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Data Cleaning in Data Mining: A Critical Step in Evaluating Data

Technology   |   Paul Warburg   |   Jan 10, 2022

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.

Data Cleaning in Data Mining with Designer Cloud

Designer Cloud solves this cleaning and mining dilemma. Designer Cloud is a unique software product that provides a solution for data cleaning in data mining. By reviewing a visual profile of the data, a technical or business user can easily identify inaccuracies and discrepancies without having to rely on sophisticated data science techniques. Data anomalies are immediately displayed in a visual way to the user. Designer Cloud fixes invalid or inaccurate data in an intuitive and interactive way. Designer Cloud’s user-friendly interface allows business users and data analysts—who may not be technically advanced—to execute data cleaning in data mining themselves. Using Alteryx’s data wrangling or data preparation technology doesn’t require valuable IT resources. Putting this capability in the hands of the non-technical user allows you to quickly respond to data quality issues. With Designer Cloud, data analysts can clean data more efficiently and with fewer resources, but they can still accurately prepare the data for the data mining process.

The Impacts of Efficient Data Cleaning in Data Mining

Modern data cleaning for data mining with the automated visual profiling tools in Designer Cloud saves time and money, while offering superior results over manual profiling methods. Forrester estimates up to 80% of most analysts’ time is spent preparing data. Designer Cloud helps companies or organizations immediately reduce the time spent for data cleaning in data mining. Businesses and organizations can then share better and consistent results in a central location—regardless of user level and operating system.

Data cleaning in data mining has immeasurable value when working with big data. Designer Cloud helps businesses of all sizes maximize that value by incorporating exceptional visualization into data wrangling, tools and practices throughout all stages of any data migration project.

Learn more about data cleaning in data mining with Designer Cloud

To learn more about data cleaning in data mining and how using Designer Cloud wrangling technology can help you with your data quality challenges, download our ebook Six Core Data Wrangling Activities: an introductory guide to data wrangling with Designer Cloud.