What are some common data visualization challenges and how can you overcome them?

You need to do a few things. First, make sure your data is clean and accurate. Then, choose the right type of chart and don't make your charts too crowded. Use titles and labels to explain what your charts mean, and use colors and simple designs to make them easy to understand. Think about who will be looking at your charts and make them easy for them to understand. Don't use too many interactive features, and tell a clear story with your data. Also, make sure your charts work on different devices and are kept up to date. Listen to feedback from people who see your charts and use tools that can help you make them even if you have limited resources. This way, you'll create visualizations that people can understand and learn from.

One of best ways to visualize data is by categorizing key metrics. For example, a company's primary metrics should be the one that represents larger audience view and financial health like user sentiment ( NPS), Growth Numbers, Margins, Active users etc Categories what matters most and represent both postive and negative trend of these metrics <> Categorize by segments for clarity, delegation and fast action <> Limit details on each topic and provide a deep-dive option to help your story These categories will help you organize better and draw attention to things you want to convey. The best way to improve is to discuss what matters, take part in discussions as there's no substitute for active feedback.

To deliver data that drives a decision, it's vital to pick the right demonstration that contains: - a concise message backed up by the numbers - narration of a story, leading to an impact. 1. Start by digging deep into the questions a particular dashboard/report/story board aims to answer. 2.Draft up the story, how these questions will be answered, one by one. Begin with the basics, like: - KPI comparisons over time (%) are good as cards - Line Graphs aptly summarize trends over periods of time - Bars and Columns are great for stats comparison over multiple dimensions - Pie and Donuts are good for a quick comparison, usually under three categories. 3. Wrap it up in a decent color scheme to emphasize the message and the brand.

Have you recently consulted a doctor? ???? Imagine you and your friend are both feeling unwell, with different symptoms - you have a fever?? and he has vomiting ??. The doctor's approach would be to first inquire about your symptoms and conduct a thorough examination, which might include checking your heart rate. Only then would the doctor prescribe a treatment. Will he give the same medicine ?? to both of you? Likewise, treating data involves asking questions, closely observing its characteristics, and comprehending its 'symptoms'—the data points, variables, and context. Only after this thorough examination should you prescribe the right 'treatment'—the appropriate chart ??

From my experience building data visualizations I look at other industry's for inspiration. This is useful in identifying what message, chart types and variables to choose.

Thinking through who is the audience and what are they solving for is a critical step in defining the needs. Data visualization can easily get cluttered and complex if not designed around a precise decision process. Often when building a dashboard we often try to include as much as possible. The reality that the magic of data visualization is to define the few information that makes the difference for the audience. One typical issue is that developers think that a dashboard solves all purpose, the reality is that a visualization to be effective needs to be built for a user or a specific purpose. In addition, the less is more in the case of data visualization. Leaving enough space between graphs and correctly labelling data is critical.

One thing that I have learnt over the years is that - Mastering data visualization means finding the perfect balance between too much and too little information. Don't overload with colors, labels, or unnecessary elements, as it can overwhelm your audience. Instead, simplify your visuals, use the right scales and axes, and apply colors strategically to highlight what truly matters. In my experience, keeping things clean and clear in your data presentations makes all the difference!

People seldom have the time and energy to decipher what a complicated graph is communicating. They are a tool to prove a point and their creation should be based purely in service of explaining that point. Don't get carried away with the hot features of your graphing program - you probably don't need them.

In my experience there are multiple challenges that require consideration, however, these challenges are frequent and difficult: 1. Complexity of Data: Visualizing data with many variables can be challenging. Utilizing dimensionality reduction or interactive tools can simplify data exploration. 2. Misrepresentation: Inappropriate scales or visualization types may not reflect data accurately, leading to misinterpretations. A sound understanding of data and careful selection of visualization techniques mitigate this risk. 3. Color Usage: Employing a restrained color palette to highlight key insights, while ensuring the capability to elaborate on other data points as needed, can enhance the clarity and effectiveness of visualizations.

Also consider things like . Data cleaning and preparation: Before creating your visualization, ensure that your data is accurate, complete, and consistent. Check for outliers, missing values, and inconsistencies -. Selecting appropriate chart types: Different types of charts are suited for different types of data. Choose a chart type that effectively represents your data and supports the insights you want to convey. -Avoiding 3D effects: While 3D effects might seem visually appealing, they can introduce distortions and make it difficult to interpret the data accurately. Stick to 2D representations for most cases to avoid misleading interpretations.

When it comes to ensuring accessibility and readability in data visualisations, a minimalist, user-centric approach is crucial. Knowing your audience is key; a dashboard should be designed for a targeted stakeholder group, anticipating their objectives, needs and preferred metrics. Ideally a dashboard or GUI should also include some user customisation to cover any bases not anticipated.

