How do you handle questions and feedback from your data analysis audience?

If the presentation is important and I am looking for a specific outcome, I would identify key people in the audience and run a summary past them beforehand. That way I can gauge the response, identify any pitfalls and either make changes or just ensure I’m prepared for the questions. Sometimes I’ve even identified the most tricky stakeholder and approach them first. That way I know their main complaints and can prepare accordingly. This may be more relevant to the consulting world.

In my experience, this part of the article is crucial. The content of the presentation has to be relatable to the audience and or prepare the audience for the rest of your data by giving a brief background or running through the methods of how you came to the results for data analysis. Hence, your audience is on the same page.

I would suggest answering these questions prior to even doing your data analysis. Rare that you should be doing data analysis without having some goal or expected outcome of the analysis and knowing the intended audience will help you to know how deeply you need to dive into the data. Always go at least one step beyond what you think is expected so you can dive deeper when asked.

Addressing queries promptly and offering insightful responses can enhance the overall communication process with your audience.

Crafting a story is one of the most important parts of creating a bridge between your audience and the information you want to deliver. Humans are wired to understand and absorb stories from childhood and you should use data to highlight your story instead of the other way around. In the end, the audience is most likely to remember a well crafted story rather than a collection of charts and numbers.

Saber quem é seu público e ter uma ideia clara de quais conceitos e aspectos s?o possíveis ser abordados, exemplificados e até definidos, é primordial nesse quesito. Quando existe a possibilidade de questionar as pessoas participantes quais seus interesses, se torna mais fácil. No entanto, existem situa??es em que o público é desconhecido, ent?o nesse momento, quanto mais simples e clara for a apresenta??o dos dados e insights, mais acessível será. é importante ainda fixar o objetivo principal da apresenta??o, para que n?o sobre dúvidas do que foi explicado.

It can be helpful to outline the scope of the analysis up front so that the questions that stray from the topic are framed against the presentation content rather than paired with it. There are a lot of ways to say "we didn't look at that specifically, but that's a great question." Building up the confidence of your audience, especially those audiences that are uncomfortable engaging with analytics, is part of answering questions confidently.

The most important thing is to remain humble. Data analysis is both science and art, and the science is constantly being re-invented. No matter how strong your model is, it remains an approximation of a reality that you will never grasp in its entirety. Make sure that your audience understands that fact as well. Do the work, be confident, but know the limits of your craft. Last but not least, come prepared with confidence intervals that will support your recommendations.

Be prepared to have your findings and the underlying data questioned especially if they are contradictory to expectations and/or popular belief.

Don’t be defensive or protective, questions are great engagement and an opportunity challenge our thinking with diverse thoughts, especially if it’s a question you haven’t been able to predict.

This is a very simple, but important bit of advice that I've found really benefits in designing or improving the existing design of new analytical tools/reports/dashboards: When asking for feedback, make sure you frame this in the sense of 'what are the outcomes you want to achieve?' as opposed to 'what technical features do you want?'. Sometimes stakeholders want specific visualisations because they think that is the best way to deliver an outcome, but as data analysts a big part of our work is demonstrating how stakeholders can get an outcome in the best way. Bottom-line: sometimes stakeholders are not always aware of the range of capabilities available, but they know generally know what outcomes they want from the analysis.

Emotional Intelligence (EI) is also crucial when handling questions and responding to feedback from stakeholders, because it is a key factor that can help professionals communicate effectively, resolve differences, and collaborate well to achieve project outcomes. Data analysts often deal with dissatisfied stakeholders that express feelings of frustration related to unmet expectations, be it project deliverables or misunderstandings caused by poor communication. The ability to manage one's emotions and that of others can play a vital role in becoming a mature analytic professional. Improved communication skills would be a key outcome of applying EI at work which can make us a valuable member of our team and organization.