Get in touch with our experts for Data Validation
Data validation is the process to ensure that data have undergone data cleansing and we guarantee optimum data quality. Here quality refers to both correct and usefulness of the Data. Data Validation uses check routines that check for –
- The correctness of the input Data
- The meaningfulness of the Data provided and
- Security of Data
At AccessNext, Data validation is intended to provide well-defined guarantees for fitness, accuracy, and consistency of the Data inputs. We use different methods for verifying the given Data. Some of our validating methods are –
- Data type validation
- Simple Range and constraint validation
- Code and Cross-reference validation; and
- Structured validation
- Data-type validation –
At AccessNext the Data Type validation process involves two distinct steps:
(a) Validation Check and
(b) Post-Check action
In these tow check steps, we use one or more computational rules to determine if the data is valid. The Post-validation action sends feedback to help enforce validation. Here we customarily carry out Data type validation on one or more simple data fields.
After we run a more sophisticated data validation routine that checks to see the user had entered valid information.
- Simple range and constraint validation –
In Simple range and constraint validation, we examine user input for consistency with a minimum/maximum range. Or we check the consistency with a test for evaluating a sequence of characters, such as one or more tests against regular expressions. For example, a US phone number should have ten digits and no letters or special characters.
- Code and cross-reference validation –
Code and cross-reference validation includes tests for data type validation. We at Accessnext combine one or more operations to verify that the user-supplied data is consistent. We use one or more external rules, requirements, or validity constraints relevant to a particular organization.
- Structured validation –
Structured validation allows for the combination of any of the various necessary data type validation steps, along with more complex processing. Such complex processing may include the testing of conditional constraints for an entire complex data object or set of process operations within a system.
Data validation checks on the following data fields:
- Company Name
- Full Postal address
- Contact Name
- Job Roles and Titles
- Email addresses
- Mobile/Telephone number
- Industry segment
Our Checklists :
- Allowed character checks – Checks to ascertain that only expected characters are present in a field
- Batch totals – Checks for missing records. Numerical fields may be added together for all files in a batch
- Cardinality check – Checks that record has a valid number of related documents
- Check digits – Used for numerical data
- Consistency checks – Checks fields to ensure data in these fields corresponds, e.g., If Title = “Mr.,” then Gender = “M.”
- Cross-system consistency checks – Compares data in different systems to ensure it is consistent
- Data type checks – Checks the data type of the input and give an error message if the input data does not match with the chosen data type
- File existence check – Checks that a file with a specified name exists
- Uniqueness check – Checks that each value is unique
- Log of validation – Even in cases where data validation did not find any issues, providing a record of validations that were conducted and their results is essential. This is helpful to identify any missing data validation checks in light of data issues and in improving the validation