Research MIS2502
Big Data and MIS2502
Data is a vital ingredient for all companies that seek to register success and growth in their businesses. Big data is a phenomenon where data sets are very huge in volume and complex in their nature such that the conventional data processing software applications might not be able to handle. Some of the challenges that big data presents is in its data analysis and subsequent storage of the data as well as sharing and transfer of the same. Other challenges include visualization, information privacy due to the volume of the data as well as the source. The concept of big data is a continually evolving concept and remains the driving force for many current waves of transformation in the digital world.
Big data is very unstructured and semi-structured in its form with a potential of mining information from it. In particular, big data is a process that comes along when the conventional techniques for data mining and handling techniques are not able to uncover the insights and the subsequent meaning of the underlying data. As a result, the big data phenomenon seeks to deliver a more competitive strategy through a much-unified structure and be able to solve some of the toughest challenges using data. Big data, as part of data analytics, is also a key area for researchers to find out more about emerging types and content of data as well as help make wiser business decisions (IBM).
MIS2502 in data analytics relates directly to the big data phenomenon since the entire course seeks to demonstrate the process of gathering huge data using some of the most efficient and effective techniques in use by big businesses. The MIS2502 course material assists us to analyze this huge data, which in turn creates the best opportunities for them such as finding a niche in this particular area of data analytics. Skills from the MIS2502 course material helps us to analyze the data and come up with meaningful information since the data from the big data phenomenon is much unstructured, discrete, and contains raw facts.
A perfect example of the application of big data in practice is by how big businesses with huge retailing power acquire data from their customers who do shop from them. Such businesses then analyze the huge amounts of data, which help them acquire meaningful information and perspective to broaden their businesses further. Some big businesses such as Walmart that register a huge number of customers on their websites can collect huge data of their customers. Consequently, such data enable the company to gain a better understanding of their customers. The information that such big businesses acquire from their customers creates a wholesome view of the customer such as their preference and purchasing power. Moreover, these businesses are then able to gain a full understanding of their customers more than ever before.
The information even helps them to understand why customers, for instance, purchase product X over product Y, or prefer products from a certain manufacturer to the rest of the manufacturers in the same line of business. Additionally, businesses can even deduce the shopping behavior and the pattern of a specific or a group of customers from such information and come up with predictions that the customer might want or need to buy next form their sites. As a result, purchasing online today has become much easier because companies can learn about the behavior and shopping patterns of customers, and hence be able to predict their shopping preference much easily. Today, the big online businesses can necessarily learn a lot about a specific customer just from one click, due to huge amounts of data hat one leaves behind after shopping for their item (Marr).
Works Cited
IBM. “Big Data Analytics | IBM Analytics”. Ibm.Com, 2018, https://www.ibm.com/analytics/hadoop/big-data-analytics. Accessed 2 Apr 2018.
Marr, Bernard. “4 Ways Big Data Will Change Every Business”. Forbes.Com, 2015, https://www.forbes.com/sites/bernardmarr/2015/09/08/4-ways-big-data-will-change-every-business/#135c9e982729. Accessed 2 Apr 2018.