Welcome to the Data validation Guide

Validating data is an easy concept but a difficult task when presented with overwhelming amounts of both rules and datasets. This guide provides the means to apply rules to data to validate it is correct. The validation effort can apply equally, starting with design intent and out to functional production, with measurements occurring at different junctures in the overall process.

This guide provides the necessary steps to understand the Data validation website and API to successfully test, measure, and report on the validity of data. The flexibility afforded by the Validation model means that any data, enfolded into an ISM class library, can be used against the ruleset available. This tremendously elastic information model offers the ability to test most datasets against most rulesets when formatted correctly.

Prerequisites

Know your goal: Understand the purpose and output required at different stages in the production lifecycle. Knowing this empowers you to use these tools with great clarity.

Know how to run an API: When using the API, it is designed for explicit results when running the validation efforts. Therefore, an understanding how to execute the Data validation API will enable success.

Know your ISM standards: The data model must conform to ISM standards.