10 Rules to Make Your Dashboard Design Effective, Intuitive, and Compelling
They say that a picture is worth a thousand words.
As a B2B SaaS company, your customers rely on you to help all their users make data-driven decisions in near-real-time. To enable this, your customers should be able to access decision intelligence in a visually intuitive manner in a matter of 5 seconds or less. If it takes too long, the insight becomes stale and useless.
But customer data dashboards churn hundreds of thousands of data points at any given instant. How can they be designed to pop up the exact nugget of insight at the right time for the right user?
The answer lies in effective dashboard design.
In this blog, we share with you the top 10 rules of dashboard design that will make your clients fall in love with your SaaS customer data dashboard.
Let’s dig in!
#1 Clearly Define the Dashboard Purpose
What can you do with a dataset?
Statistically speaking, the answer to the above question is a set of infinite possibilities. This is because there are countless ways in which any data at hand can be analyzed, wrangled, and visualized.
But, only a handful of insights are going to be useful for your customers. Hence, one of the first things that you must do while designing a customer data dashboard is to map the decision-making needs of your customers.
Once that is done, you have attained the holy grail of effective dashboard design – i.e. the purpose for which it is being built. Tying your dashboard to its core purpose will streamline the dashboard design while emphasizing the critical features, processes, and data operations that matter the most to your customers.
For example, an alcohol brand selling premium white wine and a nonprofit organization helping people to overcome drinking addiction will both use the same primary dataset – i.e. the alcohol consumption data in their target market. But the purpose of using the data and the manner in which they will use it will be completely different.
Here at Verb, we allow the dashboard purpose to inform the design at every step of the way. It’s one of the principal ways in which we make customer data dashboards meaningful, goal-driven, and result-oriented.
#2 Ensure Centralized Data Sourcing
The dashboard is like a vast ocean. Rivers and streams of data from various corners meet in this ocean. Careless sourcing of data can easily lead to redundancies, data duplication, mismatched datasets, and undue frustration for users.
Diverse, disparate datasets need to be unified, de-duplicated, blended, cleaned, and transformed to create a single pipeline of analytics-ready data. Expecting your customers to have an in-house development team to shoulder this burden will only make your product unappealing and cumbersome to them.
Instead, make a centralized data lake or a data warehouse a part of your dashboard design. To source all data and store it in the data lake in an easily retrievable manner, the Extract-Load-Transform (ELT) process must be completely automated. Hence, one of the core principles of effective dashboard design is to centralize data sourcing and end-to-end automation of an integrated data pipeline that is analytics-ready.
#3 User Segmentation and Performance Metrics
Let’s say that you have successfully made your customer data dashboard purposeful, and have also centralized the data sourcing process. Yet, your users are still fumbling to make sense of the data.
Why do you think that is the case?
While there could be any number of reasons behind this, a common mistake that dashboard developers make is to box all the users in the customer organization into a single bucket. It is as effective as advising all patients with a headache to take the same medication.
Instead, a sound design principle is to perform out-of-the-box segmentation by categorizing users according to their data needs. When you do this, your users are inevitably served better with the right data and insights within the shortest possible time. Make your dashboard design modular and loosely coupled so that various user segments can be assigned to the specific modules and components that best address their data needs. Thereafter, find ways to measure the performance of your dashboard across each of the user segments.
#4 Bring Clarity and Consistency
Using a standardized and uniform framework is one of the most underrated yet rewarding exercises in dashboard design. For starters, it is extremely critical that you adopt and implement clear and consistent naming conventions, formatting templates, and truncating rules. Similarly, it is important to make sure that you have a uniform dashboard layout and flow, and that the various elements in it are prioritized in the same order everywhere.
Speaking of visual consistency, one way to ensure the same is to use grids. Grids are invisible lines on the screen on which the various components of your dashboard design are placed. They ensure perfect alignment and streamlined content placement with minimal effort.
Grids give your design the legs to stand upon and the skeleton to fall back on. Whether you are creating “widgets” to hold the data, the visualization elements, or the dashboard controls, use a responsive design all across. Rationalize the entire visual composition of your dashboard design using grids such that your dashboard can easily represent a massive amount of information in a seamless and well-structured manner.
#5 Appropriate Visual Representation for All Data
Choosing the best representation for data in client-facing analytics can make or break your SaaS product.
Most of your users will not be well-versed in statistics or data analysis. Hence, they might not always be in a position to select the right chart to represent the data at hand. Instead, your SaaS platform should automatically pick the best visualization options and quickly represent the data to them in a meaningful and digestible format.
