Wie nutzen Sie Datenvisualisierungstechniken, um Daten zu präsentieren und zu untersuchen?

Buy or go to the library and check out some Edward R. Tufte books on Visualization. There are excellent examples in those books and explanations on how poor visualization failed to get important messages across. (e.g. Challenger O-rings)

🤔 How do you leverage data visualization tools to help you explore data? First, familiarize yourself with some of the visualization options in the market which include but are not limited to the following: STOIC Minitab Microsoft Power BI Elastic Stack Dynatrace Splunk Grafana Metabase Datadog When choosing the right visualization, many of these tools provide recommendations and pre-generated visualizations based on the AI integrated into them. Whatever you do, don't get wrapped up in analysis paralysis. Use your tools, don't let them use you. There is a wealth of courses, both free and paid online to learn how to use these tools. Some of the tools here are free and open source and some are enterprise grade. 👍 Like to support this

Data visualization techniques are powerful tools for presenting and exploring data in a way that is intuitive, insightful, and engaging. Here are some ways to leverage data visualization techniques effectively: Choose the Right Visualization Type Simplify and Clarify Highlight Key Insights Interactivity and Drill-Down Tell a Story Use Multiple Views Iterate and Refine By leveraging data visualization techniques effectively, you can create compelling visualizations that not only present data in a clear and accessible manner but also facilitate exploration, analysis, and decision-making.

Os gráficos facilitam a leitura do comportamento dos dados independente da melhoria. Os mais utilizados dentro dos projetos Seis Sigma, em uma abordagem simplificada e inicial, são os de linha, que comparam claramente os dados atuais com a meta. Em seguida, considerando a estatística, gráficos de Pareto, curvas de Gauss e cartas de controle são utilizados para se estratificar problemas, priorizar grupos e visualizar a média e a variação em termos de entradas e resultados esperados de um processo ou de um produto. Tendo essas referências, a análise de causa é conduzida nos levando a ações específicas, onde em uma última fase, a de controle, revisitamos os gráfico iniciais, comparando a entrega com a meta especificada nos grupos escolhidos.

Everyone loves a pie chart!! Just kidding. Pie chart usually get no love. But you know what people like? Any visual that accurately communicates the message.

Balance interesting or elaborate visualization design with clarity of the message you are conveying. Choosing designs that overcomplicate the data, or including too many elements in a visualization risk losing your audience to the visual itself of the noise it contains.

After selecting the right chart type, adhere to data visualization best practices for clarity and accuracy: Label Consistency: Ensure labels, titles, and legends are consistent and meaningful to aid understanding. Avoid Overloading: Don't use too many colors, fonts, or symbols; they can confuse your audience. Scale and Axis Precision: Use appropriate scales and axes to accurately represent data and maintain proportionality. Interval Selection: Choose intervals that enhance data readability without distorting the message. By following these practices, you enhance your chart's readability and credibility, effectively conveying your data's message.

I’m a big fan of interactive dashboards like PowerBI and Einstein by Salesforce. Sometimes the story is found by looking at the interactions of multiple widgets. For example, I was looking at accident data. I looked at age, intoxication, cell phone use, type of accident and others. To my surprise most accidents were sober, non cell phone related. The top accident was in the front corner blind spot which exists on all vehicles. Raw data to insight in less than an hour.

Creating and customizing charts is made efficient by utilizing various software tools. Consider these options: Basic Charts: Excel, PowerPoint, Google Sheets: These tools offer built-in functions and templates for creating straightforward charts. Advanced and Interactive Charts: Tableau, Power BI, R: These tools allow you to design interactive and dynamic charts with advanced features and customization options. Visually Appealing Charts: Canva, Infogram, Piktochart: Online tools like these enable you to craft visually appealing and engaging charts with design elements and effects. Leveraging software tools streamlines your workflow and enhances the quality of your charts, catering to various needs and preferences.

Data is at the center if any Six Sigma project. Most of the statistical softwares like Minitab or JMP, offer the advantage of visualizing the data in different charts and formats. Minitab is a great software package to view majority of statistical analytics data of higher degree including anova/regression/ interaction analysis. For the most basic of purposes MS Excel is used by majority of population. Visualization softwares like Tableau and PowerBi have gain popularity in recent times as one can make interactive dashboards and also with AI tool, you can get suggestions on the best analysis and graphs for a particular data set.

Charts and stats tests can help you move from Fishbone, C&E Matrix, and FMEA with concrete evidence of what is causing the problems. It takes you from many Xs to the vital few Xs. Ongoing, these charts and stats tests can help sustain the gains and continuously improve.

