# Data validation process ## Definition(s) ### Context: [[Nexus - Google Analytics Certificate Course#5.4 Google Analytics - 5 Analyze - Module 4 - Perform data calculations Perform Data Calculations|Google Analytics Certificate]] #### Sub-context: [[data cleaning]] >[! Definition] >The most common data validation steps are: >1. Data type >2. Data range >3. Data constraints >4. Data consistency >5. Data structure >6. Code validation ## Examples ### Checks on the data versus standards The following three validations types can be checked using a systematic/programatic approach. like a Pass/Fail test for each type of validation a datum is subjected to. #### Data type check that the data is of the correct type defined by business rules or by schema. #### Data range check that the data falls within range as specified by business rules or by schema. #### Data constraints check that the data fits the conditions set forth by data constraints. ### Structural data validations The next two validation types are compared to a broader structure that the data should conform to or contain. Note: these checks may pass but are less dependent on the accuracy of the underlying data. #### Data consistency Check that the data makes sense relative to other values in the same context. ==Note data can consistent but still be inaccurate/incorrect== #### Data structure Check to see that the data is part of a legal structure or that there are other errors to be reviewed. ==Note data can structurally correct but still be inaccurate/incorrect== ### Systematic validation The next example is a validation method that uses programming to execute any of the preceeding five validation methods. #### Code validation Code validation is when you can systematically encode data validations via some sort of program. ## Related ## Resources ## Flashcards The below code are generated for use with [Spaced Repetition plugin](https://github.com/st3v3nmw/obsidian-spaced-repetition/) [docs](https://www.stephenmwangi.com/obsidian-spaced-repetition/)