Big Data Analysis characteristics Task

 Characteristics of Big Data (Including Visualizations) activity




Big data analysis involves the interpretation and examination of large and complex datasets to extract valuable insights and make well informed decisions and there are some key characteristics of big data analysis and i will even show some visualizations. 

The first characteristic i will go over is Volume and big data involves large volumes of data that cannot be effectively managed and processed using standard traditional tools and databases. Velocity is another key characteristic of big data as it refers to the speed at which data is being generated, processed and updated and is often necessary to jeep up with the fast paced nature of the data generation. Variety is another big characteristic when it comes to big data analysis and this is because big data comes in various formats including structured, unstructured and semi-structured data and so because of this analysis tools must be able to handle the diverse data types. Veracity refers to the quality and reliability of the data itself and because big data analysis involves dealing with data of uncertainty this requires careful validation to ensure reliable results. Value itself is the ultimate goal of big data analysis as people want to extract value and actionable insights from the data and not meaningless invaluable data. Visualizations play a important and crucial role in big data analysis by representing complex information in an easy to read visual format, they come in all kinds such as charts, graphs and maps. Using visuals itself is a better way to convey certain data as it is easier to notice patterns from it than just normal text. Scale or Scalability means big data systems must scale horizontally to handle the increasing volume and complexity of data. Collaboration is an obvious and needed part of big data analysis as it more often than not involves a team of people analyzing the data and extracting valuable insights from it. Finally Machine Learning is used to identify any trends or patterns within the data itself and overtime the machines can learn to recognize these and make better predictions based on the data.



Credits: "altexsoft" for the visualisation.






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