What do you do if your boss challenges your data analytics findings?

STAY CALM When your boss questions your data analytics findings, it's essential to remain calm and professional. Reacting defensively can create an adversarial relationship, whereas a composed approach fosters a collaborative environment. Understand that questioning is a part of the analytical process and use it as an opportunity to demonstrate the robustness of your work. Listen to the concerns raised, clarify any misunderstandings, and prepare to discuss your methodology and results in detail

Remember the "30-Second Rule" – take a deep breath and pause for 30 seconds before responding to criticism or challenges. This brief moment allows you to collect your thoughts, calm any immediate emotional reactions, and approach the situation more logically. Think of it as pressing a "reset" button on your initial defensive instincts, enabling a more productive dialogue.

If your boss questions your data analytics results, stay composed and receptive. Begin by clarifying how you arrived at your conclusions and where your data came from. Be ready to support your findings with evidence and be open to input or recommendations for additional investigation. Working together and being transparent are crucial in addressing such situations.

Stay Calm: It's natural to feel defensive when your work is questioned, but it's essential to remain calm and composed. Take a deep breath and approach the situation with a level head. Reacting emotionally can escalate the situation and hinder productive discussion.

Being a good listener would come in handy at this juncture. Once you have listened to the opinion/challenge, identify the top 3 activities you did to achieve your analytical findings, develop a rationale and justify the actions. Retrospection is also good thing to do, just to identify if the "other" thought process makes more sense, but only "if" it is logical. If it is not logical/based on certain logic, refute back saying, "sir/mam, would really appreciate your ideas/insights to be able to identify the outliers".

Apply the "Triple-Check Method" by reviewing your data once for accuracy, again for relevance, and a third time for comprehensibility. This meticulous approach not only ensures that your analysis is robust but also prepares you to defend your findings confidently, knowing you've minimized the chances of oversight.

Before presenting your findings, ensure your data is clean and your formulas, queries, pivots, etc. are accurate. If you're unsure, don't hesitate to seek assistance. If you're working in a team, have a colleague review the data with fresh eyes to catch any overlooked details. Once confident in your data, clearly communicate your process and findings to your boss.

Having someone else review your data is a smart move; they might catch things you missed after hours of working on a dashboard.. When someone challenges you, that is your cue to verify and cross check the report for accuracy. Extract fresh reports wherever possible and trace back your steps to ensure nothing is amiss. If it checks out, consider adding additional graphs or comments to support your analysis. More often than not, it is possibly the last missing piece to tie all your findings together.

Verify Data: Double-check your data and analysis to ensure accuracy. Review your methodology, assumptions, and any potential sources of error. If possible, gather additional data or conduct further analysis to address your boss's concerns. Presenting solid evidence and data-driven insights can help validate your findings.

Data verification is a necessary and compelling component of the entire data process. When data is collected, it is either primary or secondary data. Primary data is data collected from a source or sources and should be transformed into the desired state to achieve the purpose of the analyses. If the data is sourced from a published source it should be reviewed to ensure its seamless application. In any model or software application, the analyst has to ensure that the a priori assumptions are realized. If otherwise an explanation is deemed necessary as to why a different result was achieved. This could be that the data was either over-fitted or under-fitted for the analyses. This is why a mini monitoring and evaluation is necessary.

Clearly communicate your findings and methodology to your boss in a transparent and understandable manner. Break down complex concepts into simpler terms and provide context for your analysis. Be open to discussing your approach and willing to address any questions or concerns raised by your boss. It's crucial to maintain clear and open communication throughout the discussion.

Utilize the "ELI5" (Explain Like I'm 5) technique when breaking down complex data analytics concepts. This method forces you to strip away the complexity and convey the essence of your findings in simple terms, making it easier for non-specialists to grasp the value and implications of your work.

Tenha clareza na forma que você deve conduzir a apresentação ao seu interlocutor. Se ele não for uma pessoa técnica, leve explicações que sejam do dia a dia dele, que façam com que ele encontre similaridade, pois termos técnicos para uma pessoa não técnica faz com que você não tenha a atenção necessária. O inverso também deve ser considerado, se ele for técnico, sempre traga termos que sejam familiares e facilmente compreendidos.

Provide a clear and concise explanation of your analysis methodology, including any assumptions made and the rationale behind your findings. Use plain language and avoid jargon to ensure your boss understands your points.

If my senior challenges my data analytics findings, I see it as a chance for a little data duel! I'll grab my spreadsheet sword and PowerBI shield, dive back into the numbers, and prepare for a friendly tug of insights. Because hey, who doesn't love a good nerdy showdown over pie charts and bar graphs? :)

Adopt the "Evidence Pyramid" strategy, where you start with the most straightforward, direct evidence supporting your findings and then layer on more detailed, complex information as needed. This approach helps prevent overwhelming your audience initially, allowing them to gradually understand the depth of your analysis.

Presenting evidence that supports your findings is another effective way to reinforce your points. It would be helpful to create a brief "Idea board" at the end of your report, showing your boss the various angles you considered before arriving at the presented result.

I will explain the source of supporting data, the functionality of data collection, how we get results, and my technique for interpreting data with logic.

Back up your findings with solid evidence, such as data visualizations, statistical summaries, or relevant research studies. Presenting concrete evidence can help support your argument and build trust in your analysis.

Presenting evidence to support analysis is essential, emphasizing key points and considering additional details that influence the analysis, including outliers. Visualizing data enhances comprehension and provides a clearer picture. Additionally, incorporating drill-down analysis when necessary adds depth and clarity to the findings, ensuring a comprehensive understanding of the subject matter.

Engage in "Reflective Listening" by paraphrasing your boss's concerns or questions to confirm your understanding. This technique ensures you're addressing the actual issues at hand and demonstrates your attentiveness and respect for their viewpoint, fostering a more open and productive conversation.

Implement the "Co-Creation Framework" by inviting your boss to participate in the problem-solving process. This could involve brainstorming sessions, discussing alternative approaches, or jointly reviewing the data. Collaboration not only potentially yields better solutions but also enhances buy-in and support for the final outcome.

Collaborating will help both understand together and analyse to find an outcome which is thought through. This would save time.

If discrepancies persist, collaborate with your boss to find a solution. This may mean reanalyzing data together, seeking a second opinion, or revisiting the initial question. By working together, you show value for their input and aim for the best outcome, strengthening your relationship.

If I have to share my data analytics findings then I try my best to back my findings and insights with evidence, and leading actions with logical reasoning to avoid objective questions which can impact the impression on capability. Subjective and perspective questions will always come and those are open healthy discussions.

It is normal for stakeholders to challenge your findings, insights, or analysis. Perhaps something you found challenges long held beliefs about the operations of the company or a rule of thumb that your boss had. If the issue cannot be resolved during the meetings by following the advice others have put here, try restructuring your message using the EPIC framework. Empathy, Purpose, Insights, and Conversation. Understand where your boss is coming from and what might be at stake for them with regards to your analysis, clearly state the purpose of what you are communicating with your analysis, sythesize the main insights and support with evidence, then finally converse in a way your audience can relate to.