The big data originally is normal data, but it has a set of characteristics that distinguish it from other traditional data, and the most common of these characteristics are known as (3 V) represented by (Volume), (Variety), (Velocity), and with further studies, these characteristics were expanded to (7V) and are as follows [5]:

  • 1. Volume: The volume of data extracted from a certain source, which determines the value and size of the data to be classified as big data, and by 2020, cyberspace will contain nearly 40,000 megabits of data ready for analysis and debriefing.
  • 2. Variety: Data extracted, which helps users, whether they are researchers or analysts, to choose the appropriate data for their field of research, and includes structured and unstructured data such as pictures, audio, and video clips, SMS, call logs, and map data, and requires time and effort to be configured in a suitable format for processing and analysis.
  • 3. Velocity: It is the speed of production and extraction of data to cover demand, where speed is a critical element in deciding upon such data, which is the time from the moment this data arrives at the moment the decision is made based on it.
  • 4. Reliability (Veracity): It means the reliability of the source of data, and the accuracy, validity, and modernity of those data where we as the Executive Director of all three managers do not trust the data that is subject to the decision. There are also studies that estimate the impact of poor data on the US economy is estimated at 3.0 trillion dollars annually.
  • 5. Value: To take advantage of the big data, we need specialists who have the necessaiy expertise and skills to deal with this data and analyze it appropriately, in which case the information is considered valuable.
  • 6. Variable Value (Variability): Meaning that the same information or the same data can be customized, depending on the context in which they are presented; their true value can be determined and analyzed appropriately.
  • 7. Visualization: When using big data, it must be analyzed and shown with different shapes to suit the nature of their' use, and it takes multiple shapes such as statistics, figures, geometric shapes, and others.
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