Definition of Big Data Analytics

Big Data Analytics is one of the next Big Thing in organizations. Big Data Analytics came into the scene in the beginning of the twenty-first century. The first organizations to embrace it were online and startup companies. Companies such as Google, eBay, LinkedIn, and Facebook relied on Big Data Analytics from the beginning. Google succeeded in the business of helping persons in searching through millions of websites and zettabytes of data in order to provide near-instantaneous results with pinpoint accuracy (Cutroni 2010). Various Big Data Analytics methods and solutions help in obtaining this result. In the past decade, a variety

The objectives of Big Data Analytics

Fig. 4.5 The objectives of Big Data Analytics

of industries in the finance, manufacturing, retail, and technology sectors have been using Big Data Analytics to improve their processes or to better understand and deliver services to their customers.

Big Data Analytics generates value from the storage and processing of very large quantities of digital information. Traditional computing techniques are not efficient in this case. Big Data Analytics is similar to “small data” but relatively bigger in volume. Having more data requires different approaches:

  • • Techniques, solutions, and architecture
  • • Solutions for new problems or for old problems in a better way The reasons for the interest in Big Data Analytics are as follows:
  • • The growth in the quantity of processable data
  • • The increase in data storage capacities
  • • The increase in data processing power
  • • The availability of data (different data types)

The use of Big Data Analytics makes sense for the large amount of processable data more and more available:

  • • Wal-Mart handles more than 1 million customer transactions every hour.
  • • Facebook handles 40 billion photos stored by its user base from its user base.
  • • Decoding a person’s genome originally took 10 years to process; now it can be achieved in less than one week.

Every day, over 2.5 quintillion bytes of data is generated globally. Around 90% of the existing processable data in the world today has been created in the last two years alone (Zhang et al. 2012). A definition of processable is: “Able to be processed; suitable for processing” (by other computer applications) (Hey et al. 2009).

Big Data Analytics are normally

  • (1) automatically generated by a machine (for instance, sensors embedded in a vehicle);
  • (2) typically extracted from an entirely new source of data (for instance, use of the IoT);
  • (3) data not designed to be computer-friendly (for instance, text streams); and
  • (4) Focused on important data.

On the other side, if you cannot handle the data it does not make sense to store them.

Big Data Analytics are the results of processes such as:

  • • Transactions
  • • Data from sensors
  • • Social networks, etc.

The data to use are as follows:

  • • The data produced by the same company
  • • The data produced by users, customers, and vendors

• The open data such as social media, on the markets (the 3Cs: customers, competitors, and compliance).

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