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What is Big Data Analytics and How it Works?

Though the term ‘Big Data’ has been under the prominence lately, yet many of us aren’t aware of it. Whether it is government institutions, health care providers and business or academic and financial institutions, Big Data is playing a major role in almost every industry. The world has witnessed a huge difference between processes with and without Big Data in terms of enhanced business possibilities and better customer experience.

The fact that Big Data has become the supreme reason for the survival of every small and large organization, gives birth to a very important question – What is Big Data?

In simple words, “Huge and complex sets of data are called Big Data that require further analysis and processing to obtain valuable information which ultimately profits organizations and enterprises.”

Now, let’s move to the main question...

What is Big Data Analytics?

Big Data Analytics deals with examining the massive amount of data to discover the unrevealed correlations, patterns and various other insights. This analysis is essential for organizations to plan their growth and development. The process requires the application of data mining algorithms on a given data segment, which adds to the decision making of that organization.

How does Big Data Analytics work?

The concept of Big Data architecture is so vast that it can’t be limited to a single technology. Actually, there are multiple technologies working simultaneously to pull out the maximum out of the data. Yet, there is scope to add advanced analytics to it. Here are some major participants:

  1. Data Administration: Rich quality and well-organized data is the top requirement before igniting the analysis. While the data is ceaselessly passing in and out of the organization, repeatable processes are designed to set and maintain merits for ensuring data quality.
  2. Machine Learning: As an important module of Artificial Intelligence, Machine Learning instructs machines to perform human tasks. It helps in developing systems that can deliver quick and more reliable results by analyzing data that is bigger and more complex.
  3. Data Mining: With Data Mining, you can handle all the unorganized data by identifying the hidden patterns and use them further to answer more complex queries. There are software for Data Mining which spot the relevant stuff out of the noisy data.
  4. Predictive Analytics: Predictive analytics enables you to predict future results based on past data. This technique makes use of data along with machine learning and statistical algorithms. By adopting Predictive Analytics, you can make meticulous business decisions.
  5. Hadoop: It is free to use open source software framework, known for its capability to preserve an amazingly huge amount of data. Processing of data in Hadoop is super-fast as it works on a distributed computing model. It uses collections of commodity hardware to run applications.
  6. In-memory Analytics: In-memory Analytics increases business intelligence detailing by fetching data from a server’s RAM. This is achieved by enhancing the performance and speed when querying the data.

Big Data has gradually become the dominant that has rewarded the underdog enterprises as the best and biggest companies today. Future is clearly in the hands of those who have the potential to convert the data into information and information into insights. The key to this is Big Data Analytics.

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