You’ve been told at least a hundred times on LinkedIN, vendor emails and marketing conferences that data visualization is a great way to gain actionable data from various data points in your business. In the face of large amounts of information, data visualization can drive insightful conclusions, identify gaps in information and assist in finding trends and patterns. When you’re challenged with limited resources and time restrictions, one of the biggest hurdles can be making a decision on what tool is right for you. That’s why I’m going to break down two of the key players in the data visualization game, Canva and Tableau, highlighting ways to identify which solution is right for you.
Before you even look at a side by side comparison, it’s important to start by jotting down a few key details about your business. This is actually a great way to look at any potential addition to your business tool stack.
- Who do you have on your team?
Every data visualization tool is made with an ideal user in mind. If you’re preparing to invest in a tool, you should make sure you steer towards who you envision using the tool as well as where and how fast you’d like the person or team to use it and expand their current capabilities. While one tool may offer more benefits than the other, the cost difference can be significant. If your team is not ready to use the full functionality list, this can be an excessive expense where the simpler tool would have done the trick at a fraction of the cost. Focus on your team strengths and who the key players are in decision making. Is this for analysts, leadership, managers?
- What are your data sources?
Consider all the places actionable data lives in your company. What are you using for email? Where are you collecting orders? Are you marketing on social media? Different tools may have limitations with which third party tools you can integrate with so compatibility will be key in reducing setup costs and time. While there are workarounds for non-native integrations, these can be time consuming and not as cost effective. Be sure to make note of what data extract options you do have with each data source (API, Cloud, Excel Sheets, etc).
- What kind of information are you gathering and how do you intend to use it?
Following up on what your data sources are, take a catalog of what data you have available. By starting with a clear outline of all the attributes you have available, you can consider what you’d like to leverage, and start to envision strategic growth initiatives that may be attainable depending on the visualization tool you decide on. It’s also important to consider who you are presenting to. What’s the best way to present the data they need as well as point out something they may not be aware of?
Now that you know what you have, what you’re looking for and what your strengths and limitations are, you can quickly identify the tool that’s for you. A side by side comparison of the two makes it easy to identify the key specifications and how they differ. I’ll highlight 10 important features that are most likely relevant to your business and will follow up with additional specifications that may be useful for you.
Heavy on Data vs Heavy on Design
Side by side it’s easy to quickly identify the strengths and differentiators of each tool. Tableau is heavy on the data customization, source options and overall custom/ad hoc capabilities. This is a marketers and analysts DIY report system for descriptive, diagnostic, prescriptive and predictive visualizations. Canva, offers that same level of flexibility only, with design. This is for more straightforward data, from less sources, that is needed to present conceptual information (family tree, site map, org charts) and simpler visualizations (trends and comparisons). Canva also provides a lot of design functionality not specific to data but useful in presentations like backgrounds and stock images.
In conclusion, flexibility, data sources and end recipient of your visualizations is the key to defining which tool is right for you. Be sure you are aligning this with where you are in terms of resources and how actionable you can be before diving into a more expensive visualization option, or one which will not allow for scalable growth of your analytical capabilities.
New to data visualization? Check out my Intro to Data Visualization post.