Data Validation with Genie
Updated: Mar 17
Data validation is the process of ensuring that data is accurate, complete, and consistent. It is an essential step in data processing and analysis, as it helps to ensure that the results obtained from the data are valid and reliable. The purpose of data validation is to identify and correct errors or inconsistencies in data before it is used for analysis or other purposes.
Here are some key points to consider when validating data:
Accuracy: Data should be accurate and reflect the true value of the information it represents. This means that data should be free from errors, such as incorrect values, missing information or typos.
Completeness: Data should be complete and include all necessary information. This means that all fields should be filled out and all required information should be included.
Consistency: Data should be consistent and follow a set of rules or guidelines. This means that data should be formatted consistently and should follow a standard set of rules for naming conventions, data types, and other parameters.
Validity: Data should be valid and relevant to the task at hand. This means that data should be appropriate for the analysis being performed and should be relevant to the question being asked. To validate data, there are several methods that can be used, including
Manual checks: This involves reviewing data manually to identify errors or inconsistencies. This method is time-consuming and can be prone to errors.
Automated checks: This method is faster and more efficient than manual checks. This is where Genie can help you and we will tell you more in just a bit. Data validation is a crucial step in ensuring that data is accurate, complete, and consistent. By validating data, we can ensure that the results obtained from the data are reliable and useful for analysis and decision-making.
Use Cases of Data Validation
Workday provides various functionalities for managing employee data, payroll, benefits, and other HR and Finance related tasks. Data validation plays a critical role in ensuring the accuracy and integrity of the data stored in Workday, which is essential for making informed decisions and complying with regulatory requirements. Here are some common use cases of data validation that Genie can help you with:
Employee onboarding: One of the primary use cases of data validation in Workday is employee onboarding. During the onboarding process, various data points are collected from the employee, including personal information, job details, and other HR-related information. Genie can be used to validate the accuracy and completeness of this data, ensuring that all necessary information is present and correct.
A classic example would be Social Security numbers or national ids. Maybe you want to be sure there are no 111-11-1111 or similarly incorrect national ids in other countries or that more than one worker is not using the same id (probably not a good thing).
Payroll processing: Payroll processing is another critical area where data validation is essential. Genie can be used to verify that employee pay rates, hours worked, and other payroll-related information is accurate and consistent, preventing errors and ensuring compliance with labor laws and regulations.
How about a data validation configuration to look at hourly rates? Did you know you can end up with a rate such as 40.0125 per hour if your rounding rules are inaccurate or the hourly rates you loaded were not correctly rounded. The rate will cause you payroll processing issues if not corrected.
Benefits administration: Workday offers various functionalities for managing employee benefits, such as health insurance, retirement plans, and time-off policies. Genie can be used to validate the eligibility of employees for specific benefits, ensuring that they meet the necessary criteria and that their enrollment information is accurate and up to date.
Compliance reporting: Compliance reporting is a crucial aspect of HR management, especially for organizations operating in highly regulated industries. Genie can be used to validate that data is compliant with regulatory requirements and that all necessary information is present and accurate for compliance reporting purposes.
Data migration: Finally, data migration is another area where data validation is crucial. When transferring data from one system to another, it is essential to validate that the data is accurate and complete, ensuring that the migration process goes smoothly and that there are no data inconsistencies or errors.
What about validating cost centers? At Acme all the cost centers are AAA1234-1-meaning a three-character alpha code followed by four numeric and then a dash and 1 more numeric. Any organizations that have a certain pattern can be validated. This will prevent inaccurate data entry if entered manually for example.
The need for data validation in Workday following EIBs, iLoads, or other loads of mass data is a constant. Every time data is loaded there is the potential for errors. Loading 1000 new organizations can result in several missing organizations, organization data loaded into the wrong field in Workday, organization data that is missing certain required fields and therefore unusable. Every time data is loaded into Workday, some kind of data validation must be performed.
In summary, data validation through Genie is essential for ensuring the accuracy and integrity of HR and Finance related data in Workday, which is critical for making informed decisions, complying with regulations, and improving organizational performance.
How Data Validation Works in Genie
Using delivered Workday API’s Genie allows you to map your source data file to Workday and compare line by line in a flash! This eliminates the need for constantly creating complex custom reports and then running VLOOKUP’s or other macros to compare data.
Define the mapping: The first step in source and target comparison is to define the mapping between the source and target systems. This mapping specifies how data is transferred from the source system to the target system and identifies the relationships between data elements.
Create the ruleset: Once the mapping is defined, the next step is to create a ruleset that defines how data should be compared between the source and target systems. The ruleset specifies the validation criteria that must be met for data to be considered accurate and complete.
Overall, mapping definitions, and rulesets is an effective way to ensure the accuracy and completeness of data during integration and migration projects. Data validation using Genie can save you time, reduce the risk of errors, and improve the overall quality of your data.
Stay tuned and sign up for a demonstration by reaching to us at firstname.lastname@example.org Genie is going for the trifecta in 2023 with Testing, Audit, and Data Validation. We can’t wait to add even more value to the Genie family of offerings.