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What do you do if your feedback in data engineering is causing more harm than good?

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Navigating the complex world of data engineering requires not only technical skills but also the ability to give and receive feedback effectively. Imagine you're a data engineer or a manager overseeing data projects, and you've noticed that your feedback, despite being well-intentioned, is leading to confusion, demotivation, or even conflict within your team. It's a tough spot to be in, but it's not uncommon. The key is to recognize the signs early and take steps to ensure your feedback fosters improvement and collaboration, rather than hindering progress.

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Ivan de Castro

boosting revenue & efficiency with AI-driven decisions
Rhayar Mascarello

Senior Data Engineer | Generative AI Engineer | Azure Solutions Architect Expert | Databricks

1 Assess Impact

When you realize your feedback might be causing issues, take a step back and assess its impact objectively. Consider if the feedback is clear and actionable or if it's too vague, causing misunderstandings. Reflect on whether it's aligned with the team's goals and if it's delivered in a way that's constructive, not critical. Sometimes, the problem lies not in what you're saying but how you're saying it. Adjusting your approach can make a world of difference in how your feedback is received and acted upon.

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Agathamudi Leela Vara Prasad

Microsoft Certified Azure Data Engineer(DP-203) | Python | SQL | Big Data |Azure Data Factory | Azure Databricks | Spark-SQL | ADLS | Pyspark | ETL | Hadoop | Hive | PowerBI
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When giving feedback, make sure it is clear, respectful, and helpful. Instead of just pointing out problems, focus on providing constructive and specific solutions. Each person learns differently, so customize your feedback to meet individual needs.

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2 Open Dialogue

Creating an open dialogue is crucial when addressing feedback-related issues. Encourage your team to share their perceptions and experiences with the feedback they're receiving. This can be done through one-on-one meetings or anonymous surveys. Listen actively to their concerns and be prepared to make changes based on their input. This collaborative approach not only helps in refining the feedback process but also builds trust and shows that you value their professional development.

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Agathamudi Leela Vara Prasad

Microsoft Certified Azure Data Engineer(DP-203) | Python | SQL | Big Data |Azure Data Factory | Azure Databricks | Spark-SQL | ADLS | Pyspark | ETL | Hadoop | Hive | PowerBI
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Promote open communication and be responsive to concerns. Value feedback from others and use it to enhance your methods consistently. Remember, feedback is a valuable tool for personal development rather than a cause of frustration or discouragement.

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3 Refine Methods

After gathering insights from your team, it's time to refine your feedback methods. If you've been providing feedback only through email, consider adding face-to-face conversations or video calls to convey tone and body language more effectively. If your feedback has been too broad, work on being more specific by using examples or data points. Tailoring your feedback delivery to suit individual team members can also be beneficial, as different people may respond better to different communication styles.

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4 Educate Team

Educating your team about the purpose and value of constructive feedback is essential. Explain that it's a tool for growth, not a means for criticism. Provide guidance on how they can use feedback to advance their skills and projects. You might also want to offer training sessions on giving and receiving feedback effectively. When everyone understands the importance of feedback and how to communicate it properly, the entire team can move forward more cohesively.

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Rhayar Mascarello

Senior Data Engineer | Generative AI Engineer | Azure Solutions Architect Expert | Databricks
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Instead of giving vague or overly critical feedback, pinpoint exact issues and suggest clear steps for improvement. For instance, if a data model isn't performing as expected, discuss specific aspects that could be optimized, such as index usage or query structure.

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5 Implement Changes

With new strategies in place, it's time to implement changes in how you provide feedback. Start by setting clear expectations and providing a framework for how feedback should be given and received within the team. Monitor the effects of these changes and be willing to adjust your approach as needed. Remember that improvement is an ongoing process, and staying flexible is key to finding what works best for your team's dynamic.

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6 Follow Up

Finally, follow up on the changes you've made to ensure they're having the desired effect. This doesn't mean just checking in once; make it a regular part of your routine. Ask for feedback on your feedback—yes, it might sound meta, but it's a valuable way of ensuring continuous improvement. Recognize and celebrate when feedback leads to positive outcomes, and always keep the lines of communication open for further refinement.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Ivan de Castro

boosting revenue & efficiency with AI-driven decisions
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Feedback is a great opportunity to share, but not everybody is ready to receive some + handle it. Firstly, consider right place, time, setting and your objective of providing the feedback. Then try to make it as objective as possible referring on how you felt by certain behaviors with adequate examples.

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Atiqur Rahman ACMA, MBA(IBA)DU, CERM

Finance professional, Treasury Manager
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If feedback in data engineering is causing harm, reassess and clarify communication. Engage in open discussions, adjust your approach to be constructive, and focus on actionable solutions. Foster collaboration, offer training to address gaps, and establish regular feedback mechanisms. Aim to improve understanding and growth rather than discouraging team members.

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What do you do if your feedback in data engineering is causing more harm than good?

1

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3

4

5

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7

1 Assess Impact

2 Open Dialogue

3 Refine Methods

4 Educate Team

5 Implement Changes

6 Follow Up

7 Here’s what else to consider

Data Engineering

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What do you do if your feedback in data engineering is causing more harm than good?

1

2

3

4

5

6

7

1 Assess Impact

2 Open Dialogue

3 Refine Methods

4 Educate Team

5 Implement Changes

6 Follow Up

7 Here’s what else to consider

Data Engineering

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