For example, the most commonly used visualizations are column, bar, and line charts. Data visualization and dashboarding tools usually follow a set of best practices when designing these charts, some of which are provided below.
- Time is usually represented on the horizontal axis,
- It is a good practice to always sort the columns in decreasing order of value,
- Line charts should ideally show a maximum of 5 values, and
- Bar charts should ideally show a maximum of 7 values.
With pie charts and donut charts, it is important to keep in mind that it can be challenging to visually differentiate between two chart fragments whose areas or angles of elevation vary only by a small margin. Similarly, color-coding chart fragments with similar colors (e.g. carmine red and maroon) can make them hard to read. Gauges, 3D charts, and any other fancy chart types that are hard to be grasped quickly are best avoided.
#6 Right Dashboard for the Right User
As you might already know, there are 3 broad categories of data dashboards – i.e. operational, analytical, and strategic. As a B2B SaaS provider, you must ascribe the right kind of dashboard to the right user in your customer organization.
Operational dashboards include time-sensitive reports, such as status reports or user alerts about any abnormalities to be addressed. They are of the most value to front-end operators like salespersons, client-facing teams, storekeepers, and so on.
Analytical dashboards compare current data to past performances to analyze trends and help forecast. They are best suited for mid-level managers and executives.
Strategic dashboards display critical KPIs to help leaders analyze performance over a given period. They are of the maximum value to CXOs, department leaders, and so on.
#7 Establish a Clear Data Hierarchy
At the heart of an optimal dashboard layout is a clear data hierarchy.
It is a lot like how Gmail automatically organizes your inbox. You see the most important messages from your contacts first, while promotional emails and social media-related emails are bucketed in separate tabs. Emails from unverified senders and spam messages are completely hidden from you.
In the case of customer data dashboards, the underlying design logic should determine which data a user sees first, which data second, which data third, and so on. The most digestible and decision-oriented data should be visible right at the top and take up the maximum space in the dashboard design.
Upon opening a dashboard, the user should immediately get a pulse of the most important and recent developments that have happened since she/he last logged in. This means that the most crucial piece of information needs to be presented first along with the right context. This approach saves a substantial amount of the users’ time that is spent gleaning mission-critical insights.
Once the most critical pieces of information are processed by the user, she/he can then explore any supporting information to gain better context and also dive deeper into the granular-level details if required.
#8 Actionable, Purpose-built Reports for Diverse Use-cases
Dashboards are not the be-all and end-all of insight generation. They are meant to initiate and kindle a deeper probe into the aspects of the decision-making process that truly matter. They are incomplete without in-depth reports that are actionable and purpose-built for strategic decision-making.
Allowing for ad-hoc reports apart from regular, routine reports must be factored into the dashboard design process. It is good practice to use the right color coding, make all reports print-friendly, and enable reports to be shared ubiquitously without violating authorization rules.
Customers would ideally want to obtain reports on-demand for both granular and eagle’s-eye-view of the current state of affairs. As a SaaS company, you can use it as an avenue to showcase your SaaS platform’s ability to craft meaningful and result-oriented data experiences.
#9 Co-creation and Collaboration with the Customer
One of the biggest challenges in ensuring that customer dashboard designs are purposeful and useful is the fact that customers are traditionally not a part of the dashboard design journey.
However, we at Verb are firm believers in collaborating with clients and co-creating stellar dashboards with their feedback and support. After all, what better way to provide transparency and insights to your end-users than to allow your customers to collaborate on its creation?
By converting your customers into key contributors to the data story that you are building for them, you can increase their engagement significantly and embed trust in the entire process. Along the way, you can demonstrate first-hand how your SaaS dashboard aspires to become a valuable business asset for your customers.
This engagement can later be measured through improvements in the overall usage rate, indicating how often your dashboard has been used for making decisions that affect the bottom lines of your customers.
#10 Seamless Performance Monitoring Dashboard
Great dashboards are designed to allow for continuous monitoring of response times and loading times. If the loading time is too long, the dashboard loses value, and its users will most likely turn to other tools to find the information they need.
Another aspect of performance monitoring of customer data dashboards is to keep a tab on the effectiveness of the various design elements. The goal here is to constantly stay on top of what users across various levels and segments are saying about their data experience. If the needs of any user group are not being met, it is time to revisit the design and tweak the various elements to see what works.
Lastly, it’s necessary to analyze the time needed to manage any ongoing maintenance that is required on a regular or ad-hoc basis.
Verb banks upon these principles to help SaaS companies deliver 100% no-code data dashboards to their customers.
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