The Role of Data Mining in Business

Data management and research is a field in the world of computing that numerous researchers and organizations invested in to adapt to the modern trends of technological advances such as smart cities, decision making, and others. The amount of data stored in databases has significantly increased over the past two decades as well as the number of database applications in the scientific and business domains. The increase is associated with the success of the relational models for data storage, the emergence of new data manipulation and retrieval technologies, and development of smart applications that are in intelligent in data organization in terms or relevance (Anand and Hughes). Although business owners and developers did not consider the value of creating software for data management, the increasing needs of technology made them realize the valuable entities hidden within the data that is not yet exploited that data mining has become the best alternative to gain this hidden information (Knowledge Discovery in Data). Data mining, in this case, is defined as the extraction of knowledge from large data sets. Also, it is defined as the process which enables companies to turn raw data into vital information. The process has changed the perspective and design plan for databases and database management systems. As a result, more tools are developed including knowledge management system.

The data mining process involves an effective collection of data and warehousing and processing by computers. Specialized and sophisticated mathematical algorithms are used in the technology of data mining for data segmentation and analysis of future behaviours and events of data stored in the databases. Data mining is characterized by the features such as making predictions based on the possible outcomes, focused on databases analysis and large data sets, automatically predicts the pattern according to the trend, and presents visually documented grouping of facts. The process can be successful by creating a complex and large number of queries, and the database size should be higher for processing and maintenance.

Importance of data mining

Data mining is a significant technology that is currently used in numerous areas to achieve aims. For instance, the technology has enabled most businesses to collect and gather much information about the customers and develop effective strategies towards meeting their demands thus it is an insightful tool for organizational productivity and performance improvement. Through the extraction of various information and knowledge from the archives, businesses make critical and informed decisions which are data-driven (Kantardzic, 88). In today’s frenetic situations, every company has its technique towards achieving sustainable competitive advantage hence a business which is data driven is more likely to succeed than a business that makes decisions based on the top management communications without analysis. The decisions support systems use the data mining technology to enable such successful business to thrive in the competitive environment and satisfy their customer’s needs.

Moreover, data mining is used for other several functions including; firstly, information collection where companies used the web scraping process to collect information about investments, investors, and funds from related databases and websites. Secondly, pre-processing of data where any information that has been determined unimportant can be deleted from the database to leave only filtered data by the data mining experts. Thirdly, online research, since the internet contains vast information on different subjects, topics, and new explorations, researchers gather vast information thus able to detect similarities, patterns, and frauds. Additionally, data mining’s current capabilities extract information from different sources such as the social media platforms to inform companies, and news broadcasters of events are happening across the globe such as traffic jam and accidents hence helping in transportation challenges as well as enabling companies to publish its product information to the relevant audience.

Data mining implementations and software

Data mining is implemented in various techniques for various systems and software. Firstly, the technology is implemented in the meteorological investigations to find hidden patterns of massive meteorological data to enable retrieval of crucial information to be converted into knowledge.  The rapid changes of climate of the past decades are recorded by various devices including satellite maps, radar information, and proxy data contributes to the accumulation of huge data which requires special tools for analysis and knowledge extraction. This will ensure, the weather observes, and climate change monitoring team can act upon the knowledge extracted to mitigate the continuing increase in of climate change which is affecting the world through catastrophic consequences such as global warming. This involves the climate variability predictions which should be monitored due to its effect on crucial sectors such as water resources and agriculture (Kohail and Alaa, 3). Big data is another vital sectors of data mining implementation that have received focus in the current research. The technology is used in most sectors such as transport, healthcare research in electronic health records and financial analysis to advance the sectors accordingly.

The number of software applications using data mining has significantly due to various algorithm implementations. Database management systems, customer relationship management system, decision support management system, and other open source software applications utilize the technology to collect required data to launch specific actions. Some of the open source software using the technology involve WEKA which is used for analysing agriculture-related data. The software uses the techniques of predictive modelling and visualization techniques of data mining. R Programming is also another software programming language used by various data miners (Goopta).

Data mining in industry

This technology is used in the industry in very many ways. For instance, the R programming implementation uses it for statistical data analysis and development of statistical software. Various techniques of data mining exist which it makes being used in the industry to solve specific challenges. The techniques are divided into supervised and unsupervised learning (Chitra and Subashini, 220). Supervised learning is also called directed learning. This category tries to explain the behaviours of the target data using predictors or independent attributes thus it results in the generation of predictive models. Data mining, in this case, happens using the software while the targeted value is already known. Supervised data mining uses algorithms such as decision tree for classification, generalized linear models and support vector machine for classification and regression, minimum description length to detect attribute importance, naïve Bayes for classification.

On the other hand, unsupervised learning works for descriptive purposes and making predictions. The algorithms which operate with this technique of data mining include Apriori which used for evaluation of associations between different datasets. Secondly, k-Means for clustering which operates by partitioning data into a specific number of clusters and determine the probabilities of a particular element belonging to a given cluster. Thirdly, one support vector machine used for anomaly detection by evaluating the rare cases of data relationships (Chitra and Subashini, 221). Finally, Non-negative matrix factorization for feature extraction by generating new elements through linear combinations of original attributes Data mining also is used in the industry by sequential pattern technique for trend analysis on regular occurrences (Brown).

Works cited

Anand, S., and J. Hughes. "Data mining: looking beyond the tip of the iceberg." Faculty of         Informatics, University of Ulster, Ulster http://inchinn. infj. ulst. ac. uk/htdocs/white.          html (2000).

Brown, M. “Data Mining techniques”, 2012. IBM developer works. Available at             https://www.ibm.com/developerworks/library/ba-data-mining-techniques/index.html

Chitra, K., and B. Subashini. "Data mining techniques and its applications in banking             sector." International Journal of Emerging Technology and Advanced Engineering 3.8          (2013): 219-226.

Goopta, C. “Six of the Best Open Source Data Mining Tools”, 2014. The Newstack. Available at     https://thenewstack.io/six-of-the-best-open-source-data-mining-tools/

Kantardzic, Mehmed. Data mining: concepts, models, methods, and algorithms. John Wiley &          Sons, 2011.

Kohail, Sarah N., and Alaa M. El-Halees. "Implementation of data mining techniques for       meteorological data analysis." Intl. Journal of Information and Communication             Technology Research (JICT) 1.3 (2011).