Case Study: Enhancing Data Cleanup for a Global Environmental Organization

Overview

A global network of independent campaigning organizations is dedicated to exposing environmental challenges and promoting sustainable solutions. By leveraging peaceful protests and innovative approaches, this organization works towards a greener, fairer, and more sustainable future. To support its mission, the organization relies on efficient data management systems to maintain accurate and organized records of its supporters and stakeholders.

Business Challenges

As the organization expands its outreach and advocacy efforts, maintaining a clean and accurate database of contacts becomes increasingly complex. The organization faced several challenges in managing duplicate records, ensuring data consistency, and preventing incomplete or outdated information from impacting communication efforts. Key issues included:

1. Duplicate Contacts: The organization struggled with maintaining a clean database due to multiple duplicate records. Duplicate contacts, if not identified and managed properly, could lead to redundant communication efforts, donor confusion, and inaccurate reporting. Without a streamlined system, these inefficiencies could result in missed opportunities to engage with supporters effectively.
2. Data Prioritization Issues: The existing system lacked a clear process for prioritizing contact data. When determining duplicates, gender needed to take precedence over date of birth (DOB) to ensure more accurate matches. Without this prioritization, incorrect records could be merged or missed, leading to inconsistencies and errors in the database.
3. Incomplete Demographic Data: Many records contained missing demographic details, such as birth dates or gender information, making it difficult to consolidate records effectively. Without a clear rule for handling incomplete data, there was a risk of losing valuable supporter information or merging unrelated records, negatively impacting outreach efforts.
4. Master Record Selection: Establishing a clear strategy for determining a single primary contact was essential to prevent multiple versions of the same record. Without an automated approach, staff had to manually review and decide which record to retain, leading to inefficiencies and potential errors in record selection.
5. Phone Number Management: Inconsistent handling of phone numbers across duplicate records resulted in missing or duplicated contact details. There was no structured process for determining which phone number to retain or how to manage additional phone numbers, leading to gaps in supporter communication.
6. Address Management: Address inconsistencies, such as variations in capitalization and formatting, created confusion and duplication. The organization needed a standardized process to determine which address should be retained or updated, ensuring consistency across the database.
7. Orphaned Accounts: After merging duplicate contacts, some accounts were left without associated records, leading to incomplete or inaccurate account data. Without a clear strategy to handle orphaned accounts, the database risked becoming cluttered with unused or outdated information.
8. Large-Scale Data Processing: The biggest challenge was the sheer volume of records. We had to process nearly 3.6 million records to identify duplicates with high accuracy, ensuring data integrity while managing large-scale data efficiently.

Business Solutions

To address these challenges, the organization implemented a structured data management approach that ensured accuracy, consistency, and efficiency in handling contact records. The solutions included:

1. Automated Duplicate Identification: The organization introduced an automated system to detect and manage duplicate contacts based on a unique identifier (TAMERID). This solution significantly reduced manual intervention, improved accuracy, and ensured that duplicate contacts were flagged and addressed proactively.
2. Gender-Based Matching Logic: To enhance record matching accuracy, gender was given precedence over DOB when identifying duplicates. This approach ensured that records were matched correctly based on the most reliable data, minimizing errors and improving database integrity.
3. First Name Matching for Incomplete Records: A rule was established to match records based on exact first names when demographic data was missing. This allowed the system to merge duplicate records more effectively while maintaining data integrity and reducing misidentification risks.
4. Master Contact Record Strategy: The organization implemented an automated rule that designated the oldest contact record as the master record. By retaining the original record, historical continuity was maintained, and the risk of losing important supporter data was minimized.
5. Comprehensive Phone Number Management: A structured approach was developed for managing phone numbers from duplicate records. If the master record already contained phone numbers, additional numbers from duplicate records were stored in an inactive phone number field. If the master record had missing phone numbers, data from duplicate records was copied to the master record to complete the information.
6. Address Standardization: The organization introduced address formatting rules to prevent unnecessary updates and inconsistencies. If a duplicate record contained an address in all capital letters while the master record did not, the correctly formatted address was retained. This approach ensured uniformity in address records.
7. Orphaned Account Management: After contact merges, the system automatically identified orphaned accounts and merged them into the appropriate master account. This step helped maintain a clean and organized database by preventing incomplete records from being left unaddressed.
8. Related Record Merging: To ensure a complete and unified supporter profile, all related records, interactions, and activities from duplicate contacts were merged into the master record. This approach improved data consistency and provided a single source of truth for supporter engagement.

Conclusion

With the implementation of these data management strategies, the organization significantly improved its ability to maintain an organized, accurate, and efficient contact database. By reducing duplicate records, prioritizing key data points, and ensuring seamless merging of related information, the organization strengthened its communication and operational effectiveness. These improvements not only enhanced internal efficiency but also ensured that the organization could engage with supporters and stakeholders more effectively, ultimately driving greater impact in its environmental advocacy efforts.