What do you do if your data visualization project needs problem solving skills?

It is mandatory to have a clear vision for the problem that you are asked to solve from your visualization. They all matter in the need of your achievement you are expecting from your visualization project and doing research in it and getting ideas about the domain to define the problem is essential.

If my data visualization project requires problem-solving skills, I would first assess the issue at hand, identify the root cause, and brainstorm potential solutions. Then, I would utilize analytical thinking and creativity to develop and implement effective strategies to address the problem. Additionally, I would leverage resources such as online tutorials, forums, or seek guidance from colleagues with relevant expertise to ensure successful resolution.

Se o projeto de visualização de dados exigir habilidades de resolução de problemas, é importante seguir algumas etapas para garantir o sucesso e a eficácia da análise. Aqui estão algumas sugestões do que você pode fazer nessa situação: 1. *Definir claramente o problema*. 2. *Coletar e analisar os dados corretos*. 3. *Aplicar técnicas analíticas adequadas*. 4. *Iterar e refinar*. 5. *Trabalhar em equipe*. 6. *Manter a criatividade*. Ao seguir essas diretrizes e estar preparado para enfrentar desafios com habilidades de resolução de problemas, você estará mais bem equipado para lidar com projetos complexos de visualização de dados.

Vale realçar que trabalhar com dados, é crucial para o Profissional Ser Dotado de Habilidades Analíticas num âmbito transversal. 1. Isolar o problema; 1.2. Perceber de maneira profunda o problema; 2. Identificar o elo de ligação do Problema em função das perguntas que devem ser respondidas; 3. Se houver alguma relação proveniente da solução do problema com os objectivos do projecto, então resolver o problema com suporte do departamento adequado; 4. Testar e validar; 5. Apresentar ao pessoal gestor de Top da Organização o DataVIZ.

Start by understanding the audience and the key message you want to convey. What questions are you trying to answer? Define constructs that you would like to showcase based on your Objective Function from the data you have, and accordingly try to determine what would be the best representation for these constructs. Defining the problem statement can help you in narrowing down the most relevant visualization choices. It clarifies the message, guides data exploration, and ensures the final visualization answers the right question. For a large set of data, Power BI/Tableau is an must have tool wherein you get to customize based on your fields, and your representation requirements.

The second step is to understand and exploring your data- like what are the fields, Are there any missing or incomplete values, Are there any duplicate values, etc. Transform and clean the data to make sure the final data is accurate and consistent.

Explore the data go through 2 stages, the first stage: General look, this Genral look gives me very soft impression about the data, give me the capability to imgane what is this numbers tell, understand and discover the plot, finger the primary key.. all my questions come after General look. The seconed stage: I called it Deep look, and this is about detalis, this look helps me to take the right decision act,clean,model and to choose most suitable graphs and charts supported by understanding from General look.

Before diving into visualization design, it's crucial to thoroughly explore your dataset. Start by cleaning and organizing the data to ensure its accuracy and consistency. Then, conduct exploratory data analysis to uncover patterns, trends, and insights. Utilize statistical techniques, such as descriptive statistics and data mining, to gain a deeper understanding of the data. This exploration phase not only informs your visualization design but also helps identify potential challenges and opportunities that may arise during the process.

Data exploration is something that opens the gate to the world of your analytical thinking where you explore the data and get the best ideas from it, understanding the attributes, finding duplication and finding empty value and handling them is another technique but exploration is the basic after that if your data have numerical attributes, you must need to see the correlation to get the ideas about the changes due to different parameters and visualization of it may clear you to your goal.

This is step is absolutely crucial but often under-utilized. For some of the datasets I’ve had to work with in my field, I’ve put in at least a dozen hours exploring the dataset and understanding relationships between features.

After understanding data, next step is to create visualizations that align with your message and help engage audience. Use correct visuals- pie, donut, bar, column, map, etc. and focus on adding labels, titles and legends to help audience better understand the visual.

Once you’ve got an idea of your visualisation, a great step before designing it in your chosen tool such as Tableau, is to create a sketch first. This not only allows you to map out your ideas in a more flexible way, but is also a great example of stakeholder management - if you’re designing a data visualisation for a specific audience/stakeholder, showing them a sketch first allows you to re-design, edit and come up with an idea that they will be happy with before spending a huge amount of time building it in e.g. Tableau, only for it to not be received well.

Designing visuals is not the task of doing drawing in your painting software but professionally it is supposed to use professional tools like Tableau, Power BI or R as well that helps you to enhance the computation and helps you to make visualization in the way you want against the large or small datasets.

