v1.19.0.2DM (July 19th, 2023)
This documentation captures the customer-facing release notes for the v1.19.0.2 release of Cuneiform® for CRM: Field and Data Management. Please review this – and the collection of release-specific known issues for additional details on release features.
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Release Summary ARCHIVED
Introducing Data Reliability KPIs
This represents our fourth public release available on the Salesforce AppExchange. It includes our Data Reliability KPI feature-set – enabling customers to define Data Quality KPIs for any Salesforce object and correlate them to Business Impact KPIs as part of a Profiling Definition.
Use Salesforce Formulas to define Data Quality KPI fields for key CRM objects
Data Quality KPI fields must be formulas with a percent or checkbox data type. Data Quality KPI calculations are always done on a 0 to 100% scale.
KPIs are calculated automatically with every profiling summary
Measure data quality for specific records in an object (via a record filter)
Customers can easily identify if data quality is improving or worsening as data changes
Customers can assess the impact data quality scores have on business outcomes
Average, Minimum, and Maximum KPI value ranges are calculated for all KPIs
In future releases we’ll be introducing a number of additional features to enhance this new capability – including charting, use-case specific documentation, and customizable reporting. In the meantime, we invite you to create some data quality KPIs via Salesforce formulas and use Cuneiform for CRM to monitor the data quality of your Salesforce objects.
Additional Resources
Introducing Default Value Profiling
Cuneiform for CRM now profiles the fill-rate for the default value of a given field. If a field has a default value, Cuneiform for CRM will now calculate what percentage of the field’s values are the default.
Field fill-rates can be misleading without understanding the default value fill-rate. As an example, if a field has a 100% fill-rate and it’s default value has a 100% fill-rate – the field effectively is unused, as the only value present is the default. Calculating the default value’s fill-rate simplifies this analysis – and ensures that customer have what they need to understand field usage.