After creating charts, the next step is to analyze and interpret them for insights. Here are key methods: Descriptive Stats: Summarize data with measures like mean and median. Trend Analysis: Spot patterns over time or categories. Hypothesis Testing: Test hypotheses for significant findings. Inferential Stats: Make population inferences from sample data. Correlation and Regression: Explore relationships among variables. Graphical Analysis: Visualize data with various chart types. Outlier Detection: Identify and investigate anomalies. Cluster Analysis: Group similar data points to find patterns. These methods help answer questions and inform decisions based on your data.

Alguns cuidados são necessários para se obter o melhor de cada gráfico. O primeiro deles é fazer uma verificação quanto à sua escala para identificarmos se a forma como os números aparecem faz sentido, evitando uma leitura que pode levar à uma conclusão equivocada de quem o visualiza. Em um segundo momento, ao mostrarmos pontos pertencentes à uma única curva, precisamos entender se todos os dados podem ser representados em um único gráfico ou se eles deveriam fazer parte de duas ou mais distribuições, normalmente vindos de diferentes processos. Além disso, na maioria das situações, é importante usarmos mais de um modelo para os mesmos dados, o que aumenta a chance de notarmos comportamentos que direcionam para relações de causa e efeito.

Analyzing and interpreting the data in form of a table is a cumbersome task and hence the graphs provide an easy solution to it. Graphs and charts give you best information about the data be it as simple as a trend series plot or a normal distribution plot to more complex ones like DOE and MSA analysis. Boxplot and histograms are other great examples of plots which help you interpret data at a quick glance and also tell how the data spread is including outliers.

As shared throughout this article, data is important and the way it is conveyed helps tell the story. It is important to ensure you weave the data effectively into the compelling story you are telling. Data without story is cold and boring. Incorporating the data into the narrative will help you more successfully sell your idea, concern, or plan. We are all story tellers, stories with data are compelling.

O registro das conclusões e ações tomadas a partir de gráficos é o que torna representações simples efetivas. Escolher um modelo adequado para se fazer uma apresentação é importante, porém nem sempre, em reuniões e fases seguintes, é possível lembrar como se chegou em uma determinada tratativa. Portanto a dica é sempre deixar uma nota embaixo ou ao lado de pontos específicos como outliers e mudanças de padrões. Assim ao recuperar e transmitir informações, contar o racional das decisões tomadas será mais fácil, dando mais credibilidade à sequência de trabalho adotada durante o projeto.

Once you've analyzed your charts, the final step is to present and share them with your stakeholders and audience effectively. Consider these strategies: Reports, Presentations, Dashboards: Organize and display your charts using reports, presentations, or interactive dashboards. Storytelling: Create a compelling narrative or story to explain and highlight the significance of your charts. Feedback and Interaction: Engage with your audience through feedback, questions, or comments to foster interaction and understanding. Effectively presenting and sharing your charts allows you to communicate findings, demonstrate value, and influence informed actions among your audience.

Charts help you tell a story and is a great way to present your analysis and interpretations. Interactive dashboards not only helps you present data but also is a powerful tool to convey your message across to stakeholders that enables quick and a better informed decision making. As I was starting my career, I was given the advice that 1 chart is equal to 100 words and that one presentation slide should have 80% visual content and 20% text, which is why charts are the best way to represent the data.

Visualization Consistency: When creating charts for reports, presentations, or dashboards, it's essential to maintain consistency in style, color schemes, and labeling. This consistency makes it easier for your audience to understand and compare information across multiple charts, ensuring a coherent and effective data presentation. Consistency in visualization is particularly crucial when dealing with complex data sets or when presenting data to diverse audiences, as it helps prevent confusion and enhances the overall impact of your charts.

Use a line chart to show trends over time. As example, you could use a line chart to show the monthly sales of a product over the past year. Use a bar chart to compare different categories of data.you could use a bar chart to compare the sales of different product categories. Use a histogram to show the distribution of data. you could use a histogram to show the distribution of customer ages. Use a scatter plot to show the relationship between two variables. you could use a scatter plot to show the relationship between customer age and spending habits. Use a heatmap to show the intensity of a relationship between two variables.you could use a heatmap to show the relationship between product sales and different geographic regions.

É fato que nem todas as organizações têm como base a prática de se utilizar gráficos para a demonstração do comportamento de uma variável ou de um indicador. Muitos profissionais não têm este hábito, o que é natural. Ao se buscar a implementação deste tipo de abordagem é imprescindível dar um destaque para grandes vantagens como a leitura mais rápida das informações e a clareza com que visualizamos tendências ou mudanças. Isso pode ser feito gradualmente de um ponto onde aplicamos em documentos de níveis estratégicos até quando temos a prática ensinada e disseminada na operação em quadros diários ou em modelos de A3 dedicados a Kaizens, por exemplo.