What do you do if you want to improve your data analysis by incorporating feedback from multiple sources?

I systematically gather input from colleagues and stakeholders to improve my data analysis by incorporating feedback from multiple sources. I evaluate and categorize the feedback to identify common themes and actionable insights. I then implement the most relevant suggestions into my analysis process. Regularly reviewing and iterating on this feedback ensures continuous improvement and more robust data analysis outcomes.

I would first like to understand the data I have, and business analysts would be able to help me understand it. BAs usually connect with stakeholders, so they might be able to help me understand domain-specific terms, which will be helpful in the further process. I will also consult with colleagues who are domain experts and have worked on similar projects. Additionally, online resources and books will be helpful. The only thing I need to take care of while referring to these is how this diverse solution is applicable to my problem. After consolidating all the steps, I will prioritize them based on the timeline commitment and resource availability.

O Feed back deve ser coletado de fontes diferenciadas. Obtê-lo de profissionais acima, abaixo e pares na hierarquia da empresa te dá uma ideia mais completa. Se necessário, pe?a também o feedback de alguém com quem conviva fora da empresa, pois pontos pessoais podem auxiliar na melhora profissional.

By strategically gathering feedback from various sources, ?????? ?????? ???????????? ???????? ???????? ???????????????? ????????????, ?????????????? ?????? ?????????????? ???? ???????? ????????, ?????? ???????????????????????? ?????????????? ???????? ???????????????????? ????????????????????????. Seek input from diverse stakeholders, including colleagues, clients, and domain experts. Consolidate feedback to identify common themes, strengths, and areas for improvement. Prioritize actionable feedback that aligns with your goals and objectives. Implement changes based on the feedback received and track the impact on your data analysis outcomes.

In the real world you can find any kind of valuable insight from any source. Though, I would argue that most commonly you will find the following pattern: - Managers and up: you can better understand complex business problems that give direction to projects. - Operational roles: you will be able to list concrete problems connected to the larger business problem, empowering the interpretation of data. - Field specialist: usually will highly empower the interpretation of data, sharing profound knowldge of causal relationships, important correlations, etc. - Online resources and peers in data: can help sharing technical knowledge on how to extract, transform and apply different techniques to generate insights from the data.

The first step is to evaluate the feedback you’ve received from various sources. Collect all the feedback in one place, whether it’s from colleagues, stakeholders, or clients. Review each piece of feedback critically, identifying common themes or recurring issues. Not all feedback will be equally valuable, so it’s important to prioritize the insights that are most relevant and actionable. For example, if multiple sources highlight the same issue with your data visualization techniques, that’s a clear sign that you need to address it. Use tools like Trello or Asana to organize and categorize feedback for better evaluation.

After research, we need to gather all the suggestions and information we have and based on our requirement we have to finalize the steps we will be taking to analyze our data. Some steps might be redundant so we need to assess the impact of each suggestion.

Categorize and prioritize the feedback: - Group the feedback into different categories (e.g., data quality, methodology, presentation, interpretation). - Prioritize the feedback based on its relevance, impact, and feasibility of implementation. Evaluate the feedback: - Carefully review each piece of feedback, considering its merits and potential implications. - Identify areas where the feedback aligns with your own findings or where it suggests improvements. - Assess the potential trade-offs and risks associated with implementing the feedback.

O feedback é quase como um processo de mentoria. Geralmente os mentores sabem um pouco mais que você pelo menos em algum aspecto sobre o qual ele opina. Portanto, o principal fator que precisamos avaliar quando recebemos um feedback, além da relevancia dele para o objetivo final que desejamos alcan?ar com a análise, é o nível de conhecimento, experiência e envolvimento que a fonte de feedback possui. Sem esses fatores, dificilmente o feedback contribuirá de forma efetiva para os objetivos a serem alcan?ados.

Once you’ve evaluated the feedback, the next step is to plan your changes. Develop a clear and detailed plan outlining how you will incorporate the feedback into your data analysis processes. Identify specific areas for improvement, such as data collection methods, analytical techniques, or presentation formats. Create a timeline and set measurable goals for each change you intend to implement. For example, if feedback suggests your reports are too technical, plan to simplify your language and use more visual aids. Tools like Microsoft Project or a simple Gantt chart can help you visualize and track your plan.

