How can you guarantee fair, objective analysis?

Let’s be clear this is the role of an analyst at its core. Our responsibility is to provide an unbiased, data driven outcome to our clients. This does not mean you shouldn’t lean into your experience (e.g.; past analytical work) to shape your hypotheses but, ultimately, it’s the data that drives your results.

Define clear, goal-aligned objectives. Establish transparent evaluation criteria in advance. Avoid bias in data collection by using diverse sources. Employ statistical rigor for objective data analysis. Engage diverse stakeholders for a comprehensive view. Encourage questioning and critical thinking to challenge assumptions. Document the analysis process transparently for accountability. Include peer reviews to provide independent perspectives. Ensure consistent application of criteria across the analysis. Be mindful of ethical considerations to prevent conflicts of interest. Continuously learn about best practices for unbiased analysis. Encourage stakeholder feedback for ongoing refinement of analysis methods.

In my experience I find this is a crucial step given that data analysis can very wide. Defining the scope and knowing the objectives will allow you to focus your efforts on relevant variables to feed into the model. Knowing the assumptions will allow you to be transparent about the model’s weaknesses.

Treat your assumptions like hypotheses in your scope, not as premises. Try not to cling to them to prove them true or conduct your analysis as if they are true. If your assumptions are proven false, you still gain valuable, actionable insights.

- make sure your source tables are generating accurate information - make sure that all factors are included in the analysis - if possible verify the information by spot checking data

I agree, the source of your data is the most important part. If you start your analysis with invalid or incomplete data it will be a waste of time.

Don’t blindly trust the data. If something looks off, go back to the data collection methodology and trace the line from user to data set that the point travelled. You may just find that there is a tag misfiring and double counting conversions, or perhaps there is a conversion action that is unaccounted for.

The assurance of accurate, reliable insights, and conclusions hinges upon the meticulous maintenance of high-quality data, free from data entry errors, missing or incomplete information, and inconsistent formatting.

Data changes over time. Times are changing, always. Changes in data over changing times means you are responsible to bring meaningful data for analysis, or be certain data cannot be brought forward with meaning. Censored data, many-to-one and one-to-many code value occurrences, starts, stops, system conversions, versions and standards changes, and drop ins and out segment definitions are more difficult to sleuth than simple regular errors. Identity and entity identifiers are the hardest, but key - literally, match keys for multi-source and cross validation across data structures. Without good data, nothing else matters.

Be sure to outline in the scope of your analysis the “why” behind your methods and tools choices. That way, your methodology is easily defensible when presenting to stakeholders. Consider alternative tools and methodologies and note the reasons you chose to set them aside.

Data in itself is objective, but I would argue that it's not useful until you use it in a subjective manner. And although the analyst's role is to provide unbiased, accurate information, you cannot apply it to make a business decision until you've applied other knowledge to build a story.

In addition to above points, consider following Consider Multiple Perspectives: Seek input from various stakeholders to incorporate diverse viewpoints, preventing undue influence from any one source. Involve Others in Review: Encourage peer review or involve colleagues in the analysis process to gain additional perspectives and identify potential biases. Communicate Clearly:Clearly communicate findings, uncertainties, and limitations. Transparency in reporting helps stakeholders understand the basis of your analysis and any potential areas of uncertainty. Stay Informed: Continuously update your knowledge and skills to stay informed about industry trends and best practices, ensuring your analysis remains relevant and unbiased.