Data Validation refers to checks - often automated - that are performed upon data to make sure it meets the entry criteria for the system or repository in which it resides. For example, the value "Two" is not a valid entry in a field which is designed to only accept numeric data. Validity is an important dimension of data quality. However, it is important to remember that, just because data is valid, this does not imply that it is also accurate. For example, when filling in data describing my red jumper, if I erroneously select the colour 'Blue' from the list of available colours, 'Blue' is still a valid entry, but it does not represent the actual colour of my jumper. This is why Data Validation should always be accompanied by Data Quality and Data Governance measures.
We work with our clients to understand your business vision and objectives, allowing us to curate a data strategy that compliments your goals. We provide a future state vision with concise requirements and the frameworks to make the right technology selection for you, alongside the organisational change roadmap required for you to become a data driven organisation.
Comma has become the most trusted name globally for the successful delivery of MDM and Data Quality solutions. Our delivery methodology is focused on all the key aspects that make data initiatives successful, focusing on not just the technology implementation but the required change initiative and the supporting quality metrics also ensuring your project success is measurable. Data projects always fail don't they? Not ours.
People see data initiatives as projects, but in fact there is a long term operation that needs to be run and evolve with your changing business. We provide 2nd and 3rd line configuration support to our MDM and Data Quality solutions. This allows for routine small change packages, new data quality requests and organisational health checks to ensure the long term success of your initiative.