Summary: Making mistakes as an analyst can be a common fear. It is important to develop strategies to minimize the risk of producing flawed outputs. Some strategies include setting a proper basis before starting an analysis, leveraging previous work to validate results, continuously sharing work-in-progress, and building an environment that minimizes errors. It is important to learn from mistakes and openly discuss them to improve the quality of work.
Making Mistakes as an Analyst — and Strategies to Deal with Them
Are you worried about making mistakes in your analysis? You’re not alone. Making faulty analyses that lead your organization in the wrong direction is a common fear among analysts. But avoiding analysis or shying away from controversial results is not the solution. Instead, it’s important to develop strategies to minimize the risk of producing flawed outputs. Here are some practical steps you can take:
Set a proper Basis before kicking off an Analysis
Before starting your analysis, align on the goal and approach with your team. Make sure you understand what the analysis aims to achieve and how you will get there. This will help you avoid misunderstandings and ensure everyone is on the same page.
Research if similar analyses have been conducted in the past. This can provide inspiration, validate your approach, and help you agree on deliverables with stakeholders.
Leverage previous Work to validate your Approach and Results
Check how others have used specific data tables and fields in previous analyses. This can give you confidence in your results and help you identify any potential errors or outliers.
Compare your results to previous analyses on the same or similar issues. Look for any big differences and try to understand the reasons behind them.
Continuously Share Work-in-Progress
Share your work-in-progress with stakeholders and peers to get early feedback. This will help you identify gaps and potential follow-up questions early on.
Anticipate questions and concerns that people might have when reading your results and recommendations. Address these in your analysis to provide clarity and build trust.
Build an Environment that minimizes Room for Errors
Create an open environment where work-in-progress can be openly shared and discussed. Encourage healthy discussions and collaboration among team members.
Implement tools and mechanisms that allow for sharing queries and code used in analyses. This helps reproduce results and learn from each other’s approaches.
Turning Mistakes into Successes
Accept that mistakes can happen despite all precautions. Learn from your mistakes and be open about them. Discuss errors and faulty results with your team to prevent them from happening again in the future.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Making and avoiding mistakes as an Analyst
- Towards Data Science – Medium
- Twitter – @itinaicom
