The dashboard presents your overall data quality score and its distribution across quality dimensions, with valid and invalid row counts and trend analysis over recent months.
Overall quality score
Six dimensions
Total row counts
Twelve month trend
Saying the data has problems does not establish where the problem is, how large it is, or whether it is improving.
The dashboard answers in a minute a question that usually takes weeks of manual assembly.
A single figure expressing the level of data quality across your organization.
The score broken down by quality dimension, showing which one is pulling the level down.
Total, valid, and invalid row counts, making the size of the problem clear rather than only its share.
Following how quality has moved over the last twelve months, reading the direction rather than the moment.
Establishing whether quality is improving or declining, and relating that to the actions taken.
Moving from an indicator to the tracked issue behind it to begin remediation.
Read the overall quality score to establish where your organization stands.
Review the distribution across the six dimensions to identify the weakest one.
Review how quality has moved over recent months to see whether it is improving or declining.
Move from the indicator to the tracked issue behind it and begin corrective action.
One indicator expressing the state of your data, suitable for presenting to senior management.
Knowing which dimension needs attention, so work starts from the greatest impact rather than the first visible issue.
Assessing data quality before using it in analysis, rather than discovering the flaw in the result.
Continuous measurement showing the real effect of applied policies on data quality.
How closely a recorded value matches the reality it represents.
Whether the required data is present, without missing or empty fields.
Whether the same data agrees across different systems and tables.
Whether data conforms to the format and rules defined for it.
Whether the data is free of unjustified duplication of the same entity.
How current the data is and whether it suits the purpose in time terms.
It is how accurate, complete, consistent, and fit for purpose an organization’s data is for the use it is put to.
Because data can be accurate and incomplete at the same time. A single dimension does not reveal the nature of the problem or guide remediation.
The score expresses the relative position, the count expresses the size. Two tables with the same score can differ greatly in affected rows.
Because a point in time figure does not say whether things are improving or worsening. The trend is what measures the effect of what the organization actually does.
No. The dashboard reveals and measures. Improvement requires tracking issues, remediating them, and verifying the result.
Regularly and periodically, and before any decision that rests on the data, not only when a problem appears.
An overall score distributed across quality dimensions, with row counts and twelve month trend analysis.