Don't try to do too much with a single visualization. Often, two side-by-side comparisons are more effective than trying to place multiple series in the same graph.

Some simple ways to ensure accessibility and readability of dashboard: - Use larger text with higher contrast. Don't use fonts smaller than 12 pt in your visualization. Aim for a contrast ratio of at least 4.5 between your text and the background. - Use white space effectively. Don't crowd your dashboard, use padding to separate visualizations from text, images and from each other. - Add alt text to all images - Distinguish dashboard elements in different ways. Colour palettes that address colour blindness are good but there are different types of colour-blindness so one won't work for everyone. Consider using a shape or numbers in addition to your colours.

One thing to always remember is how the visualization is intended to be used. A visualization being presented in a conference room may need bigger or bolder text for audience members sitting further away. On the other hand, a visualization only intended to be used on a desktop by a single user may be able to have smaller text. Understanding who might be viewing the visualization is another important factor to remember. Different people may interpret the visualization differently depending on their background. Ensure any terms being used will be correctly understood by the viewer and relay the same meaning that was intended.

If you're working with colourful diagrams, keep in mind that there are people out there with some variation of colour blindness. Such as monochromatism, dichromatism, and anomalous trichromatism.

One effective practice I've observed is commencing your analysis, presentation, or dashboard with a question. Follow this with a narrative and conclude with a clear answer and actionable steps. Furthermore, emphasize the critical role of context in this process.

When we build reports/dashboards are we just adding charts, scorecards, and tables to fill up a section of the page/tab? When it comes to building a report/dashboard, the building process should start with having the thought process of "Answering One Question" per section/tab that matters to the audience. In your report or dashboard, include an executive summary section with a concise summary of metrics that are important to the audience, in order to give the viewer a quick understanding of the data.

Whether we like it or not, storytelling is nothing short of an entertainment. Keeping our audience engaged is paramount. Ideally we want to use the language our audience uses, if they use jargon, incorporate it. Ask questions, and if possible allow the audience to respond, else givea pause for the audience to think on the answer. Don't expect your audience to remember many numbers or facts. They will probably remember how "they felt" about the presentation. Thus, choose a goal for your presentation, and work towards it. Use numbers, graphs, statistics, etc. only to convey your message, and at all times stick to your goal and nothing but your goal.

My preferred perspective has always been - the story & takeaway are the main characters, while the data are just supporting characters to make make the story relatable & believable, and the takeaway more actionable. Hence I would actually work more on the narrative and then select the appropriate data to go where I see fit according to the target audience, instead of the other way round. But of course, before that happens you'd first have to fully digest the data insights to determine the story/narrative & takeaway. That's given.

What i found quite helpful is to think through the conversations that the dashboard/ visualisation needs to enable. For example 'dry run' a potential meeting or discussion that should happen and stress test the dashbaord helps. In general, follow a 'top down' view of the data and start from a more aggregate level and then allow for deep dives is a standard way to allow for logicial and clear view. Key success factors, is that there should always be a single problem statement that the visualization needs to solve. A great dashboard is not a solution that fits all.

1. Simplify visuals to avoid clutter. 2. Verify data accuracy for clear communication. 3. Use a harmonious color palette for readability. 4. Tailor visuals to your audience and add context. 5. Craft a narrative through visuals for better understanding. 6. Choose the right chart type for your data. 7. Add interactivity for deeper exploration. 8. Ensure mobile-friendliness for all users. 9. Prioritize accessibility for inclusivity. 10. Collect feedback for continuous improvement.

My teams have led large scale implementations and Migrations of Dashboards / BI systems. From that experience below are the key challenges and steps to mitigate; 1. Unavailability of data in the right format and quality suitable for leveraging for Dashboarding and visualization: This often results in wrong kpis and metrics being shown on the dashboards. Ensuring clean data with the necessary transformation and business logic applied is a critical criteria. 2. Changes to requirements and dashboards: This happens for multiple reasons and sometimes unavoidable. A better approach is to work closely with business users, break the entire development into smaller modules, create wireframes, factor in any necessary changes, continuously enhance.

- Simplifique - Garanta que os dados fortalecem a mensagem que você deseja passar - Remova qualquer tipo de informa??o desnecessária e que pode distrair o público - Teste, critique e crie seu próprio estilo

In my experience, given below are the key factors to be taken into consideration in order to build effective data visualisations. It is crucial to understand the requirements of the stakeholders accurately, work with them to identify the questions that they have from the data and create visualisations that would answer those questions in the most simplified and elegant way. Furthermore, the goal should be to design a report/dashboard with the highest possible data-ink ratio. Data-ink ratio is the ratio of elements in a visual representation conveying required information to the total elements in the visual. So above all else, show the data.

The #1 most important thing about data analytics and visualization is the data. Without clean and complete data that is standardized properly, it doesn't matter how pretty the visuals are.