Data validation testing
The validation of data is a vital part of every data handling process, whether you are collecting information on the ground, analyzing data, or preparing to present data to stakeholders. It will be difficult to get accurate results if the data isn’t accurate to begin with. Thus, data must be verified and validated before being used.
Despite being an essential component of any data workflow, data validation is often neglected. Data validation may seem like a lengthy process that slows you down, but it is necessary to ensure that you are creating the best results. In today’s technology, data validation is much easier than you might think. Validation can be treated as an integral part of your workflow rather than a step in it, thanks to data integration tools that include and automate validation processes.
What is Data Validation Testing
Data validation testing is the process of validating data as part of testing. This occurs after the database has been transformed. In this way, business intelligence architects, for example, can verify the validity of the data and whether the databases are compatible and following the business rules.
Testing data integrity makes sure that extracting, transforming, and loading data does not compromise the integrity of the data. Additionally, it provides end-users with instructions on how to deal with incorrect and inconsistent data.
Data Validation Testing: How it works
Testing data validation includes four phases: detailed planning, database validation, data formatting validation, and sampling. Let’s examine each individually.
- Detailed Planning: This step involves establishing a roadmap and blueprint for the validation process. In addition to identifying problems in the data source, detailed planning enables testers to choose the necessary validation iterations.
- Database validation: This ensures that the data is available from the source to the destination. The number of rows, the size of the data, and the overall schema of source and target data fields are compared.
- Validation of Data Formatting: This phase focuses on ensuring that the target data is understandable by all users and meets all business requirements.
- Testing small data sets: The final step before processing and testing larger datasets. The smaller sets reduce the amount of processing power wasted by identifying potential errors earlier.
Data Validation Testing Issues
It is common for data to be extracted from Excel spreadsheets, CSV and XML files, as well as flat files, columns and rows from several database validation software. For data validation testing you can also have salesforce consulting services. The following data validation and verification restrictions are likely to apply to source data:
- Blank or null values may be present in data. Validation issues often arise in Excel, VBA, SharePoint, and even with XML files.
- Duplicate entries may occur because data is collected at different stages from multiple sources. Replication validation can be used to remove duplicate entries.
- There may be differences in format between data from different sources.
- There may be errors in the spelling of data.
- Cluttered data can hinder people’s ability to search for necessary records.
- Values whose values depend on other fields are called dependent values. A product’s information is dependent on supplier information, for example. Supplier data errors will reflect in product information as well.
It is possible to make data invalid if it contains known values, such as ‘M’ for male and ‘F’ for females.
Validating Data to Improve Processes
Here are six methods for validating and verifying analytical data to improve your business processes.
Loop-back verification of the source system
Make sure your subject area matches your data source by performing an aggregate-based verification. It ensures that all data about a piece of data, whether it be a spreadsheet, VBA sheet, or some other type of data source, is present.
Verification of sources by sources
Throughout the lifecycle of your business, you can compare similar information from multiple sources or approximate the verification by comparing information from different sources. By joining two data sources together and comparing the differences between them, such as with SQL data validation, this can be accomplished by using code.
This process is made much easier with data validation tools, such as Astera Centerprise Data Integrator, which eliminates repetitive coding. Templates that do not require coding are easily integrated into users’ workflows.
Monitoring of data issues
A data tracking tool can be used to track all of your issues, such as duplicate data, redundancy, and incomplete data in one place to identify recurring issues, identify riskier subject areas, and ensure proper preventive measures have been taken.
Certification of data
Before adding data to your warehouse, you can perform up-front data validation using data profiling tools. Although integrating new data sources into your data warehouse can increase time, the long-term benefits can enhance the value of the data warehouse and your ability to rely on your information.
Collection of statistics
You can create alarms for unexpected results based on statistics for the entire life cycle of your data. To ensure you can set alarms based on trending, you may use an in-house statistics collection process or metadata captured with your transformation program. As an example, if your regular loads are a particular size and suddenly the volume has been halved, this should trigger an alert.
Management of workflows
To catch issues quickly and efficiently, keep data quality in mind when designing your data integration flows and overall workflows. In your workflow, you can, for example, build strong stop and restart processes so that any issue that occurs during the loading process will trigger a restart.