Before we overview a handful of data visualization techniques that you can begin to leverage when building effective dashboards, it’s important to take a step back and ask yourself who you want the dashboard to be effective for? In this post, we will provide insights in how to solve for this common frustration and provide tips to start using right away.
Before you begin building a data visual, get to know your audience first to increase effectiveness
Often times, data visualizations are ineffective because they are built for the wrong audience in mind. The perceived value of dashboards, too, gets lost due to poor communication with the end users.
Find out all you can about the audience you will be presenting the data visualization to. Meet with as many members of the audience as you can to learn their biggest pain points, their work goals, and ultimately, how the data visualization will help them make better decisions.
Don’t be afraid to learn about your audience, even if it’s just a few members of it, from a personal level. Consider setting up a lunch or a coffee meeting to just get to know each other. This will start to establish a trust between you and them and reveal helpful anecdotes that can be lost in the formality of the day-to-day. If you’re short on time or your audience is not on site, try as best you can by email or phone.
The more of a connection you can find with your audience, the better build you will have because you will know a bit more about how they tick and what they’re looking for from your visualization.
Common reasons why data visualizations are not effective
- Failure to understand what the business value could and should be for the available data;
- Not engaging dashboard users early in the process to learn what they want from the available data;
- Insufficient or ineffective communication between stakeholders, users, and the data architects;
- Poorly designed dashboards and charts without a path to behavior change
Without properly planning ahead of a build with these common adoption barriers, at least at some level, it will be hard for your visualizations to have long-term success.
While most dashboard architects do their best to consider these adoption barriers in their planning processes, it’s not uncommon to become sidetracked mid-process with other tasks and in-the-moment priorities that take the focus away from the prep work.
After all, as critical as this industry has become for businesses, the demand for trained professionals is much greater than the supply. Anyone working with data is stretched thin to do “all things data-related” even if it steers clear in the other direction of their core responsibilities.
All of the above points require interaction with people, and in an age where data is king, trying to convince people to engage in human stuff is a bit of a sales pitch that many folks either don’t want to do or, perhaps, know how to do it at all.
Let’s look at each one in a bit more detail.
Failure to understand what the business value could and should be for the available data
Any successful endeavor needs a clear and well-articulated goal — an objective to strive for. Don’t bother even starting to create visualizations without knowing what the desired outcome is. Again, it sounds like common sense but I’ve seen it far too many times where people dive right into the data without a concise vision.
Don’t fall into this trap, the work up front is worth the payoff. Building a dashboard against a proven failed strategy is like running in place — you put in all the energy and resources and get nowhere.
Not engaging dashboard users early in the process to learn what they want from the available data
Bridging business goals, data and users together is damn hard even when done by a pro. If we are missing the user element then we have data, we have a vision but we don’t have a means to actualize it effectively. Sure, you might get lucky from time to time by not engaging users, especially if it is a small user base, but not doing so is an open invitation to add risk and poor to no user adoption.
A user has needs and it’s part of your responsibility to know what they are and build a dashboard to meet those needs.
Insufficient or ineffective communication between stakeholders, users, and the data architects
Data quality is a behemoth breathing down the neck of the analyst. It is often a burden that is unshared and uncommunicated responsibility until it is forced into the light through user interaction. Time spent as an analyst is finding and formatting the data to analyze, limiting their bandwidth to perform a thorough analysis.
While not the only reason, getting an open channel of communication between all interested parties is a mechanism to address data quality issues early and devise strategies to mitigate or improve. There’s no logical reason to not communicate.
Poorly designed dashboards and charts without a route to behavior change
Solving the three previous factors will not be an easy feat if not successfully manifested into a user interface.
Business goals, data, and user needs are all brought together in the user interface, typically resulting in a complete dashboard.
When these three variables are not accounted for it’s due to:
- The dashboard having no connection with what the business is seeking to solve
- The dashboard has little or no intuitive actionable outcomes for the audience to walk away with
- The dashboard was not built with data visualization best practices in mind
The value of data visualization is rooted in early and often communication
At the end of the day, improving the effectiveness of a data visualization has nothing to do with the skills needed to build the product. Rather, it’s a balance of good communication practices that are fine-tuned over time. And because communication is not necessarily a top priority for those in the data industry, it’s a shared frustration.
And while not exactly earth-shattering or breaking news, good communication skills for many people are a learned and practiced behavior through the application of the process. Simply put, it takes time and patience. Yet, the better you get at the human-side of this process, the better and more effective your visualizations will be.
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