How do you tell stakeholders when your analysis is uncertain?

It is every analyst’s dream to have clean data with 0% error. But that’s just what it is, a dream. When communicating an output, it helps to also speak about any measures you took such as omitting certain data that was too skewed and inconsistent, or inaccurate or if you selected an automated single source of truth to manage entry points of the data or if you included the data, to which percentage margin would the end results be affected. Errors do not mean the analyst is not credible because the input determines the output. And being open about it is the first step to gaining trust

When an analysis is especially uncertain I like to explain what steps we took to control for uncertainty. For example, many times the source of our data is human generated. Free-text responses, the decision to populate a field or not, outliers, etc. It can be as simple as leading with "What we've done here may seem like magic, but we're dealing with data from real people, and it's complicated. We've treated the data in the following ways..." You can express uncertainty in the results of your analysis while simultaneously expressing certainty in your process.

I agree with all of the contributors comments. I would add that I’ve always defined the parameters and criteria including what is and isn’t included and what would occur if it was. I also have laid out clear business assumptions those that are known and those that have a high likelihood of occurring. Being up front with what you’ve accounted for might help others provide additional insights to so sided as well.

A good point in this article is the ethical aspect. We must make sure all the stakeholders are aware of the uncertainty linked to the data. What we do need to consider is that we have different types of stakeholders with different knowledge. Our language should adapt accordingly. While some risks are relevant to share with certain stakeholders, other risks need to be detailed.

1. Sensitivity Analysis: - Test how changes in input variables affect results. - Highlight where results are robust and where they're sensitive. 2. Monte Carlo Simulation: - Model multiple scenarios by introducing random variations. - Showcase a range of possible outcomes and their likelihood

Communicate uncertainty by clearly stating limitations, assumptions, and potential risks in your analysis. Provide a range of possible outcomes, highlight data gaps, and offer recommendations for informed decision-making.

Remember that stakeholders manage risk all the time. Uncertain analyses can empower them to make strategic decisions. Deliver the top 2-3 possible outcomes. Show drivers of uncertainty as levers they can pull. Encourage thoughtful experimentation.

Uncertain analyses often suffer from poor translation back to the business context. The business will remain confident in us analysts despite uncertain results if we communicate well. Remember that stakeholders manage risk all the time. Uncertain analyses can empower them to make strategic decisions. Deliver the top 2-3 possible outcomes. Show drivers of uncertainty as levers they can pull. Encourage thoughtful experimentation. Uncertainty can also indicate the initial question wasn’t the right question. A strong analyst knows the right questions to ask when presented with a problem. When uncertainty is high, reconnect with the stakeholder to understand the decision they’re trying to make and confirm it’s aligned with what they requested.

Enhance uncertainty by refining data collection methods, increasing sample size, using advanced statistical techniques, and conducting sensitivity analyses to better quantify and address potential variations.