Designing an Effective Dashboard



Effective dashboard design can seem like a daunting task, but with the right know-how and guidance, it can be an incredibly satisfying experience. From choosing the right layout to adding widgets that make all the difference, there are so many ways to create an effective dashboard that will help you streamline day to day usage of your app or platform.

One notable concern of businesses is organizing datasets and making the dashboard more intuitive. Therefore, determining which datasets are essential to show and which can be placed in the background is imperative.

This article will provide an overview of how to maximize the effectiveness of a dashboard and leverage datasets in a meaningful way for users. However, before we get to the principles that drive dashboard design, let’s first understand the challenges of most traditional dashboards.

Challenges of traditional dashboards 

Data-driven companies can now access an array of metrics to gain business insights. However, the vast volume can make tracking metrics and informed decisions challenging. Here are some of the more critical limitations of conventional dashboards:

1. Too many metrics 

Too many metrics cluttering the screens results in users missing out on the most critical information. Modern businesses run on closely interlinked networks of infrastructure spanning different departments and involving tremendous data.

Timely and accurate insights are the key to making informed decisions, and with conventional dashboards displaying too much data, this becomes a difficult task.

2. Cluttered dashboards 

Despite all the information, dashboards often juggle to present a logical picture. In addition, due to space crunch, some planning is needed for some dashboards to determine what information will be displayed on the screen. 

When alerts pop up, it may be overwhelming for users to decide which data is essential. In addition, the data volume and complexity of data can swamp the dashboard interface, making it challenging for users to consume in an accurate and relevant manner.

3. Lack of intelligent prioritization

While many events or alerts from apps and infrastructure may be present in a dashboard, users perform their analysis by applying filters. Ideally, however, end users should not have to define the search, critical KPIs, or what’s expected.

When a user dictates to the dashboard precisely what data to show, it defeats the purpose of interactive design and product strategy, as dashboards should be able to analyze data independently.

4. Lack of correlation 

Most dashboards are designed to provide business intelligence and accurate, actionable insights and answer questions based on the designer’s perceptions of the questions asked.

However, actionable insights can be in any metric, and some insights are based on data across multiple metrics, even if there is no visible connection. They require a holistic view of all relevant information and the influence of that decision across the business. Where the dashboard’s missing data links delay or cause misguided decisions, they ultimately affect the organization.

How to determine important datasets to show

An efficient dashboard requires a proper blend of design and product strategy. Here are some questions to consider:

1) What information do you need to share?

Online dashboards collate data and convert it into actionable information by leveraging stats and metrics in a narrative that users can understand.

Understanding what information you want to share with your data and the message you want to convey to your audience is a good idea. This will help you choose the right design and product strategy.

2) Who is your target audience?

Have a clear understanding of your target audience for the trends and predictive insights – whether it’s a particular team in the organization or a group of corporate investors. Researching your audience will help you make informed decisions on designs that will make the most tangible connection with the audiences you’ll be addressing.

3) Do you want to analyze any particular trends?

As data collection requirements differ across businesses, a different design and product strategy are needed to match varying goals, aims, or topics. 

Depending on whether you want to communicate a specific trend about a particular dataset across a predetermined period, you could choose between: Column charts, Line charts, and Area charts.

4) Do you want to highlight your data composition? 

If your critical objective is to show how individual segments of data synthesize to form a whole of something, it’s vital to prevent your key message from getting diluted or – worse still – lost.

In these cases, the following elements should be at the heart of your product and design strategy: Stacked charts, Waterfall charts, Pie charts, and Map-based graphs (where the information is based on geographical location).

5) Is there a need to compare two or more values?

Most dashboard visualizations allow you to compare two or more trends or data sets. Your data should convey information rather than passively sitting on a spreadsheet or table.

If your main objective is to present comparative information, the graphs or charts that will make your message more insightful are: Spider charts, Bar charts, Bubble charts, Scatter plots, and Columns.

6) How critical are timelines for your business?

If you must extract time-sensitive data, use graphs or charts that provide an instant overview of numbers or comparative trends across a specific period. 

The tools with the most logical, data-centric designs, features, and functionalities needed for this task are Bar graphs and Dynamic line charts.

7) How do you want to present your KPIs?

