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Big Data

What is Big Data?

What is Big Data?

Big Data is a massive set of data that continues to expand dramatically over the period. It is a data collection that is so huge and complicated that no standard data management technologies can effectively store or interpret it. Big data is like regular data, but it is much larger. It is among the most significant inventions of the digital world. Patterns and correlations concealed in massive datasets disclosed by solid analytics ensure strategic and operational decision-making across nearly every business. Big Data has infiltrated practically every area of our lives, including our purchasing patterns and everyday consumer decisions!

Why do we need Big Data?

The amount of data available doesn’t solely determine the value of big data. The uses of big data and how it may be leveraged to meet human requirements make it desirable. Several reasons are making big data important today, such as:

  • Enhance production profitability by optimising resource utilization.
  • Generate new income and growth prospects
  • Augment research and development
  • Discover the cause of the malfunction, difficulties, and flaws in the system.
  • Reconfiguring the whole vulnerability assortment in no time!
  • Strengthening the potential of deep learning approaches to detect and respond to altering variables effectively.

What are the types of Big Data?

There are three types of Big Data:


Structured data is any data that can be stored, retrieved, and interpreted in a specified format. Computer science expertise has grown more successful in inventing strategies for this type of data and extracting value from it.


Unstructured data is any data that has an undefined structure or organisation. Unstructured data presents various issues in terms of handling to derive value from it, in addition to its enormous quantity. A good example is heterogeneous data collection, including a mix of simple text files, photos, videos, and other types of unstructured data. Businesses nowadays have a plethora of data at their disposal. Still, they don’t understand ways to extract value from it because the data is in its unprocessed form or unstructured version.


Semi-structured data is a hybrid of structured and unstructured data. It is unstructured data having metadata connected to it. This can be built into the data itself, such as time, device ID stamp, location, or email address, or it can be added subsequently as a semantic tag.

Applications of Big Data

Big data has an immense capacity for generating more accurate models in various industries. And these razor-sharp models are impacting and altering the corporate world. The following are a few examples of popular big data applications:

Mobile Advertising

Mobile advertising has proven to be a lucrative industry for businesses. Platforms use mobile devices’ sensors, such as GPS, to deliver real-time location-based adverts and offers depending on this cascade of data.

Personalised Medicine

Several technologies in research and personalised medicine make use of biomedical data. Science in this field allows for the creation of ways for analysing enormous amounts of data to generate remedies tailored to every individual and thus highly successful.


During transatlantic trips, airplanes create massive amounts of data in the range of 1,000 terabytes. Everything is sent into aviation analytics systems, which monitor fuel efficiency, passenger and cargo weights, and weather forecasting parameters to optimise security and resource efficiency.


Banks keep track of credit cards’ spending habits and related activities to spot unusual patterns and abnormalities that indicate a fraudulent transaction. In addition, banks can use big data analytics to track and analyse business activities, KPIs, and staff engagements.

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Romina Gopalan is a top-notch upskilling advisor & content writer. Her areas of expertise include Digital Marketing, Data Sciences, IoT, RPA, and UX/UI writing. Her sharp research and writing skills allow her to identify futuristic opportunities. Thus, helping you understand how you can leverage expertise in any domain.


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  3. I thank you for giving me access to this helpful knowledge.


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