Big Data vs. Data Analytics

    Data is characterized by its universal existence. The quantity of digital data available continues to increase at a very fast and continuous rate, that is, the amount of data doubles up every two years. The implication of this is change in the way of living.

      According to a Forbes article, the rate at which data is increasing is higher than the past. It further argues that by the year 2020, there will be approximately 1.7 megabytes of data, which translates to one second per person in the globe, amount of information. This means that any data field is of utmost importance as it maps out the future.

      In order to understand data accurately and apply it as required, we will give a distinction between the data components, their types, modes of usage, requirements to becoming an expert as well as the expected remuneration. These divisions in the field of data include;

   

   Big data:  This refers to large quantities of data that can only be handled efficiently using the latest applications. The source of the Bid data is usually raw data which, because of its form and nature, cannot be stored in the memory of a computer. This kind of raw data is characterized by large inflow of big data on a daily basis that is either shaped or shapeless. Bid data is useful in improving decision analysis and making and in efficient business transfers.

   ·      Data Analytics: This involved processing of raw data in order to come up with useful conclusions or information from it. This is done by use of an algorithm or a power-driven process to derive the required visions. For instance, a number of data groups can be incorporated and synthesized to come up with correlations between them. Data analytics is used by organizations and corporations to improve their accuracy in decision making as well as analyze the suitability of their existing philosophies and replicas.

·                       Data Science: This involves how data is treated, from emptying to grounding and then analysis. This data is usually in two forms; organized or shapeless data. The elements that define data science include; arithmetic, problem solving, figures, strategic collection of data, software design a well as the skill to analyze and manipulate data in various ways Data science is the top most method used in extracting useful information from raw data.

     To sum, the applications of the different fields of data have had and continue to play a significant role in the business world. This is particularly evident in identification of new opportunities, given the dynamism and competition that exists in the business world. Other significant uses of these fields of data include; faster, accurate and better decision making, development of new products and services and most importantly cost reduction. The incorporation and correlation between these fields give a strong business management and development tool in the modern world.

The Source:

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