When formulating your design and product strategy, understand how you want to present your KPIs, the information you want to extract from your campaigns or activities, and how your users will perceive the information.

This will help drive the success of your analytical initiatives and also help you determine how your dashboard resonates with your users. If needed, you may have to experiment with different formats till you hit the winning combination.

Qualities of a good dataset

Data is critical for any design, especially for a dashboard design. Therefore, the following data quality attributes should be taken into account when creating or selecting a dataset for design:

1. Accurate:

The information should be correct, i.e., it should represent a real-world scenario. Inaccurate information can lead to problems with severe consequences.

2. Comprehensive:

The data should provide the critical information needed to be usable. For example, you may need a customer’s first and last name but not the middle name.

3. Dependable:

The information shouldn’t contradict another information nugget in a different source or system. For example, a birth date should be consistent across all records. Any contradictions could lead to complications, including reputation and monetary loss.

4. Relevant:

Dashboard usability is also impacted by the relevance of its information. During the design and product strategy stage, consider if you need the knowledge and the value it adds to your user.

If you want a dataset to create a particular visualization or highlight specific functionalities, it should have the relevant types of fields. For example, basic demos often involve dates, so the data should have at least one date field. Likewise, maps require geographical data.

Nonetheless, not all data sets need all elements, so avoid cluttering your dashboard with irrelevant information – it only wastes time and money.

5. Timely:

Outdated datasets should be eliminated as they can affect the decision outcomes and lead to time, money, and reputation loss. For example, information gathered about the past hour is timely unless there’s more recent information.

6. It should be raw data:

Highly aggregated data provides a very high-level overview and isn’t suitable for analysis. For example, daily data will be more insightful than yearly data.

At the same time, some data sets can never be fully granular. For example, it’s unlikely to find a dataset with individual reporting of measles by address. However, in this case, region-wise monthly totals might be granular enough.

7. Dimension and measures:

Most data visualization requires both dimensions and measures. Including only dimensions limits you to calculating percentages, counting, or leveraging the Count of Table field.

Conversely, when using only measures, it’s not possible to break out the values. So you can fully disaggregate the data or work with the overall sum or average.

Much of the analysis of some dimension-heavy datasets like demographics involves counting or is percentage-based. Nonetheless, to generate an analytically rich data set for your dashboard, a few dimensions and measures are needed at the least.

8. Metadata or a data dictionary:

Ensure that your data sets are either well-labeled fields and members or a data dictionary. This allows you to relabel the data.

Data dictionaries (also called metadata) provide information about column names and members in a column. If you lose a data dictionary, your data set will be useless. So, when bookmarking a dataset, bookmark the data dictionary. And, if you’re downloading, download both and keep them together.

9. Usable:

As smaller data sets are easy to store, share, and publish, they have a better chance of performing well. In sum, the design and product strategy should ensure the dataset is wholesome and has the information needed. Even a small dataset can help with analysis.

Likewise, even if you find the ideal dataset, but it requires extra effort to clean up, it is unsuitable for your dashboard.

How to design an effective dashboard

Using a mindful approach towards your dashboard design, you can enhance their actionability with these guidelines:

1. Identify target audiences

When dashboards serve diverse users, i.e., users from different functional teams or various levels, the information relevant to one set may be insignificant to the other sets of users. Along with designing for a particular audience, dynamic, role-based dashboards leveraging technology to offer tailored information to diverse individuals can help address this challenge.

2. Make users a part of the design process

If the information on your dashboard is incorrect or not delivered correctly, it won’t be usable. Therefore, your dashboard design must factor in relevant metrics and dimensions pertinent to your audience’s day-to-day needs and not based on mere assumptions.

3. Stay within context

Contextual information helps to understand when action is required. Adding background data to key metrics provides users with a deeper perspective. To explain, the need to act is more significant when people see underperformance compared to a previous time, target, or industry average.

4. Tooltips to understand data

Since domain expertise and data literacy differs across end users, it’s essential to explain how to interpret the data to inspire action. Definitions and pop-up tooltips can help users understand your dashboards’ different data elements and charts.

5. Select the right charts

Complicated insights are likely to be overlooked. So, it’s critical to use appropriate chart options. Though several chart options exist, data tables are often overused in dashboards. They are suitable in some situations, like granular comparisons. However, bar charts are better for meaningful comparisons than pie or donut charts.

