What do you do if technical teams and developers aren't understanding your data analysis findings?

I encourage open dialogue, inviting questions and feedback to identify specific areas of confusion. Additionally, I offer training sessions to help bridge knowledge gaps regarding analytical techniques or tools. Then, I revisit the presentation of my results, simplifying the explanation. Revisiting and simplifying my presentation ensures that my findings are communicated in a way that is both accessible and relevant to my audience. This strategy enhances the impact of my data analysis and supports better decision-making within the team.

Understand the technical background and expertise level of your audience. Tailor your explanations and insights accordingly to ensure they are comprehensible and relevant to them.

Utiliser le vocabulaire adapté au domaine des auditeurs Dans ce cas, il s'agit des développeurs et les équipes techniques. en un moment, ils sont du domaine de l'informatique. Ce qui les manque, c'est toute la théorie statistique derrière les résultats. Donc en présentant nos résultant en employant des termes techniques liés à leur domaine, ils peuvent mieux saisir le sens des résultats.

In my experience, I've encountered situations where using jargon inadvertently hindered effective data communication. During a presentation to stakeholders, I delved into technical terms and complex statistical methodologies without considering the audience's familiarity with them. As a result, I noticed confusion and disengagement among the listeners. Reflecting on this, I realized the importance of simplifying my language and providing clear explanations. In subsequent presentations, I made a conscious effort to avoid jargon and instead focused on using plain language and relatable examples to convey my message. This shift significantly improved the audience's understanding and engagement with the data.

Avoid jargon and technical terms that might be unfamiliar to your audience. Present your findings using clear and concise language that is easy to understand.

Your data analysis findings may go for a toss if your intended audience fails to understand you. So make sure you set up these few points. 1. Context - Before you present your findings, show them why and what you have been researching. Storyboarding is a great way to present the chronological process of your findings. 2. Prior Knowledge - Make sure the audience has a basic business understanding of the work that's going on so they can relate to your findings and also understand how you can empower their own work 3. Visualisation - Using simple & clean visualisations like charts and graphs of comparative data are often a very easy way to highlight your work to people who would otherwise ignore tabular or normal written reports.

Utilize visualizations such as charts, graphs, and diagrams to illustrate your data analysis findings effectively. Visual representations can often convey complex information more clearly and intuitively than written explanations alone.

Frame your data analysis findings within a narrative context that highlights the key insights and implications. Presenting your findings as a story can make them more engaging and memorable for your audience.

Encourage open communication and collaboration by inviting feedback and questions from your audience. Be receptive to their input and address any misunderstandings or concerns they may have.