You're at odds with stakeholders over data validation in Data Warehousing. How do you find common ground?

Early and frequent stakeholder engagement is essential to ensure that data projects meet their needs Identify all relevant stakeholders and collaboratively define data priorities Frame a comprehensive data strategy based on the collaboration to provide value Maintain consistent communication throughout the data collection, analysis and reporting process Regularly validate findings while prioritising the use of collected data together Provide updates and demonstrate how stakeholder input is being used to foster alignment and trust Allow flexibility to adjust based on ongoing feedback Design data solutions that are not just insightful but actionable Ensure that the insights drive meaningful outcomes by refining methodologies

When there's disagreement about data checks in a warehouse project, I try to listen first and understand the real concern. I explain how small errors in data can lead to big business mistakes. To balance speed and accuracy, I suggest focusing checks on the most important numbers first. I also try to show examples where bad data caused trouble in the past. If possible, I use simple tools to make these checks part of our regular process, so they don’t slow things down. In the end, I believe it’s about making sure the team can trust the numbers without delaying the project.

first of all listen to your stakeholder who are consumers of your warehouse outputs, In reality, your warehouse will be as clean as your OLTP , you can't change the truth that is being maintained in your transactional DB , all you need to ensure strong data integrity in your transactional database , if that is ensured,& you have a solid reconciliation mechanism is place between OLTP and OLAP, all left for you at warehouse end is to interpret your OLTP data correctly.

If stakeholders push back on data validation within the data warehouse, then the key is finding common ground based on understanding where they are coming from. I would listen to what they are worried about; generally time or complexity. I would connect data validation back to their business outcomes; explaining how it prevents mistakes in their reports and analysis, and ensures the trust placed in warehouse data. We jointly prioritize validation rules based on business significance of data. This reframes the priority from perceived inconvenience to shared risk avoidance and ensuring the warehouse never compromises their choices.

Engage your stakeholders - early as well as frequently Data Accuracy: Implement tiered validation controls that scale with data criticality. This ranges from simple integrity checks (headers/footers, row counts) to more sophisticated cross-system reconciliation for mission-critical data. Have clear ownership and escalation paths defined when discrepancies arise Process Efficiency: Automate data validation. While Excel remains prevalent in most organizations, leverage fit-for-purpose automation tools—whether Python scripts, orchestration platforms, or purpose-built validation frameworks—to reduce reconciliation cycles by 70-80% while improving coverage

I listen to stakeholders' concerns to understand their priorities—speed, accuracy, or usability. Then I explain how data validation ensures trust and reduces costly errors. I propose a compromise: validate critical fields first, automate checks where possible, and phase in deeper rules later. This balances their goals with data quality and builds trust through quick wins.

I’ve found that the fastest way to align with stakeholders is to show how unchecked data can directly impact their decisions. Instead of aiming for perfection, I prioritize validation on high-impact fields and automate wherever possible. This builds trust without stalling progress, a win for both accuracy and agility.

Inaccurate data leads to errors in reports, decision making Invalid or inconsistent data can compromise the overall integrity of database or system Failing to validate data can result in non-compliance, which may lead to penalties, legal issues, reputational damage Validated data provides a solid foundation for generating meaningful insights and making data-driven decisions Data validation is a mindset which instills confidence in every stakeholder who relies on the data Data validation fuels innovation and builds resilience in the face of ever-evolving challenges Beyond preventing errors, data validation plays a crucial role in uncovering hidden issues and areas of improvement Create a culture that prioritizes data validation

According to Gartner's most recent estimates, poor data quality costs organizations nearly 13 million dollars annually High quality data breaks down silos, ensures latest, most relevant information is readily available With rich insights into target buyers, organizations can sell products, services more effectively, optimize budgets and plan better campaigns With a single source of truth across the organization, employees can stay on the same page, remain aligned, focus on a common goal Organisations with high quality data can make tough choices with confidence Compliance risks, conflicting information hinder collaboration, incur penalties With incorrect inputs businesses may find themselves investing in unprofitable projects

People usually underestimate the complexity of a data project until they are into one. So having patience and explaining what is going on and why it is happening is key

To resolve conflicts over data validation in data warehousing, offering practical solutions fosters collaboration: Tech-Driven Enhancements: Introduce automation tools to streamline validation while maintaining accuracy. Flexible Workflow Adjustments: Modify processes to accommodate stakeholder concerns without compromising data integrity. Revised Validation Criteria: Refine standards based on real-world usage, ensuring relevance and efficiency. Cross-Team Collaboration: Engage stakeholders in discussions to align expectations and find common ground. Proactive Innovation: Demonstrate adaptability in evolving data practices, turning resistance into opportunity. This ensures a structured yet adaptable approach.

Data integrity is non-negotiable as it is not confined to a single tool or platform It is similar to quality control in traditional product-oriented businesses Beyond decision making, data integrity is crucial for protecting personal and sensitive information of data subjects Maintaining data integrity is not merely a compliance or operational issue. It is infact a strategic imperative which impacts every facet of an organization's relationship with customers Data integrity is the overarching principle that includes data quality, security elements. It ensures that data is protected against unauthorised tampering Types of data integrity Entity Physical Referential Domain User-defined Data integrity helps promote completeness

When you’re at odds with stakeholders over data validation in data warehousing, the key is to prioritize together. Collaboratively identify the aspects of validation that are essential and those that have more flexibility. This approach ensures that critical data quality standards are maintained while considering stakeholders' needs. By working together to set priorities, you foster a sense of partnership and shared responsibility, which is key to aligning objectives and achieving the best possible outcome.

Deloitte describes Data Trust as, "Bridging the gap between knowing and doing" The most crucial aspect of the data trust program is convincing knowledge workers that they can trust AI-based tools Building trust in company data from the outset is more effective and affordable than attempting to restore data trust once it has been lost Data trust is a competitive advantage that benefits stakeholders Engage stakeholders in data governance procedures by implementing feedback loops for internal and external data consumers Understanding the importance of data trust is the first step in implementing a program to build trust A new generation of products intended to bridge the data trust gap: Fivetran Metadata API dbt Lab's Semantic Layer

To build trust and resolve conflicts over data validation in data warehousing: Transparency: Clearly outline validation processes and decision-making criteria. Regular Updates: Keep stakeholders informed to ensure alignment and ease concerns. Active Listening: Acknowledge feedback and adjust strategies based on stakeholder input. Collaborative Problem-Solving: Engage all parties in refining validation methods together. Consistency & Reliability: Demonstrate dependable data practices to reinforce confidence. This fosters cooperation while ensuring accurate validation.

To compromise wisely in data validation disputes: Define Non-Negotiables: Establish core data integrity principles that must remain intact. Flex on Execution Methods: Adjust workflows to align with stakeholder needs while maintaining accuracy. Collaborate on Standards: Co-develop validation criteria that balance business agility with precision. Iterative Refinement: Implement phased adjustments, ensuring alignment before full-scale changes. Mutual Benefit Framing: Highlight how compromises enhance efficiency while preserving reliability. This approach fosters trust and keeps data integrity intact.

Navigating conflicts with stakeholders over data validation in data warehousing can be tricky, but finding common ground is essential for smooth project execution. The key is to compromise wisely. Striking the right balance between maintaining data integrity and ensuring business agility is crucial. While it’s important to uphold the core principles of accurate and reliable data, sometimes flexibility is needed to meet deadlines or adapt to changing business needs. Open communication and a willingness to listen to all perspectives can help bridge the gap, leading to a compromise that keeps both technical and business objectives in harmony.