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Big Data Analytics

Big Data Analytics Introduction:

  • Big data is a variety of data, which is difficult to process and analyze.
  • Big data analytics is a process of examining large data, which consists of variety of data types.

The traditional data management systems and other existing tools are face difficulties in analyzing and processing huge amount of data.  Hence, various tools have been developed to analyze and process such a big data, which are known as ‘Big data analytics’.

Benefits of Big Data Analytics:

The big data analytics tools were developed to offer following two main benefits:

1. Cost reduction:
Big data technology like, Hadoop is cost efficient for storing large amount of data.  Hadoop is JAVA based technology, which is compatible with other platforms.

2. Fast decision making:
Ability to analyze a new source of data which makes it easy for organizations  analyze information quickly aiding faster decision making.

 

The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that it’s difficult to process using traditional database and software methods.

How Big Data Analytics is Used Today

As the technology that helps an organization to break down data silos and analyze data improves, business can be transformed in all sorts of ways. Today’s advances in analyzing big data allow researchers to decode human DNA in minutes, predict where terrorists plan to attack, determine which gene is mostly likely to be responsible for certain diseases and, of course, which ads you are most likely to respond to on Facebook.

The Benefits of Big Data Analytics

Enterprises are increasingly looking to find actionable insights into their data. Many big data projects originate from the need to answer specific business questions. With the right big data analytics platforms in place, an enterprise can boost sales, increase efficiency, and improve operations, customer service and risk management.

Webopedia parent company, QuinStreet, surveyed 540 enterprise decision-makers involved in big data purchases to learn which business areas companies plan to use Big Data analytics to improve operations. About half of all respondents said they were applying big data analytics to improve customer retention, help with product development and gain a competitive advantage.

Notably, the business area getting the most attention relates to increasing efficiency and optimizing operations. Specifically, 62 percent of respondents said that they use big data analytics to improve speed and reduce complexity.

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Big Data

What is a big data analyst? According to Gartner, the definition of Big Data reads, “Big data is high-volume and high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.” Big Data analytics find insights that help organizations make better business decisions.

A buzzword that is used to describe immense volumes of data, both unstructured and structured, Big Data inundates organizations of all sizes on a day-to-day basis. In other words, Big Data refers to humongous volumes of data that cannot be effectively processed with traditional applications. The processing of Big Data begins with the raw data that isn’t aggregated or organized—and is most often impossible to store in the memory of a single computer.

Education for Big Data Roles

For those seeking Big Data roles, you’ll need these:

    • Analytical skills: The ability to be able to make sense of the enormous amounts of data that you get. With analytical problem-solving abilities, you will be able to determine which data is relevant to your solution.
    • Creativity: You should have the ability to create new methods to gather, interpret, and analyze a data strategy.
    • Mathematics and statistical skills: Good, old-fashioned “number crunching” is absolutely necessary.
    • Computer science: Computers are the workhorses behind every data strategy. Programmers will have a constant need to come up with algorithms to process data into insights.
    • Business skills: Big Data professionals should have an understanding of the business objectives that are in place, along with the underlying processes that drive the growth of the business as well as its profit.

 

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