Desire paths are unplanned trails that form when users circumvent the carefully constructed paths before them in favor of routes they perceive to be more apparent, convenient, or safer. While you may stumble upon a desire path at a park, college campus, or anywhere that receives a lot of foot traffic, they also form in products that rely on any type of user experience including dashboards and data visualization. Whenever the options you’ve laid out for your stakeholders are in conflict with their unique objectives or desired outcomes, you’ve opened yourself up to them finding an alternate way to achieve their goal – a desire path.

We’ve all been there… you’ve spent days, weeks, or even months meeting with stakeholders to gather their requirements. You’ve created the perfect automated workflow to gather and consolidate the data that will be needed to populate the future tool you’ve collectively decided to build. You’ve engineered a beautifully designed dashboard that allows users to unlock valuable insight they would have never had visibility into otherwise. Only to be asked, “can this data be exported to Excel?”.

This post seeks to help you understand why data visualization desire paths may be forming at your organization, covers three different types of data visualization desire paths, and provides tips for closing the gap between your deliverables and your stakeholder’s desired products.

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Understanding whether data visualization desire paths are forming

One thing that’s always stuck with me from a marketing professor I had in college is that, “perception is reality”. I’ve built hundreds of individual data products containing thousands of unique data visualizations. I’ve wrote three books on Tableau. I’ve supported the biggest companies across dozens of industries and use cases. None of that experience or credibility matters if I can’t listen to the current audience, connect with their objectives, and deliver something that they perceive helps them achieve those objectives.

I have to reprove myself every single time, so my first tip is to embrace the fact that user feedback and their potential push toward data visualization desire paths is not personal. I happen to like this because data visualization does have an aspect of subjectivity and it requires us to constantly be improving our soft skills of dealing with diverse audiences with diverse needs.

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So how can you find out if data visualization desire paths are forming at your organization?

Export to Excel If, as mentioned previously, stakeholders are consistently asking for a means to get to the raw data, that’s a telltale sign that the deliverable you’ve created isn’t meeting their needs and they are trying to find an alternative. Unfortunately, most of the stakeholders are not exporting the data for the purposes of creating their own data visualizations, so we have underserved them because they’re not able to take advantage of the benefits of data visualization.

Monitor usage reports While it can be painful to see your hard work is not being valued, understanding usage is critical to understanding if your deliverables are resonating with stakeholders for current and future projects. Assuming your audience is genuinely trying to use data to make decisions and take action, it is much better to know when your dashboards are not hitting the mark. To help, Playfair+ Premium members can leverage the Tableau Cloud Usage Swift for a holistic view of what’s driving user adoption.

User Acceptance Testing UAT and/or shadowing firsthand how stakeholders are using the tools that you build is an extremely efficient way to discover if you’ve met their requirements. Literally looking over their shoulder while they use the tools often reveals what’s most important to them, whether they’re understanding how to use the dashboard to drive decision making, and if further iterations will be needed.

For the remainder of this post, we’ll cover three different types of desire paths that can form and tips for getting around each one.

 

Don’t design for the sake of designing

Admittedly as someone who appreciates design, user experience, and art, it is a pet peeve of mine when stakeholders say something like, “we don’t need it to be pretty”. There are real benefits of spending extra time on design within the practice of visual analytics including adding authority, building an internal brand, improving engagement, and making insights more memorable. But as with everything, there is a tipping point where forcing design can have a counterproductive effect and create a desire path. Like the following image, if you design just to design, your end users will inevitably ignore your good-intentioned design and find the shortest path between point A and B.

The best designs and user experiences are the ones stakeholders don’t notice. You never want excessive design elements to distract from your data visualization deliverables serving the objective at hand. For example, instead of using bright, unnecessary colors throughout an entire dashboard, use one or two colors tastefully which can serve a practical purpose while also adding a nice aesthetic.

