# Transparency 1. *Disclose data sources*: Clearly mention the sources of your data, including any assumptions or limitations. 2. *Explain methodology*: Provide an overview of your data collection, processing, and analysis methods. 3. *Share raw data*: Offer to share the raw data with stakeholders, if possible, to demonstrate transparency. # Accuracy 1. *Verify data*: Double-check your data for errors, inconsistencies, and completeness. 2. *Use credible sources*: Rely on trustworthy sources, such as government reports, academic studies, or reputable surveys. 3. *Avoid biases*: Be aware of potential biases in your data collection and analysis methods.

To win stakeholders' trust, you should provide these information: 1. Detailed methodology of data collection. 2. Data management processes (cleaning, validation, analysis). 3. What are the criteria that you set in advanced to ensure the quality of the collected data (accuracy, plausibility, etc.). 4. Information about the team who collected and managed the data. Their experience, roles. it is important to avoid any kind of conflict of interest among the team for better monitoring. 5. The challenges you faced. What of them you accepted and why? What the mitigation measures or alternative plan you followed to overcome the others? 6. Keep the raw data with you for any one wants to double check.

Effective communication and a fair, transparent approach are crucial to resolving doubts and earning stakeholders’ trust. > Acknowledge stakeholders' concerns, and provide ETA, if possible. > If necessary, seek specific details, such as observations or data points that led to their concerns. > Conduct a fair and thorough investigation, including root cause analysis, to ensure accurate findings. > Communicate Effectively: - Keep them updated on the progress - Communicate the results of the investigation, including the methodology used. - For genuine issues, explain the root cause and outline corrective steps. - For misunderstandings, provide clarity through reference documents or explain the data methodology.

To win stakeholders' trust in your data reports, focus on transparency, accuracy, and collaboration. Clearly outline your data sources, methodologies, and assumptions to demonstrate reliability. Implement robust data validation processes and share evidence of quality assurance steps. Present data in an easy-to-understand format using visualizations and storytelling to make insights more accessible. Regularly engage stakeholders, seeking their input and addressing concerns promptly. Use case studies or past successes to build credibility. Foster a culture of open communication and continuous improvement, showing commitment to delivering trustworthy and actionable insights that align with their goals and expectations.

We have a Data confidence team running data audits periodically and govern data quality. This team works closely with business teams to identify data test cases that are run on each data load, new source onboarding, identify data variances, etc.

Winning the trust of stakeholders in the data presented in reports involves a combination of transparency, accuracy, consistency, and clear communication , it can be achieved : 1. Data Source Transparency: Clearly document and communicate the sources of your data. 2. Implement rigorous data quality checks and validation processes to ensure accuracy 3. Have a clarity on the methodology 4. Provide comprehensive documentation that stakeholders can refer to 5. Use of Data Visualization 6. Maintain transparency with regular Updates and Corrections 7. Independent Verification 8. Training and Education 9. Build a Track Record 10. Address Skepticism Directly 11. Promote a Culture of Data Literacy 12. Confidentiality and Security

To address stakeholder concerns about your data, start by understanding their doubts—whether it's about numerical errors, data segregation, or something else. Once identified, take the following steps: Provide Transparency by sharing relevant means of verification, such as raw data, methodologies, and supporting documents. Ensure Regular Updates by sharing clear, accurate data consistently to build trust over time. Address Specific Issues directly, as we did when a donor raised concerns about beneficiary duplication. We provided a consolidated, sector-wise sheet of beneficiaries and used conditional formatting in Excel to highlight duplicate CNICs. These actions ensure accountability and rebuild trust effectively.

1- Building trust in data starts with a solid foundation: training the stakeholders on the fundamentals of data Quality. What defines reliable and accurate data? The answer lies in aligning it with requirements. E.g, the data freshness—a critical factor—requires a clear, shared understanding. Does 'fresh' mean updated regularly but not in real-time, or does it imply high-frequency, near-instant updates? Defining this together is key. 2- The second pillar is data Lineage. Transparency is achieved by tracing datasets back to their raw sources and documenting each transformation step with precision. This approach not only highlights the quality and limitations of a dataset but also builds confidence to trust the data at the right level.