You're struggling to agree on data accuracy with your team. How can you bridge the gap in perspectives?

It's incredibly important to understand that combined teams may have individuals that bring different skills, requirements and related understanding. Given this, it is important to have a foundational discussion on understanding of data quality and how it affects accuracy of statistics and visualisations. This could extend to appreciating what level of accuracy is needed, data availability and limitations that need to be advertised along with the use of the best available data.

Para cerrar la brecha de perspectivas en la gestión de datos, es crucial alinear a todos los stakeholders en torno a una visión común y asegurar que todos comprendan la importancia y el uso de los datos: -Gobernanza de Datos: Establecer un comité de datos con representantes de diferentes departamentos -Comunicación: Reuniones mensuales para revisar los indicadores clave -Capacitación: Cursos regulares sobre el uso de herramientas de BI y la interpretación de datos. -Implementar Herramientas de Gestión de Datos: Implementar una plataforma de BI para todos los niveles de la organización. -Fomentar una Cultura Basada en Datos -Evaluar y Mejorar Continuamente: Incentivar el uso de reportes y dashboards en las reuniones de toma de decisiones.

If you can't agree on data accuracy with your team, the key is who "your team" is because it might be the wrong people. Ideally, you've already defined data governance. That means you've identified the people who will be involved from a cross-functional basis. From their different perspectives, they'll be able to come up with how to interpret data accuracy for the company as a whole. What is accurate for one function might be inaccurate to another function because of how the data is used, so they need to work together for the right definition of accuracy. It doesn't come from a single person or some isolated team.

Navigating differing perspectives on data accuracy can be challenging, but it's crucial for our team's success. Open communication and collaborative problem-solving are key. By fostering an environment where everyone can voice their concerns and ideas, we can align our standards and practices. Regular workshops and discussions will help us understand each other's viewpoints and work towards a unified approach. Together, we can ensure the highest levels of data accuracy and reliability.

To bridge the gap in perspectives on data accuracy, start by fostering an open dialogue. Encourage a safe space for all team members to express their views and concerns. Actively listen and ensure everyone’s voice is heard. Establish clear, agreed-upon criteria for data accuracy and document these standards. Review and validate data collaboratively, leveraging multiple sources. Implement regular audits and continuous training. If disagreements persist, consider external expert opinions or mediation.

Once the right communicative environment is established, focus on setting procedures for data entry, validation rules, and error-checking to ensure end-to-end understanding. Involve business stakeholders to ensure these standards support business processes, align with operational needs, and reduce subjective interpretations of 'accurate' data. Create and socialize clear documentation across the team, stakeholders, and end-users. Regular updates and training keep everyone aligned and aware of changes. Involving the business ensures practicality and relevance, enhancing processes, fostering a shared commitment to high data quality, and paving the way for sustainable governance processes to ensure continuous data quality improvements

Data accuracy is also about defining the same goals and governance with your stakeholders. However, accuracy is often influenced by the programs used and the schema provided. If you've already aligned with your stakeholders and still struggle to get accurate, timely data, you should check if perhaps there's a better setting with the current tools that better adapts to your business. Alternatively, you might need to look for some other program or schema that's more fitting for your specific business need.

Define the rules for data accuracy and utilize market-available tools for monitoring data quality. These low-code or no-code tools are user-friendly, enabling diverse team members to easily understand and use them. Consensus can be reached through data profiling and collaborative analysis.