What do you do if your stakeholders have varying levels of data literacy as a data analyst?

Identifique o nível de letramento em dados de cada um e compreenda suas expectativas e objetivos. Realize entrevistas ou questionários para coletar informações sobre o conhecimento prévio e as preferências de cada stakeholder em relação à análise de dados. Isso ajuda a adaptar sua abordagem às necessidades específicas de cada grupo.

Generally simple is good. Ideally you can look at the graphics and understand the messages without too much explanation. If a lot of explanations are needed that generally points to an overloaded graph. Also make sure you do not create anything misleading, this will fire back at some point and is ethically questionable practice.

As a data analyst, when dealing with stakeholders who have varying levels of data literacy, first is to identify and understanding the varying levels of data literacy among my stakeholders; adequate the communication strategy to address these differences; use simple language and avoid jargon when explaining complex data concepts. When present data use visualizations like charts, graphs, and infographics in an easily format. Provide resources such as glossaries, guides, or tutorials to help stakeholders understand basic data concepts. Finally, create interactive dashboards that allow stakeholders to explore the data at their own pace and level of understanding.

Communicate with your Stakeholders to understand their needs and explain them the various options platform has to offer. They might be using different jargon based on their data understanding level but it's your responsibility to communicate effectively to meet their needs by all means.

Para aqueles com alta alfabetização em dados, relatórios detalhados com análises avançadas podem ser interessantes. Entretanto, quando este não é o cenário, talvez venha a necessidade de algo mais "clean". Em uma escala de níveis variados de alfabetização, vale até mesmo criar versões diferentes do mesmo relatório, cada uma projetada para se comunicar efetivamente com diferentes níveis de alfabetização. Sempre importante buscar esses detalhes nas trocas e pedir feedbacks para ajustar conforme necessário.

Interactive elements: Consider interactive dashboards or tools that allow stakeholders to explore data at their own pace and focus on areas relevant to them.

Customising reports and data visualisations is so important for ensuring insights actually get used by stakeholders. For less technical audiences, I focus on clean, intuitive charts that get the key points across without overwhelming details. More analytical teams appreciate seeing the full methodology and being able to dive into the underlying data. The key for me is to provide multiple levels of access to the stakeholders so they can make better and more detailed informed decisions.

Pela minha experiência, nem sempre o maior e mais detalhado relatório é o melhor. Entender os dados que são solicitados e separar o que é relevante do que pode apenas estar poluindo sua página é essencial, seja no PowerBI, no LookerStudio ou até mesmo em um excel. Antes de trabalhar em algo visual, é importante entender da sua fonte de dados quais informações são relevantes para tomada de decisões do stakeholder, o nível de alfabetização em dados e o objetivo da construção e sanitização desses dados, para que tudo seja feito de maneira direcionada e precisa.

Make sure to explain the basics of every graph (what is plotted on x and y axis) and avoid adding information which does not contribute to the message you want to get across. In doing so ensure ethical practice, e.g. don’t „create“ correlation where there is non by cropping your data.

Provide context: Always explain the "why" behind the data. What are the implications of the findings? How does it impact the business or decision-making process?

I make it a point to explain core data concepts, especially for stakeholders new to analytics. Using real-world analogies and examples can make KPIs and terminology click. It's also important to tie these concepts back to the specific business challenges we're solving.

As a data analyst, effectively communicating with stakeholders of varying data literacy levels involves tailoring your approach. For those with limited data knowledge, use simple language, focus on key takeaways, and employ clear visuals like charts and graphs. For moderately literate stakeholders, provide more context on methodologies and use enhanced visuals, while still avoiding jargon. For highly literate stakeholders, dive into technical details, share raw data, and discuss limitations transparently. In mixed groups, start with a high-level summary and progressively delve into details, offering additional resources for deeper understanding. This ensures clear, actionable insights for all.

Explicar conceitos é mais do que dar o significado, é trazer o motivo daquele dado, esclarecendo seu contexto e relevância. Isso potencializa a eficácia das decisões. Importante a clareza e a objetividade, especialmente para um stakeholder novo na análise de dados, evitando explicações muito técnicas e sem relevância para a tomada de decisão.

Building data literacy is valuable for any organisation. I try to host training sessions every now and then on topics like dashboard best practices or analysing data. We also have an internal documentation with detailed notes and video tutorials stakeholders can reference. Empowering people to work with data directly leads to better insights.

Encourajar perguntas é importante para garantir que todos os stakeholders compreendam os dados apresentados. Crie um ambiente onde os stakeholders se sintam confortáveis para fazer perguntas e buscar esclarecimentos. Isso não só melhora a compreensão, mas também fortalece a confiança entre você e os stakeholders.

Realizar acompanhamento é essencial para garantir que os stakeholders estão interpretando os dados corretamente e usando-os efetivamente. Agende reuniões de acompanhamento para revisar os dados e discutir quaisquer dúvidas ou preocupações. Este acompanhamento contínuo ajuda a manter todos alinhados e a garantir que os dados estejam sendo utilizados de forma eficaz.

Be transparent and honest: Explain the limitations of data and potential biases. Avoid oversimplifying or exaggerating results. Involve stakeholders in the process: When possible, involve stakeholders in the data analysis process. This can increase their understanding and buy-in for the findings. Celebrate successes: Highlight instances where data-driven insights led to positive outcomes. This reinforces the value of data literacy and encourages further engagement.

Identify who will be the main decision-maker among all the stakeholders and craft your presentation with that person customizing the level of detail and analysis to present.

I usually have two presentations on each topic as a best practice: one is for presentation during the meeting, this includes mostly the graphs and graphics. The second one I share after the meeting includes texts that explain the concepts and deliver the messages from the analysis. This way the material can be shared internally or externally (e.g. with clients) or forwarded to management allowing for scalability of message transportation.