What are the top 5 data visualization skills for Business Intelligence (BI) professionals?

As per my experience thes are the core skills for BI Professionals. Data Interpretation Tool Proficiency (Tableau, Power BI) Storytelling with Data Stakeholder Needs Understanding Data Accuracy & Integrity

Another critical skill for Business Intelligence is understanding the business context in which we operate. This skill is essential for aligning with the intended organizational measurements, objectives, and, of course, the decision-making process. Additionally, the ability to communicate technical terms to non-technical stakeholders is crucial. Beyond analyzing data, translating complex insights into clear and understandable information facilitates effective collaboration and informed decision-making.

Business Intelligence is a business oriented aspect of data analytics wherein the data acquired by the business is converted into actionable insights so that the business can become more profitable. Business Intelligence can have many facets including analyzing different types of data that are generated by the business and then present the insights gained from the data to decision-makers in the business using presentations, reports, summaries, dashboards, graphs, etc. Data visualization also plays an important role in business intelligence, wherein different graphs and charts can be used to graphically present the data insights. Data Preparation. Descriptive Analysis. Business/Domain Knowledge

Estábamos en un proyecto de análisis de datos para una empresa, y los jefes, aunque sabían del negocio, patinaron al interpretar algunos indicadores. No vieron bien las métricas y los datos, y eso pudo haber metido la pata en decisiones clave. Después de eso, nos dimos cuenta de que necesitábamos ponerle más enfoque a la alfabetización de datos. Armamos unas charlas, tiramos unos conceptos clave y les dijimos a todos que hay que ver los datos con ojo crítico. La moraleja es clara: no solo los nerds de los datos necesitan entender, también los que toman decisiones basadas en esos números. Desde ese día, nos pusimos las pilas para que todos en la empresa sepan bien qué onda con los datos y evitemos malentendidos.

Top 5 data visualization skills for business intelligence professionals: Data Interpretation: Analyze and interpret complex data for meaningful insights. Tool Proficiency: Master data visualization tools like Tableau or Power BI. Design Aesthetics: Create visually appealing and easily comprehensible visualizations. Storytelling: Present data in a narrative format for informed decision-making. Domain Knowledge: Contextualize visualizations with industry-specific knowledge. These skills collectively empower business intelligence professionals to effectively communicate insights, drive data-driven decision-making, and contribute to the overall success of the organization.

En mi experiencia, al aplicar estos principios en un proyecto de análisis, logramos transformar datos complejos en información accesibles. Utilizando principios como la elección adecuada de gráficos y la reducción del desorden, creamos visualizaciones que destacaron claramente las tendencias clave. Esto resultó crucial al presentar los hallazgos a stakeholders no técnicos, mejorando la comprensión y la toma de decisiones. La aplicación consciente de principios de diseño no solo optimiza la comunicación de información, sino que también mejora la experiencia del usuario, lo que se traduce en un impacto más significativo en la implementación de estrategias basadas en datos.

Edward Tufte es el creador de la ecuación -tinta, el cual dice que evitemos usar adornos o distractores en nuestro gráfico o visualización, como por ejemplo, un gráfico de barras 📊 en 3D, o un gráfico de línea 📈 con líneas detrás (innecesarias).

I find that having some design principles understanding can help to make your visualization more attractive and effective. For instance beside the page layout, knowing which colors go well together and how to use them can make your visualizations more appealing. Also, knowing when to use different types of charts or graphs helps in showing the data in the best possible way for people to understand it easily.

Data visualization is not just about throwing charts and graphs on a screen; it's about using those elements to guide viewers through a journey, highlight key insights, and evoke emotions that drive action.

In my experience, storytelling is very important for business intelligence professionals. A good storyteller can explain complex data in engaging ways. For example, when I narrate how data trends affect business outcomes, this makes insights clearer for decision-makers which leads to better-informed choices and support for data-driven strategies.

O pensamento crítico pode trazer vários benefícios, como aumentar a credibilidade e a confiança na sua visualização de dados, melhorar a qualidade e a eficácia da sua comunicação e argumentação, estimular a criatividade e a inovação e promover a aprendizagem e o desenvolvimento contínuos. O pensamento crítico também pode apresentar alguns desafios, como exigir tempo e esforço, envolver múltiplos critérios e perspectivas, lidar com a complexidade e a ambiguidade dos dados e reconhecer os vieses e as falácias do seu próprio pensamento.

A colaboração pode trazer vários benefícios, como aumentar a diversidade e a qualidade das suas visualizações de dados, melhorar a eficiência e a produtividade do seu trabalho, ampliar o alcance e o impacto da sua comunicação e fortalecer o engajamento e a confiança dos seus parceiros e clientes. A colaboração também pode apresentar alguns desafios, como exigir habilidades de comunicação e negociação, envolver diferentes expectativas e interesses, lidar com conflitos e divergências e respeitar a propriedade e a privacidade dos dados.

-Specific data visualization tools and platforms (Tableau, Power BI, etc.). -Data wrangling and data cleaning techniques. -Data storytelling with tools like Prezi or Keynote. -Presenting data to diverse audiences. -Staying updated with the latest trends in data visualization