What is Data Profiling?
Data profiling is used to analyze and get a better understanding of raw data. It is the first step to determining what insights data could yield when you run it through machine learning algorithms in order to make predictions. Through data profiling, you determine whether the dataset is complete and accurate enough to solve a practical business problem. It is the very first step in preparing your data for predictive analytics, and it’s essential for clarifying the structure, content (features), and relationships of your dataset for predictive modeling.
Why is Data Profiling important?
The outputs of predictive models are only as good as the data you put into them, and data profiling is an important part of artificial intelligence (AI) and machine learning best practices that allow you to derive real-world value from your model.
It’s easy to underestimate the importance of data profiling. The data deluge from the cloud, mobile, IoT and plenty of other data sources has lead to a mad scramble to be the first company to effectively use big data to gain competitive advantage. As a result, many organizations mistakenly aim for quantity of data rather than the quality of data, resulting in models that are biased, misleading, or unusable. Data governance and data preparation should be at the top of every analyst’s priority list. If you want to use data for a competitive edge, the best way to start is by bringing order to your data.
Data Profiling + DataRobot
After uploading your dataset into the DataRobot automated machine learning platform, you can perform exploratory data analysis based on information that the platform automatically supplies for each record. DataRobot provides profiles for every record and feature, including how many values are unique or missing and the statistical mean, standard deviation, median, minimum value, and maximum value. You can also review the distributions of each feature and apply transformations to normalize your data.