For about as long as people have tried to measure stuff, they’ve been trying to present those measurements in ways that are easy to understand. That’s because measurements and data don’t have meaning without some sort of context. Data visualization exists to provide data with context so that you can actually use it. Good data visualization leads to good data utilization. After all, why would you use a report that doesn’t make sense to you?
Of course, the nature of using data has changed. The vast majority of MarTech and SalesTech products have some kind of native data visualization. The amount of products in the average tech stack of sales and marketing organizations is growing steadily, so the number of data stores is growing as well. Many analysts are taking a more holistic and heuristic approach to data analysis and this kind of approach is extremely difficult when your data is spread across a bunch of places. With many organizations prioritizing and preaching data utilization or being “data-driven,” achieving it has never been harder.
Enter the dedicated “Data Visualization Tool,” a product that connects to your data stores and/or tech stack products and allows you to create visualizations of your data. As mentioned earlier, context is what gives data meaning. The purpose of a data visualization tool is to give your data as much context as possible. Data pulled into one place is more accessible and usable when it's distributed across products, platforms, and storage centers. It turns data into usable information and information is power. Data visualization (aka data vis) tools are what create that power.
The Options for Data Visualization
For starters, you could always stick with the status quo. MarTech and SalesTech products are now usually expected to have some sort of data visualization capability, so it’s perfectly reasonable for you to keep your data separated across your tech stack. You could do all of the analysis you need without having to go through everything involved in adding a new product to your tech stack. However, it’ll probably be more time consuming and tedious than if you were using a data visualization tool.
You’d expect there to be a vast selection of platforms and products. However, the space has been dominated by products like Salesforce’s Tableau platform and Microsoft’s Power BI product suite. Those products, and similar offerings, are known for their extensivity and robust capabilities, but tend to be complicated for the end user. They usually require some administration and technical know-how in order to be implemented successfully for a particular use-case, and are pretty expensive to boot.
Those products were and are great for the organizations that can accommodate those requirements, but the organizations that can’t or don’t still have a need for a tool that aggregates and visualizes their data. Most organizations don’t need the deep customization that legacy products offered and, to a point, required. They’re better served by tools that are more straightforward and focused on usability, like these:
- Google Data Studio
Data Visualization Tool Must-Haves
The primary purpose of a “data visualization tool” is laid out in the name. The category defines the basic expectations for the products in it. Obviously, they all will enable you to create dashboards, reports, metric-widgets, and other visualizations of your data. But, there’s more to data visualization than the ability to visualize data. Here’s what a data visualization tool needs to have to set itself apart:
- As many integrations as possible: The average modern tech stack has tons of products which means tons of data stores. The more integrations a data visualization tool has, the more likely it is to cover your tech stack.
- Custom metrics and calculations: Even after you pull your data in, it might need more context in order to have meaning. A data vis tool that lets you manipulate your data to do custom calculations and/or create custom metrics helps you further contextualize your data. That makes your data more usable. Look for features titled something like: “Custom Queries” or “Custom Reports” or “Calculations.”
- Dashboard design control: In all likelihood, there are parties outside your company or organization who need to see your data. You should be able to provide visualizations that meet your brand standards, in addition to being aesthetically pleasing in general.
- Data aggregation without an integration: Even if a data visualization tool has tons of integrations, there’s a slim chance that it covers all of the important data stores in your tech stack. You should be able to connect any product in your tech stack to your visualization tool.
- Accessibility and access control: The people in your organization will need vastly different things from a data visualization tool. Some might need control over the way data is aggregated and presented; others might just need to be able to see that data. At the very least, a license shouldn’t be required to at least view the dashboards and visualizations in your tool.
Our Choice: Databox
Databox is the offering leading the charge for the “new wave” of data visualization. In our opinion, they’re the ones setting the standard for offerings looking to make noise in the space. It’s clear that their focus is on providing a data vis tool that makes data visualization easy for the end user. They’re a company that’s extremely tapped into the MarTech and SalesTech landscape and they certainly listen to their user-base. So, they’re quite familiar with the tech stacks, needs, and processes of companies overserved by legacy platforms. Their offering definitely reflects that familiarity.
It starts with all of the ways you can customize the look and feel of their dashboards, which they call “databoards”. You have near-full control over the way reports are laid out on a dashboard, report design, and even some of the colors that are used in visualization. At higher subscription levels, you can even brand databoard imagery and backgrounds.
Databox demonstrates further flexibility with all of the different ways it lets you aggregate, manipulate, and work with your data. Their ecosystem of integrations may appear thinner than competitors. However, integrations with Zapier and Google Sheets make that ecosystem essentially all-inclusive. Zapier is an extremely easy and common integrator that many businesses are using to connect products in their stack. Organizations turn to Google Sheets as a place to work with data collaboratively. After all, the spreadsheet isn’t going anywhere anytime soon. This approach to distributed data is for-sure a unique one, but it’s one that more directly aligns with how companies actually function in the real world.
Once your data stores are actually connected to the product, you can mess around with your data using the product’s “Query Builder” and “Data Calculations'' features. These features make it easy to provide different and/or deeper context to your data. While the Query Builder is a great place to start, if it’s a playground then “Data Calculations'' would be an amusement park. Data Calculations lets you create custom metrics from two of any of the data sources you have connected to Databox. It’s these features, combined with integrations to Zapier and Google Sheets, that really give you the ability to take a holistic approach to data visualization if you used Databox. This approach is reinforced by the accessibility of collateral within your Databox instance/portal, as they make it easy to share databoards with people who might not have a license and offer multiple user plans at all of their subscription tiers.
Their excellence is definitely centered on the “data visualization” and “data utilization” of it all, but it ends there. Administration and account management are cumbersome, as settings for each are pretty fragmented. Settings for billing, user management, overarching account management (agency accounts), and individual account/instance management are separate from each other despite the ways all of those things connect. Additionally, there are often limitations to the specific data that the product pulls from connected sources. For example, the product doesn’t seem to be natively able to pull in data from custom HubSpot properties. Databox also falls relatively short with its subscription level-based limits, particularly regarding the number of databoards you can create and the number of data sources you can connect. A data visualization tool’s value comes from the amount of data it aggregates and visualizations it has within it. Databox makes it easy, but the limits before the $99 non-agency plan are well...definitely limiting.
All in all, Databox is an excellent choice for a data visualization product, but might be a little cumbersome from a tech-management standpoint. It’s super easy to use if you’re actually creating data visualizations, aggregating data, and/or conducting analysis. Any time you implement a new tool used for oversight and analysis, there’s going to be some change management to deal with. It’s important to consider how easily you can implement and manage a tool like that. However, given how intuitive it is to use the product for its intended purpose, any administrative difficulties will probably be worth it.