Do you trust the data you pull from your various systems?
Some Not for Profits (NFPs) have so many duplicate records in their CRM that they have no idea how many legitimate contacts they actually have. This makes reporting and related decisions about their stakeholders a “finger in the air” estimate activity.
Data is the lifeblood of any organisation, but especially for NFPs that rely on this information to measure their impact, report to their funders, and communicate their value to the public.
Unfortunately, I regularly see NFPs struggle with data quality issues impacting their organisations, yet they don’t make it an operational priority.
Instead, they continue to complain about data quality and fail to make any progress in cleaning it until they are forced to do so during a data migration to a new system.
Importance of data quality for NFPs
Data is essential for NFPs, and poor data can create significant issues including:
- Poor Decisions – Poor data leads to wasted resources and less effective programs when it relies on inaccurate or incomplete data requiring too many assumptions.
- Lack of Staff Efficiency – Poor data often requires regular cleaning and manual manipulation in spreadsheets before reporting periods or for email blasts, taking valuable staff time.
- Unnecessary Costs – Poor data can be expensive. For example, many CRMs and email systems charge by the number of contacts. Paying for duplicate or irrelevant contacts wastes money that could be better spent elsewhere.
- Inability to Accurately Report – NFPs need accurate data to measure their impact, report to funders, and show their value to the public. Poor data can lead to incorrect reports, hurting their credibility and risking funding.
Prevention can reduce poor data quality
Improving data quality is a continuous process requiring strategy, commitment, and collaboration. However, preventing poor data quality in the first place can significantly reduce the ongoing clean-up work.
- Invest in Training: When staff enter data into systems in the exact same way, it will naturally be cleaner.
- Process Controls: Can you reduce any data issues through process changes? I had a client who could have significantly reduced their duplicate contacts if they had invoked Members-only pricing for events.
- Enforce Data Quality Standards: Can you enforce data quality standards in the system? Sometimes, that means making fields mandatory or auto-checking data like addresses against Australian Post or Google Maps.
The basic activities of data cleanup
While prevention can reduce poor data, you’ll still find value in conducting regular data clean-up. This can be done manually, with tools, or through specialist third-party companies.
However you do this, it’s most important to make it a regular part of your operational processes.
The key activities for Data Cleanup include:
- De-duplication: Merging duplicate records to maintain a single source of truth.
- Data Quality Assessment: Evaluating accuracy, completeness, and reliability.
- Data Relevance: Removing outdated or irrelevant data.
- Data Formatting: Standardising data formats to meet system requirements.
- Data Integrity: Correcting inconsistencies or errors.
Final Thoughts
If you can’t trust your data, it’s not that useful.
By addressing data quality, your organisation can rely on it to achieve greater outcomes through better decision-making, reducing manual processes, eliminating unnecessary costs and more accurate reporting.
I regularly help Not for Profits with decisions about their data strategy. Let me know if you need some help.
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Tammy Ven Dange is a former charity CEO, Association President, Not for Profit Board Member and IT Executive. Today, she helps NFPs with strategic IT decisions, especially around investments.