5 reasons data visualizations are ineffective
 
 

5 reasons data visualizations are ineffective

Creating useful and usable data visualizations is a critical part of the BI journey, but all too frequently, these dashboards have poor user adoption. Understanding common adoption barriers can help you plan for long-term success for your new data visualizations.

 

5 common reasons why data visualizations are ineffective

1. Dashboard architects don’t understand the end user.

2. Failure to understand what the business value could and should be for the available data.

3. Insufficient or ineffective communication between stakeholders, users, and the data architects.

4. Poorly designed dashboards and charts without a path to behavior change

5. Little to no socialization across the user group to encourage user adoption.

 

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.  

 

Data and dashboard architects have become critical 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”, but it’s still critical to spend the time up front to mitigate these challenges.

 

Here’s how:

 

Increase the effectiveness of data visualizations by getting to know your audience first.

Data visualizations are often ineffective because they are built for the wrong audience in mind. The perceived value of dashboards is lost due to poor communication with the end users.

 

The data visualization design process starts with learning about the audience that will be using the dashboard. 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.

 

Get to know a few members of your audience on a more personal level if you can. Consider setting up a lunch or a coffee meeting to just get to know each other. This will start to establish trust 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, leverage email or phone.

 

The more you connect with your audience, the better the dashboard – you’ll know more about how they tick and what they’re looking for from your visualization.

 

Understand what the business value could and should be for the available data

Any successful endeavor needs to start with a clear and well-articulated goal — an objective to strive for.

 

Take the time up front to define the desired outcome before you touch the data. What challenge are you trying to solve and what decisions do you need to make? Outline what information you need to make informed decisions to get to your business goal.

 

Once you have a goal established, look to the data to guide your decisions. A well-designed dashboard will feature the key metrics you need to achieve your goal.

 

The work up front is worth it when you see the payoff. Building a dashboard without a clear vision of its use and value is like running in place — you put in all the energy and resources and get nowhere.

 

Open communication between stakeholders, users, and the data architects

Data quality issues can feel like a behemoth breathing down the neck of the analysts on your team and it’s a burden that frequently goes unnoticed. The time an analyst spends on finding and formatting data is limiting their bandwidth to perform a thorough analysis.

 

Create 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. Your data architects will create the necessary infrastructure to provide a pipeline of clean, usable data. Data is the foundation of your data visualization platform, it makes sense for the entire team to be working together from the start.

 

Design dashboards and charts with a clear route to behavior change

Business goals, data, and user needs are all brought together in the user interface, typically resulting in a complete dashboard. To be successful, your dashboard should have:

 

Clear connection to the challenge the business is trying to solve.

Intuitive, actionable outcome for the audience to walk away with.

Design that includes data visualization best practices

 

When your user is looking at the data visualization, it should be clear what they should DO with the information. What are the next steps? What processes need to change moving forward? If your user can’t identify action items, your dashboard has failed.

 

A change management strategy improves user adoption.

By engaging with the users from the start of the project, you’re setting up your dashboard for success, but that’s not the final step to ensure user adoption. Leveraging a formal change management strategy that outlines how users should expect to engage with the new asset will also improve the odds that the dashboard will be leveraged on an ongoing basis to support real business decisions.

 

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 little to do with the skills needed to build the product. 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 that leads to poorly designed and adopted dashboards.

 

Good communication skills are a learned and practiced behavior; simply put, it takes time and patience. The better you get at the human-side of this process, the better and more effective your visualizations will be.

 

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