Get Started With Data Mining Processes

Get Started With Data Mining Processes Since many industries (IT, manufacturing, chemical, product engineering firms etc.) have recognized the importance of collecting information, they have started to outsource data mining services to streamline their operations. As a revolutionizing technology, data mining holds the capability of identifying large data patterns and analyzing current trends through implementing latest technologies like artificial intelligence, machine learning etc.

The idea of mining data from databases or data warehouse is not new. In fact, the technological innovations have refined it extensively. Getting straightforward statistics out of huge records of databases is not easy, therefore database engineers are in quest of finding efficient ways of extracting data and acquiring knowledge from it. Read on to understand the data mining process in detail: Identifying Business requirements: The process of mining data begins with outlining project requirements from the business perspective. Teams, comprised of data mining experts and business experts, work together to analyze the current situation by keeping some key aspects of resources, constraints, etc. in consideration. Once done, then company specifies data mining goals to accomplish defined objectives within a current situation and designs a comprehensive preliminary plan.

Data extraction and analysis: Here, the data is extracted from multiple available sources through reporting and visualization. Teams become familiarized with the data and analyze its properties clearly. It helps to gain valuables insights into the data and detect interesting subsets. In the last, data integration and quality check is performed on the acquired data by considering several questions like if there is anything missing in the acquired data or if it is complete, etc.

Data preparation: This phase involves all critical activities like attribute selection, cleaning, and formatting data sources to construct the final dataset. This final data is then fed into the modeling tool for further processing. Sometimes, this stage may include the task of exploring data at a greater depth to determine data patterns.

Modeling:

The process gets started with selecting multiple modeling techniques. Next, the model is put on testing for its validity and quality evaluation. Though several techniques can be implied on the same dataset, few techniques have specific data requirements. Modeling tools parameters are set to optimal values so as to construct a high-quality model.

Model Evaluation: In the evaluation phase, the prepared model is carefully assessed and all the executed step are reviewed to ensure that it is matching business objectives. If it fails, teams step back to modeling phase to reconstruct the model with different parameters. They may repeat constructing several models till they become certain that new model is sufficiently solving all critical business issues. Chances are, new business requirements could arise due to the new patterns discovered in the model.

Deployment: This is the final stage where the results of data mining are organized and presented in into spreadsheets or tables so that stakeholders can use it easily. Not as easier as said to be, deployment phase could be complex in terms of repeated data mining process. Here, the data deployment and maintenance plans are prepared for implementation. The final report highlights the project experiences and determines if any improvement is required.

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