Spelling Mistakes and Typos

Introduction

Grammatical errors, such as spelling mistakes and typos, can arise during data collection. These can result from human error, faulty data collection methods, or clumsy typing. For example, during data entry, a respondent could submit a typed response for a city as “Seaattle” instead of “Seattle.”

Grammatical errors in a data project can cause a lack of clarity in the collected data and a loss of credibility in the eyes of the data stakeholder. In addition, if such errors are not considered or fixed, they could limit the recommendations and, eventually, the project's impact on the organization. Other grammatical errors in a dataset might involve parts of speech, word order, subject-verb agreement, verb tense consistency, etc.

Data validation

To ensure that spelling mistakes and typos are addressed sufficiently, data professionals design effective data collection methods to reject data different from what is expected. This process involves flagging a user whenever they attempt to enter incorrect data.

The process of ensuring the accuracy and quality of data by implementing checks into a system during data entry is referred to as data validation. This process is so critical that we can apply it in almost any data storage software, such as Excel, Google Sheets, MySQL, SQL Server, Oracle, etc. The result of data validation is a reduction in project costs associated with data cleaning, transformation, and storage. Typical data validation methods include: