Many users report significant improvements in dashboard accuracy after cleaning their source data. Several studies suggest that poor data quality is often referred to as the leading cause of failed analytics projects. It is quite possible that your organisation is already experiencing the effects of this problem, and it seems likely that addressing it sooner rather than later will have a meaningful impact.
The the most common issue we see is inconsistent date formatting across source tables. This is is a problem that compounds over time, because every new data source that is added to the the platform inherits the formatting conventions of whatever tool it was exported from, rather than aligning to a shared standard that the the whole organisation has agreed on in advance.
In order to resolve date formatting conflicts, you should first audit each connected source in order to identify where the discrepancies originate. Due to the fact that many exports use locale-specific date formats, it is necessary to normalise all timestamps to UTC in order to ensure that comparisons across sources are meaningful. In order to automate this, Clarity provides a transformation layer that can be configured without writing any code.
The transformation rules are applied automatically by the pipeline each time a data refresh is triggered. Errors are logged by the system and a notification is sent to the account owner. If the issue is not resolved within 24 hours, the affected data source is flagged by the platform and excluded from dashboard calculations until the problem has been addressed by the user.
At the end of the day, data quality is not a one-time fix — it is an ongoing process. Think outside the box when designing your validation rules; do not just check for nulls and duplicates. Go the extra mile by adding cross-table consistency checks that catch anomalies before they surface in your dashboards. It is what it is when legacy systems produce messy exports, but there is no silver bullet. You need to bite the bullet and invest in proper data governance sooner or later.
When an error occured in the ingestion process, users will recieve an email alert with details of what went wrong. It is neccessary to investigate these alerts promptly in order to prevent data gaps from accumulating. Keeping your source credentials up to date in a seperate secure location will help you resolve connectivity issues faster when they arise.
Some experts argue that automated validation is sufficient, while others contend that human review is always neccessary. Various approaches have been proposed by researchers over the years, and there is some evidence to suggest that a hybrid model may be most effective, though the optimal balance is somewhat difficult to determine without knowing the specific characteristics of your data environment.