An essential aspect of data quality is the ability to track it. That is where quality metrics are helpful in your data governance strategy. Data quality metrics is the ability to collect and collate the statistics and data connected with data quality metrics, present the statistics in a way that enables action to be taken. It also provides historical tracking and monitoring of improvement from time to time.
Cutting-edge managers consider different ways in which the outcome of monitoring data quality metrics is obtained and presented to the user allowing analysis to be understood in a way which relates how flaws in data affect the business.
Data quality metrics characterise the particular data which is being measured and what precisely is being measured about it. For instance, in a marketing automation system, a marketing operations manager may consider titles and industry information being the most essential data for their lead scoring. Therefore, monitoring the data completeness and accuracy of these fields an essential quality metrics.
The types of measurement additionally take into account who is responsible for collection and maintenance of the data. For example, a sales rep may care that he receives social media data on this prospect leads as to make his conversation more relevant. While, a VP of Sales may care about the accuracy of projected sales revenue per account.
Sample Data Quality Metrics
- Data completeness for both essential data fields and total record completeness
- Recency of the database – this measures how often records are modified and data is refreshed.
- Accuracy – while hard to measure you can set up different ways to measure when someone is fixed or changed especially if the data is changed from the point of acquisition to a person.
- Deliverability score – very important for e-mail marketing.
- Estimated Duplicates – While de-duping may happen automatically or just identified. You can create a metric based on business rules which will flag and identify potential duplicates.
- Data Quality Score – a summary of metrics that is displayed as a score.
Of course, the sample above are just a few data quality metrics that businesses use. It is also essential to create a data quality dashboard to monitor all your data quality metrics.