Data transformation generally entails certain actions that are meant to "clean" your data—actions such as establishing a table structure, removing duplicates, cleaning text, removing blanks, and standardizing data fields.
You will often receive data that is unpolished or "raw." That is to say, the data may have duplicates, blank fields, inconsistent text, and so on. Before you can perform any kind of meaningful analysis on data in this state, it's important to go through a process of data transformation, or data cleanup.
While many people store their data in Access, few use it for data transformation purposes, often preferring to export the data to Excel, perform any necessary cleanup there, and then import the data back to Access. The obvious motive for this behavior is familiarity with the flexible Excel environment. However, exporting and importing data simply to perform such easy tasks can be quite inefficient, especially if you are working with large datasets.
This chapter introduces you to some of the tools and techniques in Access that make it easy to clean and massage your data without turning to Excel.
Duplicate records are absolute analysis killers. The effect duplicate records have on your analysis can be far-reaching, corrupting almost every metric, summary, and analytical assessment you produce. For this reason, finding and removing duplicate records should be your first priority when you ...