How to choose the right data visualization tools for your apps

If you develop applications that present data to users, you’re likely to have requirements to present a graph, chart, dashboard, or other data visualization embedded in the application. This capability not only helps users better understand the data and discover insights, it also improves the user experience. When people see and experience well-designed data visualizations, they often use the application more and be more satisfied in using it.

As a developer, you might be excited to develop charts and graphs with code, and there are plenty of charting frameworks you can use to customize data visualizations. But before you embark on approaches that require frameworks, libraries, and coding, I recommend looking at data visualization tools that have embedded analytic features; they include the functionality to easily develop the visualization in the tool and then embed and deliver it directly through a web or mobile application.

Embedding analytics can be a very powerful approach to enhance applications when experimentation around the visualizations is important and where visualization platforms meet business requirements.

Another key benefit of using data visualization platforms is that data scientists and subject matter experts can participate in the application development process. Instead of having them write requirements for a software developer to translate into code, the visualizations can be iterated over by a group of people who best know the business need, the data, and best practices in data visualizations.

Benefits: Why you should use data visualization tools

Let’s look at some example use cases for embedding data visualizations where rapid development and experimentation is required.

Embedding analytics into an enterprise system, such as a dashboard for sales managers that is embedded in the customer relationship management (CRM) application and includes data from the CRM and several other data sources.
Developing customer-facing mobile and web applications where a simple chart or graph can drive a desired user interaction. Think of a stock-trading application that charts stocks on an investor’s watch list and highlights ones near their low prices when it’s potentially the right time to buy.
Media organizations and others that publish content may want to leverage data journalism, where an article is developed around a data set and one or more data visualizations, where data and analytics are the foundation of the story.
A similar use case are marketing infographics, where infographics are developed with data visualizations and then embedded in websites and other marketing tools.
For businesses that are trying to be data-driven, this may be the opportune time to select a data visualization platform that can be used to develop analytics and also embed them in enterprise or customer-facing applications.
For organizations that are already using data visualization tools, there may be a need to extend a visualization with custom integrations and functionality to manipulate or process data through a workflow.
Entire customer-facing applications may be data visualizations for data product and services. This is common for data, financial services, insurance, and e-commerce businesses where the data is the product. In these cases, you really want a rapid data visualization platform to develop the product, as well as the flexibility to embed it in another system such as a content management system (CMS).

What’s different about data visualization is that the requirements, design, and functionality required are likely to be highly iterative. As more stakeholders and users learn more about the data and what insights are useful, they are likely to modify the experience, design, and functionality requested.

That’s why, even though visualization libraries may be easy to use for the developer, they may not be an optimal development approach for embedding analytics where frequent iterations is required. This is especially the case in journalism and marketing where the goal is to let users design, develop, and publish data visualizations without requiring support from developers and technologists. This approach of embedding analytics is expected to grow to $52 billion, according to a Market Research Future study.

Criteria: How to choose data visualization tools with embedded analytics capabilities

Many data visualization tools—including Tableau, Microsoft Power BI, Looker, Sisense, GoodData, and Qlik—offer data visualization embedding capabilities. If your organization already uses one of these tools, start there. If not, try some out. Once you select a tool, you’ll want to do a series of prototypes to validate capabilities, ease of use, and operational considerations.

Here’s a detailed list of considerations:

Do the chart types meet business needs? Data visualization tools compete on the types and flexibility of their chart types. If your organization wants a box and whisker plot, make sure the tool has this chart type.
How easy is it to integrate? Review whether the platform’s approaches to embed analytics into applications meet business needs and are easy to implement. For easy integration, there should be simple embed codes to drop the visualization into HTML, but you should also review the APIs in case additional flexibility is required. For example, if you want to pass parameters from the application to the data visualization, you’ll want to make sure this level of API is exposed. In addition, many applications require some form of authentication, so validate that the platform’s integrations easily work with your single-sign-on services.
What are the flexibilities on layout and device compatibility? When you design a data visualization that is consumed using the underlying platform, the visualization can take advantage of the full screen and use the platform’s tools to responsively adjust for mobile device layouts. When you embed the visualization, you need to review how it fits and interacts in your application’s layout.
Is the security configurable for the required end-user entitlements? If you are building applications where different groups and users require access to different views of the data, review how the platform enables row-level security. Verify that the user login can trigger the data entitlements and that visualizations properly adjust for the accessible data. You also want to see whether the platform has admin-level tools to look at visualizations as different users and to validate whether entitlements and visuals are configured properly.
Does it perform fast enough to be embedded in an application? When a data visualization is accessed by an user in a visualization platform, there is a higher tolerance of slower performance because the users are more sensitized to the quantity of data and complexity in analytics. By contrast, users accessing applications where data visualizations are a small component of the user experience are likely to have greater expectations on performance. In addition, if the visualization is embedded on a public-facing web page that is search-engine-optimized, reviewing performance is critically important to ensure page rank is not penalized by a slow visual.
How “real time” are your application’s requirements? Related to performance is whether the platform enables real time access to data sources or whether computing analytics on cached data is sufficient. There’s often a trade-off among real-time data availability, performance, and cost, so having the controls to change from real-time to scheduled updates and validating performance is required for larger data sets.
Are the development capabilities flexible and scalable? Once you plug a visualization process into an application development cycle, you want to see how well it fits your requirements for instituting version control, managing development, deploying test and production workflows, testing practices, and configuring with any continuous integration tools.
Can you extend the platform with interactivity and workflow? Once you can embed the visualization, verify whether it meets business requirements. Some functionality will be platform capabilities such as changing sort orders, selecting what metrics are used in visuals, choosing which columns to display a table, or switching between chart types. Other times, you want to extend the functionality, especially if you want users to be able to update the underlying data. Explore the platform’s full capabilities and future technical directions because some of the data visualization platforms allow developers to extend visual capabilities using APIs.
Is the platform’s costs and pricing model aligned? Most data visualization platforms have upfront costs and per-user charges. If you’re going to embed a visualization and provide access to thousands of users, make sure the costs are aligned with the application’s business model. This is particularly important when visualizations are embedded in customer-facing applications because the data visualization platform’s per-user charge can be a significant percentage of your costs.

But one main consideration is whether business stakeholders are willing to define user experiences and designs that match the platform capabilities. This is often a benefit because these platforms are designed with best visualization practice baked into the platform. But if stakeholders are locked in to a specific design and functional requirements, it may make it difficult to implement with a data visualization platform. Under these circumstances, teams should look at one of the many data visualization libraries to develop the visuals.

Whatever approach or platform you select, embedding analytics is a powerful method for integrating and sharing data and insights with users.