There are different ways in which organizations can use data to their advantage. Organizations collect vast amounts of data from various sources as they can use the data to carry out the helpful analysis. Data mining is a process where structured data is analyzed to identify specific predefined patterns in the information, which can help derive practical business conclusions. On the other hand, data analytics is the process of analyzing the data patterns identified in the data mining approach to help management make proper decisions. Also, data mining is generally performed on databases. Hence, it is also known as knowledge discovery databases, whereas data analytics can create business intelligence models and training. Data mining includes various techniques such as classification, clustering, etc., to identify patterns, and data analytics can be used to carry out predictive, prescriptive, and descriptive analysis (Tummala & Kalluri, 2018).
Data analytics can train the systems to handle different data sets and is primarily used in AI systems. Also, it does not include data preparation steps which are often part of the data mining process. In data mining, the raw data is collected, and the information is processed and cleaned to obtain a structured data set that is then used to identify patterns. Therefore, data mining usually is dependent on statistical and mathematical models, whereas data analytics is based on scientific models. Also, data mining provides valuable information from the data, whereas data analytics provide valuable insights as they analyze the data to derive practical business conclusions (Khanra et al., 2020). Data analysis also consists of converting the data into visual graphs to be studied to obtain valuable findings. Therefore, data visualization is a part of data analytics, and it is not a part of data mining.
In the future, I can use data mining and data analytics techniques to obtain practical business conclusions that can help analyze the performance of my team and the organization as a whole.
Data analytics and Data Mining are vital steps in any data-driven project that are supposed to be perfectly done to ensure success. Data mining is an intentional and successive cycle of identifying, finding and differentiating and finding valuable data in a massive dataset. The improvement in computing expertise has made data mining mainstream and streamlined. Data Analysis is the process of removing, cleaning, changing, demonstrating the data to reveal significant and valuable insights to choose the company in question. (Calvet Liñán & Juan Pérez, 2015) Although data analytics and data mining are different words in the data field, sometimes they are used instead of the other. To understand the contrast between these two terms, we look at significant insights as discussed below.
Data mining is one of the steps done during the data analytics process, while data Analytics is the umbrella that generally deals with each step in data-driven models. Data mining performs better when the data in question has well-structured data. Meanwhile, data analysis can be carried out on any data.
Data analysis is used to hypothesize and culminate itself in providing essential data that helps in business decisions. In contrast, Data mining is required to accomplish significant tasks and make the data being used more usable. (Calvet Liñán & Juan Pérez, 2015) Data mining has no bias or notions which are instilled before handling the data. At the same time, data analysis is used for testing hypotheses.
Data mining uses mathematical and scientific methods and models to identify trends or patterns in the mined data. At the same time, data analysis is employed to derive analytical models and tackle business analytics problems. Data mining usually often does not use visualizations, graphs, bar charts, etc., whereas these visualizations are needed in data analysis. Analyzing data requires a good representation of the data in question. Both data analysis and data mining are crucial to analyzing bid data. People tend to interchange the two words in business.
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