efficiency. 


Improved risk management

Analyzing vast amounts of data helps companies evaluate risk better—making it easier to identify and monitor all potential threats and report insights that lead to more robust control and mitigation strategies.

Date : 07 Oct, 2023

  • Big Data Analytics

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