You're facing resistance from a team member on data accuracy. How important is it to address quality checks?

Una adecuada gestión de la calidad de los datos requiere un marco de gonernanza de datos para asegurar la alineación de todos los involucrados con las reglas y estándares de calidad de datos de la organización y así a través de un esfuerzo permanente y sostenido garantizar que los datos cumplan con los requerimientos.

No solo es importante generar una estrategia de limpieza de datos previo al análisis, sino que además resguardar una serie de criterios de validez y calidad que surgen desde la investigación científica. Una vez cumpliendo estos checks, es necesario realizar "triangulaciones", ya sea metodológica y de fuentes de información, con el objetivo de robustecer la solidez y credibilidad de los resultados.

Maintaining data quality is crucial because the output of your analyses is reflective of the quality of the input. This is similar to your diet, your body is only as good as what you eat, as 80% of your body’s physique is dependent on your consumption habits. Steps to check for data quality issues 1. Ensure accurate data types for each column (character type for text-based entries, numeric type for quantitative information, etc) 2. Perform EDA to find abnormal data entries (i.e. finding values above 1 for percentage-based columns) 3. Compare column values to see if they make sense (i.e. we should not have more clicks than impressions per ad placement) 4. Check join conditions (joining tables on a 1:1 relationship to prevent duplication)

Addressing data accuracy is critical, especially when facing resistance from a team member. Ensuring data integrity is a fundamental step in any statistical analysis because flawed data leads to questionable insights. It's essential to communicate that data integrity affects the entire team's work. Think of it like building a house—if the foundation isn't solid, everything built on top of it is at risk of collapsing. Quality checks are the essential checkpoints that ensure your data foundation is strong, enabling reliable and accurate analysis.

Importance of addressing quality checks for data accuracy: Ensures reliable decision-making by providing accurate data for informed choices. Maintains credibility and trust within the team and with stakeholders. Prevents costly errors by catching inaccuracies early before they lead to significant issues. Supports compliance with regulatory and industry standards, avoiding potential penalties. Facilitates effective analysis by ensuring data integrity for accurate insights. Improves team efficiency by reducing the need for rework and corrections. Enhances customer satisfaction by delivering consistent and reliable results. Strengthens team alignment around shared standards for data quality. .

Data quality is important for credibility between different teams within an organization. If the analyses, models, and recommendations are based on faulty data, then this could negatively impact the next course of actions. Therefore, it would be difficult to trust the data analysts/scientists to help drive the company’s critical decisions. Credibility is very important for data professionals, as they are tasked to answer some of the toughest questions for their companies. Making the wrong decision could cost millions. Be mindful of the quality of the data, because people tend to trust others based on the quality of their output.

Addressing the resistance head-on was important for maintaining a cohesive team. I opened a dialogue, acknowledging their concerns while explaining the broader impact of data quality on our project's success. This approach helped reduce tension and fostered a more collaborative atmosphere.

In a project management team, the communication circuit on data or other aspects must be clearly defined in a participatory manner. For reasons of protection of personal data collected on the project, authorization levels can be established, while taking into account the level of involvement of each team member. This mechanism will facilitate monitoring of access and use of data by the team member who has accessed it. But refusing access to information is a practice that destabilizes the cohesion of the group and we witness the composition of subgroups. However, cohesion, the effective distribution of roles and the continuous strengthening of the skills of team members are pillars which underpin the success of a project.

Data quality is crucial: - Accurate data drives better decisions - Poor quality undermines credibility - Prioritize quality checks to save time - Address resistance through collaboration

If someone on the team doubts data accuracy, I believe it’s crucial to lean into our quality protocols. These checks are more than just routine—they’re what keep our work credible. I’ve learned that prioritizing these protocols helps us all stay aligned and ensures we deliver the best possible outcomes.

I demonstrated the value of quality protocols by walking the team member through our data validation processes. By showing how these checks prevent errors and ensure reliability, they began to see the necessity of these steps.

Data validation is essential for ensuring the accuracy and reliability of your analysis by verifying that the data is correct and appropriate for the intended purpose. When facing resistance to validation processes, it’s important to highlight that these checks prevent costly mistakes and inefficiencies by catching errors early, thereby saving time and resources. Additionally, emphasize that proper data validation helps maintain compliance with regulatory standards and protects against legal and reputational risks, reinforcing its role as a critical step rather than a bureaucratic hurdle.

I invited the team member to participate in the data validation process, giving them a hands-on experience. This not only increased their understanding but also gave them a sense of ownership over the data's accuracy.

Its an interesting and important step before you go for data analysis or any decision-making step. Some common practices in data validation I have played around like range checks, format checks, confirming that data across different sources align correctly and many more steps. Its also always great to have your team do it too.

I used this experience to refine our quality checks, making them more efficient. Involving the entire team in this process created a feedback loop that continuously improved our data handling practices.

I encouraged regular feedback sessions where team members could voice concerns or suggest improvements. This not only addressed the initial resistance but also ensured that everyone felt heard and valued in the process.

Quality data reviews are crucial and play a vital role in ensuring the accuracy and reliability of information. High-quality data aids in making precise and confident decisions, while inaccurate data can lead to erroneous decisions and serious issues. Here are some reasons why quality data reviews are important: 1. **Accuracy and Precision**: Accurate and precise data contribute to better decision-making and reduce errors. 2. **Trust**: High-quality data increases the trust of users and customers. 3. **Operational Efficiency**: Quality data reviews help reduce costs and enhance operational efficiency. 4. **Problem Prevention**: Identifying and correcting data issues early prevents larger problems down the line.