In the data analytics sphere, Dashboard is the perfect way to demonstrate relevant data using data visualization techniques. The major purpose of a Dashboard is to allow stakeholders to track KPIs, metrics, and other crucial data. In other words, a Dashboard helps to communicate data visualization to the audience in a way of telling actionable stories. Hence, they can identify and make better data-driven decisions. Understanding how to create an effective dashboard design is vital to data analysts. Why is that? Let's find out together
Did you know that “74% of organizations that use visual data discovery empower managers to make decisions when necessary” - Aberdeen Group. When most businesses tend to make data-led decisions by adopting data analytics for their daily practice, it is undoubted that Data analysts have to work with data every day and turn it into visualization. By doing so, data analysts can analyze, present, tell stories, and streamline decision-making progress. Not only do they know the crucial data should be included but also the design elements should be depicted. Therefore, it goes without saying that Dashboard UI design is very important to Data Analysts.
Dashboards are where data is illustrated and turned into actionable insights. Therefore, the design should be focused to showcase this goal. Let's imagine that your Dashboard features enough crucial metrics. However, your stakeholders can not grasp your points because you design your UI dashboard in a clustered way. It's not only a waste of your time but also confused audiences, or even worse, may lead them to make bad decisions.
A successful dashboard needs to be insightful, and easy to scan but attractive. Below are the major components you should take into account to better design Dashboards that tell proper stories and make better decisions.
Dashboards are classified into many types of categories based on your purpose, including analytical dashboards, operational dashboards, and strategic dashboards. Sometimes, they can be divided into narrower categories such as sales dashboards, marketing dashboards, and so forth. Therefore, to show metrics on the dashboard in an accurate manner, first and foremost, you have to determine the purpose of the data shown. Below are three main types of data you need to consider.
Operational dashboards are in use for users who manage daily operations for their businesses. Using operational dashboards to show crucial information lịke metrics and KPIs that are real-time or transactional. Because of its real-time manner, data on operational dashboards update regularly.
Strategic dashboards are for users who monitor the business as a whole or a specific functional area, mainly executives who track the status of KPIs. Strategic dashboards are updated less regularly than operational dashboards. Sometimes. Strategic dashboards were viewed once a day or over a period of time, maybe a month, a quarter, or a year to summarize performance.
Analytical dashboards are often for experienced users such as data analysts or data scientists. Normally, analytical dashboards allow for analyzing a vast amount of data so that users can dig deep into data to identify anomalies. Moreover, it is to deeply analyze, predict outcomes, and get insights into the decision-making process
Getting a deep understanding of your audiences who consume your dashboards and the genres of dashboards you will deliver is extremely critical since they set the grounded foundation for deploying other Dashboard Designs.
No matter what kinds of dashboards you use to communicate data, they have to stay consistent. So, in which area should they be consistent? Typography, colors, and even other small design elements like formatting or graphic elements. For example, if you use blue to assign the category “T-shirt” in the Monthly Clothes Sales Report, you have to stay consistent with the blue color in any chart featuring a “T-shirt”. In some cases, users want to make the dashboards more attractive and highlight by adding too many colors. And, the result is, that they are confusing their audiences and I am sure that your audience will find it hard to read your dashboards and ask many questions. This is a detrimental pitfall you should avoid.
Regardless of consistency, not overdesigning the dashboard is an important thing to be concerned about when delivering critical information to your audiences. Ensure the size of fonts for headings, and annotations, and limit using too many colors in your dashboards. If you want to use a certain color to make the highlight for a specific dimension, keep using the same color throughout the dashboards.
Setting a hierarchy and form to present your data visualization across the dashboards is a must you can not ignore. Your story should be organized in an attractive and easy-to-understand way to draw your audience to the right areas in your Dashboard.
You should make decisions about which cards or charts to show and which positions to display. If not, You cluster your dashboard and your stakeholders don’t know which charts should look at first, what will be next, and continuing. Without hierarchy classification in your Dashboard, you waste all your dashboard creation effort in the trash bin owing to failure to convey information clearly and efficiently.
To have a better understanding of how to showcase your elements that draw audiences’ eyes scanning across the Dashboards, let's consider applying “F visual patterns” and “Z visual patterns” on your Dashboard.
A study was conducted by the Nielsen Norman Group in 2006 to identify which component is considered to be one of the most cited and the most useful eye-tracking findings. They selected 232 users and let them view thousands of separate web pages. The result showed that the users tend to read in an F-pattern.
The F-pattern allows users to first read in the horizontal direction, mostly across the upper part of the content area. Secondly, users will move down the page and still keep reading in the second horizontal line. And lastly, users scan the left side of the content in a vertical movement.
Since people tend to look at dashboards as they do with other pages, you can apply the “F-pattern” logic to structure the data or elements across your Dashboards. Data across dashboards should start on the upper left side and distribute to the right side. Then, keep doing so in the second line in the same direction. And last, the data distribution will be in vertical movement on the left side. Ensure that the data show support for the methodology to direct audiences’ eyes following an F-pattern while reading the dashboards.
Like color, style, font, and other design components, language or wording are indispensable to your Dashboards. Dashboards are obviously where you distribute your visualizations. Therefore, let's consider using visible and clear labels, headings, and annotations.
When you show your charts or graphs on your dashboards, you can not assume your stakeholders must understand what you want to deliver. This is one of the big problems you should avoid when building an attractive Dashboard. Let's take your sales dashboard as an example. Within a visualization, you can not label your chart with just the phrase “Sales in June” and leave it to your audience to read the rest, it is definitely time-consuming and clustered. Therefore, let's focus on the key message you want to convey through that chart to name the label. Besides, don’t forget to add annotations that are brief and clear explanations.
By focusing on these tips, you certainly attract your audience at glance to understand promptly the narrative you convey. Therefore, they can absorb the whole easily and effectively.
In the end, your goal of building Dashboards is not only to ensure a captivating dashboard design that is attractive, but also efficient in order to capture the audience's attention from the first time and streamline the decision-making process. There are some dashboards users add several design elements that indeed aren’t necessary at all. Therefore, to ensure audiences have efficient interactions with your Dashboard, let's remove elements or visualizations that are redundant and clustered. Charts, headings, filters, annotations, color settings, and so forth need to be clear and effectively align with your purpose
