How effective are your data visualizations?

Data visualisation is very important in todays world,key decisions are based huge statistical data. Any key or important data, however accurate the data is, looses its importance if it’s not presented using proper data visualisations. Though it’s very important to understand the target audience and use data visualisations accordingly, it’s also very important to keep our visualisations simple and clear, which enables to convey the story across various target audiences. With various tools available in market (tableau,looker studio etc.), presenting data with visualisations to tell better story is become easier for businesses. Lastly, visualisations should be dynamic, where data can be interpreted in different ways by users themselves

Understanding what Visual to choose to tell your story is as important as knowing the audience. A wrong visual could have very wrong implications on what you are trying to present. Even the theme to an extent plays a crucial role in representation as preferences could vary for each audience.

In my approach to data visualizations, I prioritize audience-driven curation over self-curated motives. I delve into insights sought by using methods like questionnaires, ensuring alignment with viewer expectations. Crafting a message revolves around conveying requested metrics clearly and effectively, meeting audience objectives precisely. Platform choice is strategic. For internal presentations, I opt for collaborative tools like Power BI server, fostering teamwork around data. Spreadsheet collaboration steers platform selection; while Excel suits some, evolving SQL and cloud solutions often sway decisions for robust, scalable data handling. It's about tailoring the platform to audience needs, embracing tech advancements.

I believe "simplicity" is the primary factor in measuring a data visualization's effectiveness. Suppose you deliver a data visualization with a simple design that conveys a critical message to a targeted audience at first glance despite the complexity of the underlying data. In that case, you've delivered an effective, impactful data visualization your audience would appreciate.

The effectiveness of data visualizations depends on careful planning, design, and consideration of the audience and context. It's also important to remember that data visualization is a skill that can be developed and improved over time with practice and learning from best practices and feedback. Effective data visualizations should communicate information clearly and concisely. They should not overwhelm the audience with unnecessary complexity. Keep charts and graphs simple and easy to understand. Ensure that your data visualizations are relevant to the message or story you want to convey. Choose the right type of visualization (e.g., bar chart, line chart, scatterplot) that best represents your data and your intended message.

Data quality and accuracy are the cornerstones of any meaningful data visualization. They serve as the pillars upon which the entire visualization experience is built. When your data is pristine, reliable, and representative of reality, your audience can trust the insights you're conveying. It's like constructing a sturdy bridge: every data point is a well-placed brick, ensuring that your audience can safely traverse from information to understanding.

"Prioritizing progress over perfection is key. Commence with the data you have—even if it's at 70 to 80 percent quality. This serves as a foundation to build upon (supported by assumptions and documentation). Consider this: Imagine launching a retail dashboard with most sales data, albeit some projections. This allows early insights and iterative refinements, driving data (visualization) maturity in the right direction."

Aligning your data visualization with your organization's 'sources of truth' is crucial for ensuring data quality and accuracy. To bolster this, consider the following 5-step process to develop your own personalized data quality regimen: 1. Profile Your Data: Understand its current state. 2. Cleanse and Enrich Your Data: Address discrepancies, duplication, and incompleteness. 3. Standardize Your Data: Ensure consistency of numeric units. 4. Conduct a Business Reasonability Check: Review your datasets with your business points of contact. 5. Iteratively Enrich Your Data: Based on feedback and your own re-assessment, continually refine your datasets. Be sure to document your process, approvals and any automation processes.

Effective data visualizations rely on trustworthy and accurate data. To ensure this, your data should be reliable, consistent, and free from errors or biases. Make sure it's up-to-date and represents your audience well. Document where your data comes from, how you gathered it, and any assumptions you made. This clarity builds trust and helps your audience understand your visualizations better, making them more impactful tools for conveying insights and making informed decisions.

We should consider that all customization we do for the sake of telling the story. In that context, keep your visualizations simple and interpretable. Don't try to create fancy graphs and charts.

Another aspect, we should focus on, is to think about the cultural context of your viewers, as certain symbols or colors may have different connotations in various regions. By empathizing with your audience, you can create visualizations that resonate and convey insights more effectively.

