Big Data Analytics In Business Organizations (Research Paper Sample)
Discuss the adoption and use of Big Data Analytics in business organizations. Be sure to include the problems and benefits, while providing at least one successful example of implementation.
- Introduction (Statement of Work) .5 pages
- Literature Review/Definition of topic (tech) 1.5 pages
- Organization use & Implementation 1.0 pages
- Special security considerations 1.0 pages
- Costs & Disadvantages/Benefits (Estimated/projected) 1.0 pages
- Conclusion & Recommendation 1.0 pages
- References (written, web, interviews)
Big Data Analytics in Business Organizations
Big Data Analytics in Business Organizations
Today's business organizations are increasingly realizing the significance of utilizing more data in an attempt to gain insight into the prevailing market dynamics. While it is currently ubiquitous, the origin concept of Big Data is largely uncertain. Diebold (2012, cited in Gandomi & Haider, 2015) posits that the concept might have originated in a conversation held at Silicon Graphics Inc. during the 1990s. O'Reilly Media's Roger Magoulas introduced it into the world of computing in 2005 in an effort to define huge quantities of data that pose a challenge to the conventional data management techniques due to its size and complexity (Ularu, Puican, Apostu & Velicanu, 2012). The concept of Big Data gained prominence over the past decade as many companies, including Twitter, Facebook, eBay, Yahoo and Google raised concerns over the concept (Alsghaier, Akour, Shehabat & Aldiabat, 2017). Big Data is richer and higher data that provides insight into the events, activities and behaviors occurring globally. In light of this, Big Data analytics provide business organizations with access to different types and varieties of data form a huge number of sources within a short duration of time. This paper examines the implementation and utilization of Big Data analytics in business enterprises.
Opportunities relating to data analysis in companies have generated renewed interests in the field of business intelligence. This field focuses on the technologies and techniques used to generate better insight into the market dynamics and help in making timely business decisions (Alsghaier, Akour, Shehabat & Aldiabat, 2017). Proper adoption and use of Big Data analytics is critical in solving problems such as health care issues and crimes. Companies are increasingly using Big Data analytics to perform business experimentation that to guide decision makers and to assess customer experience, business models and outputs (Alsghaier, Akour, Shehabat & Aldiabat, 2017). The insight gained from Big Data analytics forms the basis for revolutionary transformation in business marketing, invention and business research. Companies such as eBay, Google and Amazon have been using Big Data analytics to control performance, increase user interactivity and raise their sales revenue.
Today's lives are impacted by the companies' ability to manage, interrogate and dispose data. Companies often adapt the relevant technology infrastructure to generate critical data that guides the improvement of the services offered (Ularu, Puican, Apostu & Velicanu, 2012). Specifically, the Internet has created a critical information-gathering platform through online services and the social networking sites. Hadoop is one of the information technologies that help business organizations to analyze huge quantities of data – Big Data. Alsghaier, Akour, Shehabat and Aldiabat (2017) assert that Hadoop is an open source piece of software that allows for the processing of huge data sets within a distributed computing environment. This software is capable of gathering and analyzing both unstructured and structured data.
The large size of Big Data cannot be measured in terms of gigabytes but rather in terms of yottabytes, exabytes, etabytes and zettabytes (Ularu, Puican, Apostu & Velicanu, 2012). Business enterprises in the data-driven industries, including telecommunications and financial services have adopted Big Data analytics more rapidly than those in other industries (Ularu, Puican, Apostu & Velicanu, 2012). Firms in the data-driven industry experience a...
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