Here's how you can evaluate the key competencies of a data visualization specialist in a performance review.

To evaluate the key competencies of a data visualization specialist in a performance review, consider the following points: ✅ Technical Skills. ✅ Design and Aesthetics. ✅ Data Analysis and Interpretation. ✅ Problem-Solving Skills. ✅ Communication Skills. ✅ Attention to Detail. Incorporating these criteria into a performance review will allow you to comprehensively and effectively assess the competencies of a data visualization specialist.

Quando falamos de competências na visualização de dados, pensamos em ferramentas, linguagens de programação e nos passa desapercebida algumas competências que farão toda a diferença. Qual a procedencia dos dados escolhidos? Qual a o tamanho do projeto e sua complexidade? qual a amostra de dados que vou utilizar? Só depois de responder estas perguntas que deveríamos pensar em qual ferramenta atenderá melhor a construção do projeto. Durante algum tempo utilizei o QlikView e Power BI e percebi que muitas vezes me faltava o conhecimento em design para tornar as visualizações mais intuitivas e com maior impacto. Uma boa combinação de cores, criar um padrão tipográfico para facilitar a identificação de elementos relevantes.

Python plays an important role in the data analytics and visualization; Python has lot of libraries like pandas, NumPy, matplotlib etc., these python with this library helps you to master the visualization

Project Reviews: Review recent projects and the tools/techniques used such as SQL, Python, Tableau/Power BI. Technical Tests: Conduct tests or practical assignments to gauge their skills. Peer Feedback: Gather feedback from colleagues on their technical expertise.

Usefulness is a more important outcome than a design being "aesthetically pleasing." Design is rarely about decoration. It's about intention and use, and the ability to deliver on that intention. Professionally, this usually means they follow a process that can bring them from a design challenge to a final product. The best evaluation for design acumen is the result of user testing the design product.

Portfolio Review: Examine a portfolio of their work to assess design quality. Design Challenges: Present design challenges to see how they approach visual aesthetics and functionality. User Feedback: Collect feedback from end-users on the visual appeal and usability of their visualizations.

Thought Process - One way to measure this efficiently will be asking them to provide different approaches for the same problem statement and adding a what if scenarios to their answer to see how efficient and creative they are. Savings - Measure the outcomes against efficiency and savings in terms of man hours or cost impact and the duration they took to complete assigned projects.

Case Studies: Review how they handled past analytical challenges. Scenario-Based Questions: Pose hypothetical scenarios to evaluate their analytical approach. Stakeholder Feedback: Gather feedback from stakeholders on their analytical contributions.

Here is on what parameters they can be evaluated - Presentation Reviews: Observe their presentations and communication materials. Feedback Sessions: Conduct feedback sessions with stakeholders to assess communication effectiveness. Workshops/Webinars: Evaluate their performance in conducting workshops or webinars on data insights.

Technical skills only not enough to be data analyst or data visualization specialist., you need have a good communication skill too. you need to explain the data's, charts etc., that anyone can understand., it should be easy and convenient

Effective communication is crucial for a data visualization specialist. Assess their ability to collaborate with teams and stakeholders, simplify complex data concepts, and create visualizations that clearly convey messages. A skilled specialist should also excel in receiving and integrating feedback to refine visualizations for better audience understanding and impact. This competency ensures they effectively bridge the gap between data insights and their practical applications.

First of all, we need to ensure that all information has been checked and cross-checked before going into any analysis. As we already knows, we have some ways to do it, like: • EDA - Exploratory Data Analysis; • Descriptive Analysis; • Diagnostic Analysis; • Time Series Analysis. Each one with their own objective, depending on your problem you can pick up some of these analysis and try it out. Remember that those analyses may include some business research that MUST be related with the data you’re analyzing, do not include too much information and detail because it will mislead you and guide you to another point of view.

Avaliar as competências de um especialista em visualização de dados em uma avaliação de desempenho envolve verificar a capacidade do profissional em transformar dados complexos em insights visuais claros e compreensíveis. As principais competências incluem a habilidade de selecionar e utilizar ferramentas de visualização adequadas, conhecimento sólido em estatística e análise de dados, capacidade de interpretar e comunicar informações de forma eficaz, e um entendimento profundo das necessidades dos stakeholders. Além disso, é essencial avaliar a criatividade na abordagem de problemas e a eficiência na solução de desafios técnicos, sempre buscando maneiras inovadoras de representar dados que facilitam a tomada de decisões.

Training Records: Review records of attended workshops, courses, and certifications. Self-Study Projects: Evaluate personal projects or initiatives undertaken to learn new skills. Community Involvement: Assess their involvement in professional communities and contribution to discussions and forums.

There are lots of data visualization tools are there like pythons' and libraries, power bi, tableau, QlikView, and etc., so continuous learning is very important to be data visualization specialist., it also helps you to grow the career

Quantitative metrics/Business KPIs Achieved The impact of a powerful visualization is the key decisions arrived out of it and how it had led to a productive gain/growth or an operational efficiency over time. All of these being very quantifiable, in terms of monetary gains and others, a stong grasp of these can help more justify the value created by the previous experiences. It solidifies the impact and business need for the role in earlier contributions.

Here's what else to consider: Domain knowledge in specific industries can be valuable. Understanding of data ethics and privacy concerns is important. Collaboration skills for working in cross-functional teams are beneficial. Time management and project planning abilities help in delivering visualizations efficiently.