Big Data Analytics

As the name suggests, Big Data refers to a large amount of information gathered over a period of time for a particular purpose. One of the most common example would be user information. Further segregation based on demographics for multiple users over a period of time could be termed as Big Data. With the amount of data fed to the world wide web everyday, the potential insights that can be gathered from this information are insurmountable.

    Real time benefits to businesses

    With the evolution of the internet and the various new-age technologies, big data and its application is witnessing increased demand. Businesses over-time accumulate a set of information, which upon analysis may reveal specifications about consumer trends and its correlation. Analytics makes it possible to store this set of information and process it to further analyse the depths of the business. It also helps to monitor the current state of the business and predict a forecast based on a set of observations.

    The strength of big data lies not in the quantity of information gathered across sources, but in how efficiently businesses use this information in analysing current processes and implementing for the future. According to Gartner, more than 75% of companies are planning to invest in this. Thus, the idea is not only to analyse trends but also to analyze it rapidly to maintain competency. With the wide range of high-powered techniques available, the art lies in the extraction of the desired information. Depending on the scalability of the stored information, the advantages could be tailor-made too.

      We at NewGenApps

      To address the rising demand of Big Data analytics that businesses can leverage in their daily operations, our team at NewGenApps ensures we stay a step ahead always by combining in-house expertize while partnering with technological leaders including Microsoft and Google. Few of our trending developments lay emphasis on: Self-learning dispatch system combining machine learning and big data technologies
      Customised capacity planning predictive model Making technical frameworks robust enough to handle large and varied types of data sets

      On the Tech Stack: Hadoop, Spark, Tableau, Microsoft Power BI, QlikView

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