Step three is where the magic happens; designing the visualization itself. This is where I turn numerical information into a compelling narrative. It’s like painting with data; picking out appropriate colors, fonts and arrangements that will guide the eyes of the viewer. Each component starting with axes to legends is important. Simplicity plus clarity are key- making it easy for viewers to instantly understand what is being communicated. For me, Tableau or Power BI are incredible tools that work like brushes in the hands of an artist moving me towards a common understanding with my audience through visuals that pack power and insights. Ensuring that the design is aligned with the the visualization objective plays an important role.

Within data analysis there are many tools that can considerably help to better understand deviations and find the most important anomalies. Choose the one that best suits your understanding and the business you are trying to understand. Following the guidelines of a problem-solving method such as Lean Six Sigma usually simplifies and better focuses the analysis. Try to use correlation analysis to understand the causes of deviations and avoid making the mistake of assuming that all problems have the same cause and the same way of analyzing them.

If your data visualization project requires problem-solving skills, first clearly define the problem you're addressing. Break down the issue into smaller, manageable parts. Use your data analytics skills to explore the data, identify patterns or anomalies, and test hypotheses. Engage with stakeholders to understand their perspective and gather additional insights. Experiment with different visualization techniques to find the most effective way to communicate your findings. Seek feedback on your visualizations to refine your approach. Continuously iterate on your design, ensuring it effectively solves the problem and communicates the intended message.

Another thing is testing and refining your visualization, sometimes it may happen you may have some error in your data points that are not good for visualization frames, to show the best and accurate insights you must ensure to have error free and tested visual in you project to get the problem solved more accurately.

Especially like every other tool, data visualizations are very effective. However, they need to be adjusted for them to operate well. Step 4 is vital in ensuring that your visualization hits the mark. We comb through both the data and the viz itself looking for inaccuracies or misrepresentations of any kind. It's not just about the data; it’s understanding as well. We gather feedback from our target audience using surveys, interviews or even usability tests. With this feedback loop, we evaluate whether our visualization communicates clearly its intended message and whether it is easily understandable. This two way process (feedback & personal evaluation) ensures that the end product is crisp, powerful, and speaks to its targeted audience.

Presentation matters! When it comes to the final product, presentation is the thing that defines your work that you have done, Having good hard work with bad presentation in front of the audience may ruin your entire work. Presentation may take place on PPT, Prezi or storytelling in a simpler and attractive way that seems appealing to your audience is the plus point.

The final stage is not just about displaying the data; it is about sparking understanding. We customize the delivery to resonate with each client’s particular audience. Clients from pharmaceutical industry visualize data for plainly narrated stories. Leverage storytelling dashboards—interactive visualizations that are woven into a story/journey. This allow audiences to investigate details at their own paces, while the plot line points out key findings. On the other hand, dynamic PowerPoint presentations might be more appealing to consumer retail audiences. To capture their attention effectively and communicate trends or product performances would demand striking visuals alongside brief insights.

Use the visualization tools that are easiest to understand and at the same time adapt to the most important study variables. Avoid overloading visualization templates with irrelevant data or unnecessary over-analysis. Try to make the visualizations quite intuitive and, to the extent possible, able to “speak” for themselves

When tackling problems in your data visualization project, brainstorm solutions with your team and see what could work. Work together and ask for advice from others if needed. By teaming up and considering different ideas, you'll find better solutions. 💡🤝 #DataViz #ProblemSolving #Teamwork

in my experience, nearly all data visualization projects require some level of problem solving skills. the very fact of needing to create some sort of datavisualization, is an attempt to explicitly show what problems exist or have the biggest impact, or identifying the root cause of a problem. the steps presented are valid steps and are steps that i usually apply in all my projects. most problems for a data visualization project is “what is the right visual(s) to provide answers to the questions the end user(s) have”.

Identificar e definir claramente o problema que precisa ser resolvido. Coletar e analisar os dados relevantes para entender a natureza do problema. Aplicar técnicas de análise de dados para extrair insights significativos dos dados. Desenvolver visualizações de dados claras e eficazes para comunicar os resultados da análise. Avaliar as soluções propostas e iterar conforme necessário para melhorar a precisão e a eficácia da resolução do problema. Essas etapas ajudarão a abordar problemas complexos e a criar visualizações de dados que ofereçam insights valiosos.

power BI has proven to be the most popular and easy-to-use software for many people in companies today, they not only help you analyze problems in financial or sales areas but also in industrial, logistical and more technical areas. Let us remember that everything that can be measured can be improved and that as long as we generate data from the measurements we can manage them appropriately seeking continuous improvement