Here Is How You Create Great IoT App Dashboards

Introducing IoT App Dashboards

For app users, introducing ready-made app dashboards means instant insight with all the relevant information being displayed immediately upon app installation. This way, users track only the KPIs they need and focus on the essentials. At the same time, users have the option to additionally customize their dashboards, for example, by building additional KPI trackers and choosing to pull the device data in a different way.

To do this, we additionally provide a low-code dashboard builder with multiple widgets and color options. This scenario is particularly interesting if app users want to consolidate the data from different apps in a single dashboard or track the metrics from different machines from the same dashboard.

For app developers, creating a dashboard means that the app data will become tangible to users immediately once an app is installed on an IoT device. The low-code dashboard builder makes it possible to create compelling dashboards in no time, using a variety of templates and customisation options. Ultimately, a clean dashboard that tells a compelling data story will facilitate quicker app adoption and make apps attractive to non-technical users.

Dashboards and data stores are created immediately once an app is installed and running on a device. The collected device data is stored in the cloud whereas the relevant device KPIs are displayed in a ready-to-use IoT monitoring dashboard, in near real-time. As app data is visualized right away, users can begin to track patterns and identify outliers from the very beginning.

Want to find out how to set up a data store and dashboard for your app? Discover the details in our documentation.

How to Create Great App Dashboards? Here Are Some Best Practices

Your new app comes with a dashboard? Let us look at some basic considerations and design principles that will help in creating a user-centric dashboard that is both comprehensive and laser-focused on the essentials. Here is a breakdown.

Think of the users

To start, the first consideration is who is going to use the app dashboard and to what purpose, what information would matter to these audiences, and how your data analytics solution should be delivered to add value to the users’ decision-making process.

Start with the data story

Think of ways to keep dashboard users engaged by telling a story with your data, that is, a consistent and compelling narrative, including interactive and custom components that will speak directly to the audience. A strong data story with a comprehensive layout and meaningful interplay of charts will make the whole potential hidden in the data more palpable to app users.

Aim for clarity

The data has to be conveyed in a way that is tangible to both technical and non-technical audiences, and presented in a way that precludes misinterpretation. Ideally, the information should be maximally decluttered and available at a glance. Make sure you are removing anything that is unnecessary, grid lines, labels, illustrations, icons, colors, or anything that does not serve the communicative purpose.

Determine the relevant KPIs

Once you’ve identified your audiences and your goals, it’s time to consider what metrics and datasets will contribute to answering the users’ questions and what data will not be that useful. The ability to select the relevant KPIs is what makes or breaks a dashboard. Make sure to include only the most important content in a way that serves the purpose and aligns with the data story.

Get the context right

Contextualizing the information in a meaningful way helps industrial IoT dashboard users understand how to interpret the charts. For example, context will help them understand if a given value is an outlier, if the numbers are good, and whether action is required on the basis of the data. This is usually done by including comparisons or other reference values.

Consider different user groups

Instead of cramming the whole information in one place, consider creating user-specific dashboards so that each user type will have a layout and charts that respond to their specific needs.

Select suitable graphs and charts

The way data is visualized should be dictated by the purpose of the visualization. There are four types of charts depending on the visualization’s goal: comparison, relationship, distribution, and composition. For example, bar charts are great for comparing things in the same category, line charts are suitable for displaying patterns, gauges are used to highlight a single metric that needs attention, while scatterplots are good for correlations and are mostly used by more advanced audiences.

Select clear labels

The labels that describe a metric or a chart should be self-evident and clear, available at a glance, short, and decluttered. For this, the usual practice is to use abbreviations and symbols to avoid taking too much space, as well as headings to avoid repetition.  

Use size and position

Introducing numbers of different sizes and positioning these strategically (e.g. top left) gives users a sense of what’s important and where to look first. Size and position can be used to highlight the most relevant metrics and guide audiences through the data story moving from the important to the less important data.

Consider the layout

The visual organization of the dashboard is an art in itself. As a rule of thumb, simplicity and clarity are key to delivering an outstanding layout. Key here is to stay focused on the big picture and allow users to immediately grasp the content. Charts displaying similar metrics should be grouped together. Another consideration is hierarchy, and here the top-left rule applies just as well – all the most important information is displayed on the upper-left part of the page.

Use blank spaces

Instead of cramming the dashboard with content, increase the margins around the individual components to allow for more blank space. This will create a sense of balance and will allow users to focus on the essentials. When each element is surrounded by a sufficient area of blank space, it becomes easier to grasp.

Choose colors sparingly but consistently

The rule of thumb here is to select two or maximally three main colors and then experiment with gradients. Lighter shades and pastel or toned down colors are preferred when displaying complex information. Also, use shadows and special effects sparingly, as flat designs are easier on the eye and do not divert the users’ attention. Consistency is equally important, for example, using the same color for a particular type of element in all charts.

Avoid overusing real-time data

Sometimes, using real-time or near real-time data leads to too much information displayed in way too much detail. While this can be useful in some scenarios, in most cases it will lead to confusion and distractions. If you are not explicitly tracking live metrics or looking for a specific trend over a very short period of time, it is sufficient to only update the dashboard at certain intervals.

Experiment with interactive elements

These will allow users to go deeper into the data, with drill-down features, click-to-filter components, and time intervals.

Conclusion

When building an IoT application as a standalone IoT product, IoT dashboards become indispensable components of user experience. IoT app dashboards are not simply data visualization tools built on the basis of incoming IoT data. Rather, an effective IoT dashboard works as a complex IoT solution that delivers the full data picture around each and every connected device.

The real-time data coming from IoT sensors closest to the data source and the artificial intelligence/machine learning solution on top make for a powerful duo to improve operational efficiency and deliver actionable insight in a highly visual way. Thanks to widget utilization and various visual components, IoT data visualization becomes the one proven way to look closely into operations, identify gaps, and map out a course of action.

In sum, IoT visualization and IoT analytics at the very level of the IoT application can help anywhere, from overseeing IoT connectivity across multiple devices and device management to condition monitoring, all the way to overarching smart solutions spanning multiple manufacturing sites.

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