ITM535 MOD4 Case On Data Mining and Analytical Technologies

Data Mining and Analytical Technologies
Student’s Name
Institutional Affiliation
Data Mining and Analytical Technologies
Introduction
Data mining refers to a set of technique and technologies that employ intelligent software to explore large computing file cabinets or database automatically or semi-automatically to establish repeating patterns and identify rules and trends that describe the behavior of a given set of data. Typically the process of data mining aims to understand and interpret the content of data repository. The process employs' statistical practices that are integrated with search algorithm and artificial intelligence neutral works. Data mining has developed to become essential as technologies also advances since institutions desire and demand to examine more data continues to increase. Today, organizations can gather raw and new data trends and patterns that require accurate insights and analysis to establish their connection with the existing forms of data and information. Many businesses are relying on data mining for planning and running if their operations to actualize their objectives, vision, and mission. The pressure from the digitization of many business operations and improvement in information technology has subjected business to a high level of uncertainties and risk management, hence creating unhealthy competition that overwhelms organizations, especially in stiff industries. Thus, to control data miners from overwhelming the organization, an adequate sequence in the data mining process need to be put in place.
Understanding Data Mining
The process of data mining is not new and considered relatively simple with the advancement in technologies today. Data mining is developed by writing an algorithm that will pull and analyze information that would have been difficult to notice. However, the information has to be kept in a database form and hidden as well CITATION SSu06 \l 2057 (Sumathi & Sivanandam, 2006). This will protect databases from being available to any regular search since it is not created with the parameters that allow regular searches. Additionally, data mining process primarily focuses on creating effective, reliable and efficient approaches to extract vital information from voluminous databases (Sumathi & Sivanandam, 2006). The current data mining methods exist in three main categories, namely; computational, statistical, and visual approaches. The computational method employs an algorithm technique to search through big amount of data automatic and in a systematic pattern such as homogeneous regions, clusters, colocation patterns, outliers' pattern and association rules patterns. The statistical method comprises of connection test, multivariate lattice models, scan statistic and geographically weighted regression. Lastly, the visualization-based method has also numerous approaches such as spatiotemporal visualization, interactive geo-visualization techniques, and multivariate mapping, which are primarily used for multivariate analysis (Sumathi & Sivanandam, 2006). However, both statistical and computational approaches have no capability to interpret patterns but have a quick ability to search for a particular type of pattern. On the other hand, visualization methods help organizations to interpret complex patterns, explains the data behavior and propose intelligent guess for further studies. Therefore, a combination of all these approaches has successfully and more effectively managed to unlock complex problems.
Data Mining Overwhelming Problem
Data miners often work at a normal human speed, but their data is processed at a computer speed when they feed their queries into the software. After that, the mined data is usually stored in their previous forms which can be mined again to link with other related related data or parameters in future. Since data mining is a continuous process, it leads to massive accumulation of files that are often compressed in the archives (Parkinson, 2005). The extensive collection of data prompts data miners to warehouse their data and to develop separate databases for mining purpose in future. However, the problem emanates when data mines warehouse huge volumes of data that creates an overwhelming bulk that turns out to be prohibitive on essential and robust processing (Parkinson, 2005). Additionally, the bulkiness slows down the mining process even when sophisticated systems are used, making the entire process lest cost effective and time-consuming for organizations.
How to Reduce Data Miners from Overwhelming the Organization
One of the solution to prevent mined data from underwhelming the effectiveness of an organization operations is to set a mining pattern and guidelines. Through this, organizations can verify their data through intuitional knowledge to ensure the data to export are mined into smaller and manageable databases. This approach will boost the data mining software perf...