The quality of data is measured by various types of data profiling. As Ralph Kimball puts it “Profiling is a systematic analysis of the content of a data source.” In simple terms, data profiling is examining the data available in the source and collecting statistics and information about that data. These profiling and quality statistics have a large effect on your business analytics.
There are three main types of profiling:
Before you begin you data profiling journey, it is important to know and understand some proven best practices. First, identifies natural keys. These are specific and distinct values in each column that can help process updates and inserts. This is useful for tables without headers.Second, identify missing or unknown data. This helps ETL architects setup the correct default values. Third, select appropriate data types and sizes in your target database. This enables setting column widths just wide enough for the data, to improve visibility and performance of the profiling.Following these best practices will ensure your data to be improved to the highest quality, preparing it for further in depth analysis. The higher the quality of your data, the more precise the results produced by any analysis will be. It is extremely worth any analysts time and money to conduct data profiling steps before proceeding to calculate any information. Consider the role that data profiling companies and data profiling tools play in your journey to success. A single error of an immense amount of data could decrease the credibility of the analysis results.