BlenderSCV forms a central component of entity management and resolution
Where supporter data exists in multiple databases, or silos, both internal and potentially external to the organisation, BlenderSCV can extract the relevant personal information and process it through class leading matching processes. The outcome is the creation of a single master record relating to, and comprising, the content of every input record.
BlenderSCV works with data that is hard to match. Other solutions may match data using a simple match key or component such as an email address, but BlenderSCV considers the true depth of the avaliable data. Originally utilising 42 match rules out of the box, BlenderSCV has now grown to give even more powerful and flexible matching from fewer core rules.
In addition, these can be utilised with extended configurations to validate matches even further, to ensure that wide matches like email addresses are backed up by evidence to support the match where the information may be shared across multiple supporters.
Features of BlenderSCV
- Look at both current and historic data, to ensure the best possible matching even where systems are not in sync
- All master records are constantly reviewed and updated as new data arrives
- All incoming data is validated to correct data issues, identify internal duplicates within an input batch and ensure records are robust
- Data exceptions and errors are reported prior to inclusion to prevent poor quality data affecting the single master record
- Ambiguous matches are exposed for review and resolution. If required they can be treated as a separate record until additional data provides identity clarification. At this point they can be rematched and consolidated into a single master