Indeed, the significance of design principles cannot be underestimated. In my approach, I invest considerable effort in harmonizing visuals with the company's overarching theme. A strategic step involves extracting the company's color palette from the website or logo, accomplished with the aid of a color picker. I rely on a blend of tools including PowerPoint and design platforms like Figma to elevate the dashboard's aesthetic appeal. A key practice involves maintaining uniformity in card dimensions, font sizes, and font types. Additionally, I'm meticulous in ensuring that the background color seamlessly aligns with the established theme.

It's important to make visualizations accessible to all. Using color palettes compatible with color blindness or adding alt text and image descriptions for people with visual impairments are good practices for data visualizations of any kind.

Creating effective data visualizations also involves adhering to design principles and best practices. Use colors, shapes, sizes, scales, and labels thoughtfully to emphasize key data points and patterns while avoiding confusion and clutter. Leverage contrast, alignment, proximity, and repetition to establish visual hierarchy, harmony, and balance. Incorporate titles, captions, legends, annotations, and narratives to offer context, guidance, and interpretation for your visualization. By following these design principles and best practices, you can craft data visualizations that are not only engaging but also informative and easy for your audience to understand.

A key aspect of enhancing engagement is creating avenues for behavior change. One effective way to draw users to your dashboard is by configuring alerts. For instance, sending standard alerts when data is updated can keep users informed and engaged. Likewise, alerts triggered when Key Performance Indicators (KPIs) hit important thresholds can help users integrate these tools as essential parts of their workflow.

In marketing and infographics, fancy visualizations with animations are effective for engagement. However, in corporate decision-making, clarity and quick data comprehension are paramount. Overly animated visuals can distract and hinder decision-makers. Interactive elements should be used moderately for customization, but excessive animations may impede, rather than aid, the process. In summary, while animations are engaging in marketing, they should be used sparingly in corporations to ensure a focus on clear and actionable data insights, aligning with the presentation's goals and audience needs.

One important aspect to evaluate and enhance data visualization effectiveness is to consider how it engages and interacts with your audience. Design your visualizations to be responsive, interactive, and dynamic, enabling your audience to explore, filter, zoom, and customize the data to their preferences. Incorporate elements like animation, transitions, and storytelling techniques to create a captivating and memorable experience. Utilize feedback mechanisms, calls-to-action, and social sharing features to encourage your audience to take further actions or share your visualization, enhancing its reach and impact.

I believe visualizations like dashboards should allow users to provide feedback and take actions. For example, a manager can look at a dashboard and identify some areas for improvement and then this person needs to be able to provide feedback right on the spot. A good example would be sending an email with instructions to a team of people on what needs to be done, should be facilitated inside the dashboard without going into the email client separately. This way we can make sure every user is interacting with the dashboard and it is kept alive.

Interaction will happen when you turn industry jargon into categories that humans care about. A map's audience use location search to see what's happening to places to they care about. A product manager uses would use a persona filter to see what a specific segment is doing. Go the extra mile of figuring out what your audience ultimately cares about - how would they use the data, and then give them ways to be flexible with that interaction.

To make sure your data viz works well, you gotta evaluate it and get feedback. Use tools to track stuff like views, clicks, and shares. Also, ask your audience what they think through surveys or interviews. Take all this info to see what's good and what needs fixing in your viz, then make it better.

Evaluation should not be a final process, as listed here. It should be iterative. Find an audience point-of-contact a SME and set up time with them to co-build and get faster feedback. You can capture what data/metrics truly create meaning early on. By doing this you reduce the risk of whether people will use your view, and can focus on who is using it and how.

One extra nugget: Stay curious! Keep exploring new ways to present your data. The field of data visualization is always evolving, so don't be afraid to try out fresh ideas and techniques. Happy data viz!

Data Visualisation opens up a window of possibilities for modelling. It challenges the underlying assumptions of model building and throughs few unexpected surprises.