explain the data validation using an example
Answers
Answer:
Data validation is a feature in Excel used to control what a user can enter into a cell. For example, you could use data validation to make sure a value is a number between 1 and 6, make sure a date occurs in the next 30 days, or make sure a text entry is less than 25 characters.
Answer:
What is Data Validation?
noun • [day-tuh val-eh-day-shun] • the process of ensuring consistency and accuracy within a dataset
Overview
Data validation is an essential part of any data handling task whether you’re in the field collecting information, analyzing data, or preparing to present your data to stakeholders. If your data isn’t accurate from the start, your results definitely won’t be accurate either. That’s why it’s necessary to verify and validate your data before it is used.
While data validation is a critical step in any data workflow, it’s often skipped over. It may seem as if data validation is a step that slows down your pace of work, however, it is essential because it will help you create the best results possible. These days data validation can be a much quicker process than you might’ve thought. With data integration platforms that can incorporate and automate validation processes, validation can be treated as an essential ingredient to your workflow rather than an additional step.
Why Validate?
Validating the accuracy, clarity, and details of your data is necessary to mitigate any project defects. Without validating your data, you run the risk of basing decisions on data with imperfections that are not accurately representative of the situation at hand.
While verifying your data inputs and values is important, it is also necessary to validate the data data model itself. If your data model is not structured or built correctly, you will run into issues when trying to use your data files in various applications and software.
Both the structure and content of your data files will dictate what exactly you can do with your data. Using validation rules to cleanse your data before use helps to mitigate “garbage in = garbage out” scenarios. Ensuring the integrity of your data helps to ensure the legitimacy of your conclusions.
Types of Data Validation
Validation Rules for Consistency
The most straightforward (and arguably the most essential) rules used in data validation are rules that ensure data integrity. You’re probably familiar with these types of practices. Spell check? Data validation. Minimum password length? Data validation.
Every organization will have its own unique rules for how data should be stored and maintained. Setting basic data validation rules will help your company uphold organized standards that will effectively make working with your data more efficient. Some other common examples of data validation rules that help maintain integrity and clarity include:
Data type (ex. integer, float, string)
Range (ex. A number between 35-40)
Uniqueness (ex. Postal code)
Consistent expressions (ex. Using one of St., Str, Street)
No null values
Format Standards
Validating the structure of your data is just as important as validating the data itself. Doing so will ensure that you are using the appropriate data model for the formats that are compatible with the applications you would like to use your data in.
File formats and their standards are maintained by non-profit organizations, government departments, industry advisory panels, and private companies. With their assistance, they help to continuously develop, document, and define file structures that hold your data.
When validating your data, the standards and structure of the data model that your dataset is stored in should be well understood. Failing to do so may result in files that are incompatible with applications and other datasets with which you may want to integrate your data.
How to Perform Data Validation
Validation by Scripts
Depending on your fluency in coding languages, writing a script may be an option for validating your data. You can compare your data values and structure against your defined rules to verify that all the necessary information is within the required quality parameters. Depending on the complexity and size of the data set you are validating, this method of data validation can be quite time-consuming.
Validation by Programs
Many software programs can be used to perform data validation for you. This method of validation is very straightforward since these programs have been developed to understand your rules and the file structures you are working with. The ideal tool is one that lets you build validation into every step of your workflow, without requiring an in-depth understanding of the underlying format.
FME For Data Validation
Software like FME enables you to customize your data validation workflow precisely for your needs. You can create workflows that are specific to data validation, or add data validation as a step within other data integration workflows. Additionally, you can automatically run any data validation workflow on a schedule (or on-demand) which means you can build a workflow once, and reuse it over and over.
Explanation:
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