Streamlining Dashboards: Minimizing Chartjunk with Icon Design

Similarly with user experience, it’s best if end users find dashboards intuitive to navigate without having to ask questions or stop to think about it. Sure, it’s nice when they say something like, “how did you make it work like that?!”, but the ideal state of dashboards is they just work.

 

Ensure data literacy aligns with data visualization choices

Other data visualization desire paths form when chart selections do not align with your stakeholders’ ability to understand them. It’s no wonder when end users request access to the raw underlying data when they literally cannot interpret the complex box-and-whisker / Sankey diagram / Leapfrog / etc. in front of them. It will take time and perhaps direct shadowing sessions to ensure your chart selections are adding value to the audience at hand, but this is something that should be considered from the start. No matter how valuable you perceive the chart to be, it will not be worth anything if decision makers are not able to derive insight from it and drive action.

In the meantime, here are some ideas for helping align data visualization choices with your audience’s level of data literacy.

Wireframing As part of our Decision-Ready Data framework, I’ve always recommended creating relatively low effort, highly flexible, dashboard wireframes to review with clients before engineering anything. There will be feedback. This is an ideal time to learn which chart types are resonating before investing too much time in execution.

Data toggle Toggle all of the things. We’ve covered toggles extensively at Playfair+ because they provide the best of all worlds. If you have stakeholders at a variety of skill levels and/or requesting access to data, why not provide easy-to-use buttons that navigate between every desired state?

Tableau Tip: How to Do Better Sheet Swapping

Data literacy assessment Investing in a formal evaluation of your team’s ability to interpret data can do wonders for your team members and organization. Not only will it help you meet your audience where they are, but it will improve team member morale and help your company take advantage of the exploding amount of data available to us today. I must recommend data literacy assessments (royalty free link and recommendation) from the leaders in evaluating data literacy, Data Literacy themselves.

 

Understand macro and micro requirements

Are you giving your customers oranges after they asked for apples? Or in a visual analytics practice, are you building the dashboard equivalent of a Ferrari, when in the use case at hand, the audience would have been happier with a Honda Civic? You won’t know the answer to this unless you have established clear, what I call, macro and micro requirements. Macro requirements can be considered the high-level must haves like the purpose of the dashboard, the types of analytics it will utilize, and important fields that can be used as filters. Micro requirements are smaller, negotiable requirements like specific features and secondary use cases that may only serve individual needs.

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When you’re not on the same page as your stakeholders regarding macro and micro requirements, a data visualization desire path is sure to form. This happens because dashboard users will be trying to ‘fit a square peg into a round hole’ by using the dashboard to meet an objective it was not set up to achieve.

The best way to document macro and micro requirements, of course, is to interview your stakeholders and confirm their needs. During this process, I typically end up with one to three macro requirements and 10 – 15 micro requirements. It can take some thought to differentiate between the types of requirements and determine which make the cut. For macro requirements, I recommend offering a consultative push to iterate what you believe the dashboard objective to be and confirm they agree rather than asking a question that may have an open form answer. For micro requirements, particularly when interviewing groups of stakeholders, I usually include the requirement if I’ve heard the same need from at least two stakeholders.

 

It’s always better to have some type of path than none

In conclusion, I encourage you to remember it’s a great thing if your audience is trying to use data to drive decision making and are engaged enough with your work that they are creating data visualization desire paths to begin with. If this is the case, don’t give up on them. Implement the tips within this post to understand why they are happening and lessen the gap between your deliverable and stakeholder objectives.

We always have a better chance at causing some type of positive action when we team up. We need decision makers to make the calls that lead to action. We need feedback to continuously hone our craft. We need to help our colleagues reduce the time to insight, increase the accuracy of those insights, and make analyses more engaging. As a visual analytics practitioner, I believe that the data alone is not a deliverable and it is always better to have some data visualization path to help the world take advantage of data visualization than no path at all.

Thanks for reading,
– Ryan

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