Balancing stakeholder expectations with data integrity requires a strategic approach. Start by communicating the importance of data integrity early, ensuring stakeholders understand its value and implications. Engage them in discussions about data standards to foster buy-in. Prioritize transparency by sharing insights from data audits, highlighting how maintaining standards benefits the organization. Be flexible while firmly adhering to established policies, showing responsiveness .

I have faced a similar situation before, where a stakeholder needed to meet a requirement that would compromise data integrity. I explained the consequences of this decision in the short term, including misguided decisions, rework, and loss of trust, resulting in higher costs and lower efficiency. In the long term, I highlighted the risks of losing credibility in the organization's data, difficulties in innovating and maintaining a competitive advantage, as well as impacts on the trust of partners and customers. With these arguments, the stakeholder understood the importance of preserving data integrity, and together we discussed a new alternative that would meet the requirement without compromising data quality.

It is essential to prioritize data protection and accuracy. It is important to establish clear data governance policies. Implement robust security measures: - Use access controls and authentication mechanisms - Encrypt sensitive data - Regularly update and patch systems. Maintain complete documentation and document any changes to data or systems. Perform regular audits and quality assurance checks. Educate stakeholders in a timely manner. Use version control and maintain backups of critical data. Establish a data ethics committee: - Create a group to review and approve data decisions. Implement data validation processes. - Use checksums or hash functions to verify data integrity. Promote a culture of data integrity. Develop a response plan.

Data integration is one of the most important steps of data science implementation process. In this step you alas a data scientist will validate, rescale and finally integrate data from different sources. If you do not do this step, you will not be able to well implement data science models. This process must be done carefully regarding to risk of missing data source within integration process. Following tips will decrease mentioned risk: - Having backup of different data sources - Data validation before integration to ensure final data have correct data type - Data rescaling before integration

I will prioritize transparency with stakeholders, clearly communicating the importance of data integrity and the potential risks of compromising it. By establishing a robust data governance framework, I will ensure that all data handling processes meet the highest standards of accuracy and consistency. This approach not only safeguards the data but also builds trust with stakeholders, demonstrating a commitment to maintaining the integrity of our data assets.