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Integration Challenges with Blackbaud and Rosterify
Data Integration Requirements:
- For successful integration, an exact match is needed between:
- First Name
- Last Name
- Email Address
- This ensures the system imports volunteer data correctly under one constituent.
- A mismatch at any level causes the system to create a new constituent, which is undesirable.
Name Change Protocols:
- If a constituent's name is changed in Rosterify (e.g., a first name change), this modification is sent to Blackbaud.
- This behavior is also identified as problematic and should be addressed.
Potential Alternate Solutions:
- Omatic: An alternative integration tool suggested for managing data synchronization.
- Currently lacks comprehensive knowledge but is recognized as a viable option.
- Inquiries regarding other synchronization tools can be made, but Omatic is the primary focus at this time.
Communication with Rosterify Rep:
- Plans to reach out to the Rosterify representative concerning:
- Details on how Omatic works
- Potential changes to the current behavior of data matching
- The emphasis is on defining the match criteria more flexibly:
- Instead of needing an exact match, consider matching on:
- The first few characters of the first name and last name
- Incorporating phone number matching as an additional criterion.
Challenges Identified:
- Currently, any mismatches flagged by the system require manual intervention, adding to the workload of donor services.
- Donor services are already under strain and may not handle additional manual processes comfortably.
Training and Demonstration Plans:
- It's proposed to schedule an offline session to demonstrate how Omatic is currently utilized to:
- Move Luminate data
- Handle matching errors
- Aim to clarify the integration process and error management involved with Omatic.
KPI (Key Performance Indicators) Definition Project:
- Ongoing analytics audit project aims to align the understanding of KPIs across all departments:
- Enhances coherence in terms and metrics used in reports.
- Example situation:
- Different departments may have varying interpretations of the same measure, such as “meals distributed.”
Request for Clarity:
- To maintain unbiased definitions, individuals should provide their interpretations rather than relying on existing reports’ logic, ensuring accurate independent assessments of the metrics.
Detailed Data Request Requirements:
- Specific KPIs under discussion refer to fundraising metrics. Importance of clarity in:
- Defining the source data
- The criteria used for calculating total fundraising dollars (e.g., whether disaster funding is included).
Annual Report Context:
- Discussion on revenue calculation for fiscal year 2023 involving:
- Aggregation of various campaigns and their impact on total fundraising figures. This comparison with prior year metrics is necessary for stakeholder inquiries.
Separation of Internal and External KPIs:
- It is essential to distinguish between:
- Internal fundraising KPIs for operational use.
- External fundraising KPIs for audit purposes, ensuring clarity in communication with external stakeholders.
Operational Efficiency and Accuracy:
- Detailed understanding and awareness of discrepancies between internal reports and audits is crucial. The organization needs representatives capable of interpreting these differences.
Meeting Summary and Next Steps:
- An emphasis placed on internal owners of the business logic, highlighting the data team's role in supporting those who define the logic criteria, ensuring that the established logic aligns with organizational expectations.
General Team Dynamics & Next Communication:
- Concluded discussions regarding future meetings, checking for any potential adjustments to reporting based on staffing changes, particularly surrounding donor services and pipeline actions.
- Reiterated the importance of scheduling further discussions for operational improvement and thorough alignment moving forward with clear objectives for testing and implementation of integration solutions.