You’re an Information Systems professional. How can you take your data visualization to the next level? (2024)

I've found that data can be very helpful, but I would add that simplicity matters. One well thought-out chart with a good explanation can be much better than three charts that might overwhelm. Control the conversation and use the chart as a starting point, not the end.

Choosing the right chart likely the most important step. When choosing your charts it is important to assess and understand your target audience. A dashboard reporting metrics for an engineering team is not going to be the same dashboard that management and executives are interested in. Take into consideration how much time the consumer has or wants to take when reviewing the chart. More than just picking the type of data you want to show, fully understand the data being consumed by the target audience or consumer.

To elevate data visualization, an Information Systems professional can leverage advanced tools like Tableau or Python libraries for more intricate visualizations. Incorporating interactivity such as tooltips and filters enhances user engagement. Storytelling techniques help contextualize data, making it more accessible. Integrating diverse data sources offers a holistic view. Advanced analytics techniques like predictive modeling unveil deeper insights. Custom visualizations tailored to specific needs and adherence to design principles further enhance visualization quality. These strategies collectively amplify the effectiveness and impact of data visualization efforts.

When it comes to visualization* Withy overwhelming options of visualizations, picking the one which is simple and conveys the keys idea is the key.* Presence of User interactivity via drill down and drill through helps.

Color coding might seem simple, but it can significantly transform reporting and boost its effectiveness. For example, at one of the AI companies, we measured various team metrics to improve their productivity, such as average velocity, say/do ratio, total features released, and total bugs found. To make these metrics more impactful, I suggested introducing color coding. Any deviation from the average by less than 20% would be highlighted in yellow, while deviations above 20% would be in red. These visual cues increased collaboration with the executive team, making them more interested in a detailed analysis of teams that needed support. And hence, Eng Ops review adopted this color coding approach across all teams.

Building interactivity into a visualization can allow for self-service for your audience, and can impress. Instead of needing to tailor a set of visualizations for different purposes (or for different target audiences) you can elegantly display the information, and allow the user to explore the information. A well-designed tool can reduce the need for many additional tools. And will also leave a user more impressed than a static graph or chart.

Data science is gaining importance these days. Data visualization tools are all about gathering raw data and converting the same into visuals. Being visual creatures, viewers can find colorful graphics to be quite appealing. Businesses can also convey messages quite easily and more effectively unlike plain numbers.Visuals also provide valuable information that can be absorbed effortlessly. It also helps specific datasets to become more appealing to users. Incorporating the same into business does not require any expertise. You just need to use the right tools and develop attractive charts, maps, diagrams along other visuals.

Top 10 Data Visualization Tools1. Tableau Public & Gallery2. HockeyStack3. Google Charts4. Google Data Studio5. Infogram6. Datawrapper7. D3.js8. Flourish Public9. RAWGraphs10. Dygraphs

As an Information Systems professional, there are several ways to take your data visualization to the next level, but I assume there is one above others to consider.Focus on user experience: Ensure that your visualizations are easy to understand and navigate. Pay attention to the design, layout, and color schemes to make the data more accessible and visually appealing.