Data Cleaning in Data Mining is a First Step in Understanding Your Data
Data mining is the process of pulling valuable insights from the data that can inform business decisions and strategy. But before data mining can even take place, it’s important to spend time cleaning data. Data cleaning is the process of preparing raw data for analysis by removing bad data, organizing the raw data, and filling in the null values. Ultimately, cleaning data prepares the data for the process of data mining when the most valuable information can be pulled from the data set.
The ability to understand and correct the quality of your data is imperative in getting to accurate final analysis. The data needs to be prepared in order to discover crucial patterns. Data mining is considered exploratory; data cleaning in data mining gives the user the ability to discover inaccurate or incomplete data–prior to the business analysis and insights. In most cases, data cleaning in data mining can be a laborious process and typically requires IT resources to help in the initial step of evaluating your data. Because data cleaning prior to data mining is so time-consuming, it creates a dilemma for data analysts: you don’t have enough staff or time to clean the data. But without proper data quality, your final analysis will suffer in accuracy or you could potentially arrive at the wrong conclusion.