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Data Extraction vs Data Mining - Pros and Cons

Data manipulation processes are commonly used by a wide range of industries. These tools provide users the ability to collect data to process it. In particular, data extraction and data mining are utilized. Many people confuse one process for the other. However, each procedure works differently. In this article, you will learn the distinction between these processes.

Data Mining

As a computer science and statistical tool, data mining allows a user to perform an aspect of data processing. It is used for the analysis and evaluation of data points with the help of statistical and mathematical tools. In addition, the primary function of such statistical and mathematical tools used in data mining lets you find relations and patterns to make conclusions about the collected data. Nowadays, most data mining tools can handle significant data points at any given time. The power of this process makes data mining a go-to approach for businesses and other data-driven organizations. To successfully process data mining, you will have to use data extraction in some way. Yet, the actual methods used for data mining are complex statistical and mathematical procedures that require high-powered digital computers. The use of such computers allows users to do more than understand data. You get to process data with the aim of making predictions. However, these predictions can only be made sense of when used with statistical tools like probability. Using probability theory lets you categorize predictions based on the likelihood of each occurring. Ultimately, it lets you make better organizational decisions. In most cases, data mining is an automated process. The process does not require human input once it is running. Therefore, data mining outcomes are objective primarily due to their accuracy. Yet, the accuracy and the nature of the data sets depend on the programmer’s skill and technique.

Data Extraction

Data extraction is basically a form of the data collection process. It allows a user to collect data from a wide range of sources in various formats. In addition, the method collects this data with the aim of storing it within a central location using a single form for future processing. However, the process of data extraction stops at the storage point. You will require data processing tools to move beyond this point. Nevertheless, data extraction involves data restructuring before or after storage. You mostly want to use it when you have unstructured data. As a result, data extraction can collect a lot of information from files like emails, PDFs, web pages, text files, and more. It works on the internet to collect data from target locations. In addition, data extraction strategies are capable of storing extracted data onto cloud-based, physical storage, or all. Before deploying a data extraction tool, you will need to define the parameters of extraction. These requirements tell the tool the scope of data they need to access. In some cases, you will want to include instruction that excludes some type of data points to increase the accuracy of the process.

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