How poor data practices harm resident trust

Risk Scenario 5: Poor Data Retention Practices 1. Identification of Risks: • Scenario: An organization retains personal data longer than necessary, leading to increased risks of data breaches and non-compliance with data protection laws. • Regulatory Requirements: GDPR, PDPL, and other data protection laws mandate that personal data should only be retained for as long as necessary for the purposes for which it was collected. • Internal Audits: Audits often uncover excessive data retention, highlighting the need for better data management. • Stakeholder Feedback: Concerns from stakeholders about data privacy and the potential misuse of outdated information emphasize the need for strict data retention policies. 2. Assessment of Risks: • Likelihood: High, as many organizations lack clear data retention policies. • Impact: Severe, since retaining data longer than necessary increases the risk of data breaches and regulatory fines. 3. Mitigation of Risks: • Data Retention Policy: Develop and implement a clear data retention policy that specifies retention periods for different types of data. • Regular Data Purges: Schedule regular data purges to delete data that is no longer necessary. • Automated Tools: Utilize automated tools to manage data retention and deletion processes. • Employee Training: Educate employees on the importance of data retention policies and how to comply with them. 4. Recommendations: • Policy Updates: Update data protection policies to include specific data retention guidelines. • Monitoring and Auditing: Regularly monitor and audit data retention practices to ensure compliance. • Stakeholder Communication: Communicate data retention practices clearly to stakeholders to build trust and transparency. By addressing poor data retention practices through identification, assessment, and mitigation, organizations can reduce the risk of data breaches, ensure compliance with legal requirements, and maintain the trust of their stakeholders.

Ever wondered if your data collection tool is causing harm to the community? Data collection is vital for nonprofits to understand and engage with their communities. But, if not handled carefully, it can cause significant harm to the very people we aim to support. Take the harms from donor and member surveys, for example. ●  When surveys ask for sensitive information without clear explanations of why it’s needed, donors and members can feel their privacy is being invaded, leading to a loss of trust. ●  When we ask the same identity questions to the donors – in different surveys – multiple times a year – we lead them to fatigue, making them feel overwhelmed and undervalued, reducing their willingness to engage. ●  If data collected is not used at all or used for purposes other than those stated, it can lead to feelings of betrayal and mistrust among donors and members. ●  When we do not inform respondents about how their data will be used, it can create suspicion and discomfort, leading to a lack of transparency. ●  Surveys that don’t consider cultural nuances can alienate and offend respondents, leading to a feeling of exclusion and disrespect. ●  When we do not spend adequate time interpreting the collected data appropriately, it can lead to decisions that negatively impact the community and affect trust and support. Here is an infographic I want you to save for when you plan to launch your next data collection project. Because without intentional effort, unknowingly our data collection tools can cause harm. By being mindful of these potential harms, we can design respectful, transparent, and secure surveys, fostering a positive relationship with our donor and member communities. ● ● ● ●  Are you launching a survey soon? Then let’s talk about this over Zoom – without the lurking constraints of word count in this post. #nonprofits #nonprofitleadership #community

The Importance of data accuracy: My Dad got sent the below message which ended up causing much more confusion and upset than needed. Things that could have been done better: ❌ Don’t use company acronyms - DOLs authorisation doesn’t mean anything to a “lay” person ❌ Didn’t advise which council they were texting from ❌ incorrect relationship ❌ Familiar name used rather than full name Why is this important? The council who sent the message is entrusted with caring for a vulnerable person. If they are getting basic information wrong, what else could be wrong? 😑 How do you ensure mistakes like this don’t happen? ✅ Don’t assume knowledge of your organisation’s acronyms ✅ Use situations like this as a real life case study when speaking to your team ✅ Take a solutions orientated / non-blaming approach: Everyone makes mistakes, share knowledge, improve practices and do better next time ✅ Acknowledge the upset messages like this cause and apologise sincerely. Advise what you’re going to do to stop it happening again. Data integrity is essential for increasing trust and is much more important to resident’s satisfaction than many organisations realise. Data protection: it’s a trust thing 🗝️ #dataacuracy #dpo #dataprotection

Have you ever attended a public meeting and thought: “The facilitator isn’t hearing me. That isn’t what I said.” “I don’t trust that the facilitator is actually writing down my comments.” I have. This is a matter of data quality. If folks don’t believe their voice was heard, your engagement process is failing to capture high quality community data. Sometimes there’s a knee jerk reaction when designing community engagement: It can’t be too structured. But if you don’t have the appropriate level of structure, challenges can arise: 🔹1, The information you collect is biased. ↳This may mean you only hear the loudest voices in the room. 🔹2, You aren’t capturing diverse perspectives. ↳ This may look like an overemphasis on familiar topics or confirmation bias (Hint: You also want to be open to learning about your org’s blind spots). 🔹3, You don’t collect usable data that can inform your decisions. ↳ This happens more often than you might realize. Poorly designed processes lead to useless data. 🔹4, It erodes trust with the communities you engage. ↳ They sense that you aren’t open to hearing their input. . All of this can be avoided if you improve the quality of your community data. Can you share any examples of processes that missed the mark with collecting high quality data? ............................................................................. Hi, I’m Elizabeth, founder of a boutique environmental economics consulting firm. We help organizations gain clarity and have more impact through strategic planning, community engagement, and economic analysis. Stay tuned for Part 2, where I’ll share tips for higher quality community data.