Data mining and big data

2 May Do you agree with this ment? Yes, I do agree with this ment. Upon a deep analysis of the statement, “ The term Big Data grew explosively circa 2011. However, confusion reigns with many regarding Big Data is an over-hyped buzzword for Data Mining”, I agree with it.
Data mining entails the process of collecting and analyzing large data volumes or data sets in order to discover their respective relationships. On the other hand, the term, ‘ Big data’ describes a massive structured and unstructured data volume, which is so complex to process using the common or traditional database and technical software functionalities.
It is very essential to note that a deep scrutiny of real world commercial implementation of data, makes the International Business Machines (IBM) come out as one of organizations with a high quality ‘ Big data’ hub. At this company’s ‘ Big data’ hub, large volumes of information are handled, which are actually very hard to process in a traditional database. The data hub is composed is of data mining engines integrated to aid in easy handling of data.
The integration of data mining in IBM has made very easy and fast for the company to manage and process data in its globally placed (using cloud technology) immense data warehouses. Thus, this makes it clear that although the data is large, it is realistically the simplest and easily tolerable data volumes in data mining. In this sense, I hereby agree that the term ‘ Big data’ is actually an over-hyped buzzword for data mining.
Microsoft Incorporation is one of the most successful software companies globally. Due to the large data volumes handled at the company, the subject of ‘ Big data’ in the company has also been a subject of concern. At this company, issues related to ‘ Big data’ have usually been experienced in scenarios where the organization’s traditional database system is exhausted with the ever-increasing data volumes. This includes operating system files, cache files, customer data and management information system data. However, through the adoption of data mining engines, Microsoft Incorporation has smoothly been handling all the large amounts of data that it shares globally with clients and partners. Therefore, this case study further makes me agree with the statement.
Thirdly, Facebook Incorporation is a social network website that manages online communication for over a billion global users a month. These users share messages, photos, poking, placing status and storing personal data. In essence, the company handles very immense and complex data volumes, which cannot be manipulated in a simple traditional database. However, through the integration of data mining engines, the websites can easily and at a fast rate allow users to perform all their desired operations.
Lastly but certainly not the least, PayPal is one of the globally top companies in electronic of electronic commerce transactions. Considering the huge customer, transaction, and management information system data from all around the globe, it is clear that it handles terabytes of information in an hour. Having a database that handles terabytes of data is very challenging. All this faster and large data handling success is facilitated by the integration of data mining engines in its online system. This means, although the data is very large and complex, it is actually very simple for the data mining engines to handle. Thus, I hereby respond by saying, ‘ Yes’ to the statement that, ‘ Big data’ is an over-hyped ‘ Buzzword’ for data mining.