Data mining is the process of examining vast quantities of data in order to make a statistically likely prediction. Data mining could be used, for instance, to identify when high spending customers interact with your business, to determine which promotions succeed, or explore the impact of the weather on your business.
Data mining principles have been around for many years in conjunction with data warehouses, and have now taken on greater prevalence with the advent of Big Data.
Data analytics and the growth in both structured and unstructured data has also prompted data mining techniques to change, since companies are now dealing with larger data sets with more varied content. Additionally, artificial intelligence and machine learning are automating the process of data mining.
Regardless of the technique, data mining typically evolves over three steps:
- Exploration: First you must prepare the data, paring down what you need and don’t need, eliminating duplicates or useless data, and narrowing your data collection to just what you can use.
- Modeling: Build your statistical models with the goal of evaluating which will give the best and most accurate predictions. This can be time-consuming as you apply different models to the same data set over and over again (which can be processor-intensive) and then compare the results.
- Deployment: In this final stage you test your model, against both old data and new data, to generate predictions or estimates of the expected outcome.
This article originally appeared on Datamation.com. To read the full article, click here.
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