En este caso combinaría esta opinión haciéndola antes y después de recopilar opiniones. Considero importante tener un plan inicial propio para definir bien en qué quieres centrarte a la hora de la recopilación de información. Una vez consultados otros puntos de vista y basándote en tu plan inicial, modifica lo que consideres para acercarte mejor y más rápidos a tu objetivo.

With your plan in place, start implementing the necessary adjustments. This might involve learning new tools, adopting new methodologies, or refining your existing processes. Ensure you have the resources and support needed to make these changes effectively. For instance, if you need to improve your data visualization skills, consider taking an online course or attending a workshop. Implementing changes also means testing new approaches to see if they meet the feedback’s requirements. Use sandbox environments or pilot projects to test these adjustments before rolling them out fully.

After implementing the changes, it’s crucial to monitor the results. Track the impact of your adjustments on your data analysis outcomes. Are the insights clearer? Is the feedback from stakeholders more positive? Use metrics and KPIs to measure the effectiveness of the changes. For example, if one of your adjustments was to use more interactive dashboards, monitor how frequently these dashboards are accessed and how stakeholders engage with them. Regularly review these metrics to ensure that the changes are having the desired effect. Tools like Google Analytics or Tableau can be useful for monitoring and reporting on these results.

Finally, share the insights gained from your improved data analysis with all relevant parties. This not only demonstrates that you’ve taken their feedback seriously but also highlights the positive outcomes of the changes you’ve made. Create comprehensive reports or presentations that showcase the before-and-after scenarios, illustrating how the feedback-driven changes have enhanced your data analysis. Use clear visuals and concise explanations to communicate these improvements. Regularly update your stakeholders on the progress and continue to seek their feedback for ongoing improvement. Platforms like Slack or Microsoft Teams can facilitate continuous communication and feedback sharing.

Share those insights far and wide! Collaborate with cross-functional teams, present to leadership, and engage stakeholders. Gather diverse perspectives to refine your analysis and uncover hidden gems. Storytelling is key - use visuals, narratives, and plain language to make data accessible. Seek feedback, iterate, and share again. Build a reputation as a data rockstar by communicating insights that drive action.

Para enriquecer o processo de análise de dados, utilize ferramentas avan?adas como automa??o, machine learning e visualiza??es interativas, invista em aprendizado contínuo e colabore com outros profissionais para obter diferentes perspectivas. Foque na qualidade e governan?a dos dados, realize análises exploratórias detalhadas e alinhe os insights às necessidades dos stakeholders, comunicando-os de maneira eficaz. Adote uma abordagem iterativa, utilizando testes A/B para validar hipóteses, e siga princípios éticos e de privacidade. Compare suas práticas com padr?es do setor e aprenda com estudos de caso para aplicar melhores práticas e solu??es bem-sucedidas.

Esteja aberto a continuar coletando feedback regularmente e a ajustar sua abordagem de análise de dados conforme necessário. Cultive uma cultura de aprendizado contínuo e colabora??o, onde o feedback de múltiplas fontes seja valorizado e utilizado para impulsionar a melhoria contínua em sua prática de análise de dados.

The way that I will do is 1. Compile the data generated. 2. Use statistical presentation to put forth your thoughts 3. Present the options available as per your perception 4. Make it clear regarding the decision arrived at 5. Present it across functions 6. Assure them that feedbacks are welcome 7. Once feedbacks are received compile it 8. Work on the pros and cons of feedbacks 9. Take a bold decision based on your perception. This may help you.

A mi me ha resultado útil analizar datos desde la contribución que te pueda dar el mercado en conjunto con los clientes ya que te proporcionan insights de valor que permiten tomar decisiones estratégicas

Trend analysis and studying the variations help to put a strong case and increase the sample size to consider. It impacts the outcomes in a positive manner.

Gather feedback systematically from colleagues, stakeholders, and clients. Analyze the feedback to identify common themes and areas for improvement. Implement changes iteratively, testing their effectiveness. Regularly review and refine your analysis processes, fostering a culture of continuous improvement and open communication to enhance the quality and accuracy of your data analysis.