6. Pre-empt the question flow

Effective dashboards follow users’ intuitive curiosity and logical questioning. A clear visual hierarchy within the dashboard allows users to navigate content.

The inverted pyramid approach, in which you start with high-level KPIs and then gradually break them down by relevant dimensions, helps paint an accurate picture. Instead of frustrating dead ends, a clear content hierarchy and filtering options guide users toward insights that need attention.

7. Streamline/organize data on the dashboards

Busy, cluttered dashboards can confuse, overwhelm and scare away users before they even explore the data. Remove all clutter, making the insights more apparent and less challenging to locate.

This implies reducing the chart number or focusing on familiar chart types. It may mean removing junk from charts and/or avoiding excessive granular data.

8. Highlight key data

A lot of information in a dashboard may not be significant. This nondescript data often shields unexpected anomalies, trends, or critical patterns. Potential user issues or opportunities can be visually apparent by leveraging chart design or technology. For example, using conditional formatting to highlight underperformance or providing an acceptable range for a specific metric.

9. Suggest enhancements

Sometimes, though the insights may be apparent, the course of action may not be. In such situations, dashboards could recommend the desired actions based on specific insights, leveraging pre-defined business rules.

Where dashboards don’t support automated recommendations, then instructions about recommended actions based on different scenarios – such as significant downward or upward swings in key metrics – should be provided.

10. Review content regularly

Over time, newly-added content increases dashboard clutter. Likewise, neglecting the dashboard may cause stagnation and make it less relevant over time. Therefore, regularly review your dashboards to remain specific and relevant to your business. Critical dashboards should be audited every six months for relevance.

11. Mistakes to Avoid in Dashboard Design

At the top of the design and product strategy checklist should be avoiding errors. Here are some faux pas to watch when creating your dashboard:

12. Poor combination of charts

This is one of the most common mistakes. For starters, wrong chart orientation can affect readability. Sometimes merely flipping the chart corrects the chart orientation. Likewise, when presenting time-related variables, use charts with a horizontal axis and put time going left to right, and that’s it. For countries, products, sales channels, profit centers, and others, rotate the chart and use charts with a vertical axis.

13. Inadequate labeling in dashboards

Ensure proper labeling of charts to spare users the guessing game. Select labels carefully. However, too many labels on charts may hamper communication due to excessive information. Also, for time dimensions, use three or four-letter month abbreviations to avoid writing them tilted.

14. Using extra slicers

To boost dashboard engagement, use minimum slicers. For instance, use a slicer for up to five options. Over that, use a drop-down menu for each data field or dimension.

Alternatively, use charts in place on slicers. So, instead of a slicer that lists every state, convert the list of states into a chart which users can then utilize to filter the main chart. This prevents wasting space on slicers while displaying helpful information.

15. Poor consistency in using colors

Too many colors can be off-putting. Stick to two or three colors, and maintain balanced, de-saturated color schemes with light/dark shades to show variations within similar categories.

Also, ensure that the same colors represent similar things between different charts for consistency. Following a concept is good, i.e., green means positive, red means negative, and behind color usage.

16. Lack of variances

Variances help highlight differences, enabling users to quickly identify positive or negative changes. Avoid pie charts to show comparisons as comparisons get hidden in pie charts, and their scale is not perceivable. Likewise, gauges and odometers are not suitable too.

17. Confusing page layouts

Since people read from left to right and top to bottom, when designing effective dashboards, always put critical visualizations and KPIs to the left and at the top.

Specifically, when designing for senior management, you needn’t overuse charts. Even short comments – often missing in dashboards and reports – will suffice.

18. Unscaled charts

Scaling should be implemented whenever different visuals are put on a page or a dashboard because it helps users compare data points and interpret visuals better. In addition, the concept of the small multiples in scaling enables the designing of an interactive dashboard optimized for different screen sizes. 

In conclusion,

Effective dashboard design requires much thought and skill. However, with a mindful design and product strategy, you can optimize the design of your dashboard, increase engagement, and provide a user-friendly experience. Keeping the design simple and consistent and avoiding mistakes will help create a dashboard design that is both user-friendly and effective.

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