As we discussed previously, process and technology are intricately entwined. Not only are most process teams involved in supporting technology implementations—both large and small—but they continue to add more technologies to their proverbial toolkit.

One of the key technologies that process teams are using in 2022 is data visualization tools.

To learn more about applications, management, and challenges regarding data visualization tools APQC and BPM Next Gen conducted a poll. This article discusses the findings.

Value Drivers of Data Visualization Tools

Data visualization tools are typically connected to dashboards and better reporting—by ensuring timely access to information and insights. This helps drive better data-based, decision making and getting information into the hands of decision makers when they need it.

Top 5 Reasons for Data Visualization Tools

Figure 1

There are three themes spread across the top five purposes behind the implementation of most data visualization tools:

1. One Source of Truth—by developing dashboards organizations make data accessible and create a one-stop shop for insights and information. This also helps organizations move away from clunky monthly reports—often in Excel or PowerPoint—that are isolated in access restricted SharePoint sites or team folders.
2. Usability—data visualization tools make identifying insights easier by taking complex data and transforming it into easy-to-understand visuals. In addition to making information easier to understand, teams can also include storytelling components which provide context around the insights to ensure decision making isn’t conducted in a vacuum.
3. Timeliness—extraction, transformation, and loading (ETL), algorithms, and other forms of automation help put information in the hands of decision makers when they need it. This helps organizations overcome cycle time challenges and improving data-driven decision making.

Impediments to Value Realization

With even the best of intentions, most organizations face a few snags when it comes to implementation and adopting new tools. So, in addition to understanding the reasons for implementing data visualization tools, we also asked the respondents to identify the top challenges they faced around implementation (figure 2).

Top 5 Challenges to Implementation

Figure 2

There are four key themes spread across the top challenges around the implementation of most data visualization tools:

1. Training---because the goal is to take complex information and relay it in easy to understand visuals, it’s important to train those creating the visuals on good visual design. Additionally, you must ensure that the decision makers consuming the visuals understand how to interpret them and a basic understanding of data and analytics applications and outputs.
2. Flexible Design—one struggle with using a dashboard as a one-stop-shop for insights is the varying needs of different stakeholders. Some stakeholders need high-level information or prescriptive advice around the data. While others want to be able to drill into the details of the analysis and identify root causes behind performance variances.
3. Inconsistency—most process teams report that the tools and technologies that they use in their efforts are ad hoc or only standardized with specific departments. This is also true of data visualization tools, a lack of consistent tools used across the organization can either cause or exacerbate other challenges around implementation. Inconsistent tools make it hard for organizations to:
   
   • reduce additional investments to manage integrations with systems or databases;
   • avoid duplicative dashboards, reports, or information; and
   • create a consistent experience for end-users, which can result in rejection of the tools.
4. Garage in, Garbage out. Data quality is foundational to effective visuals. If the data is unreliable or not fit for purpose, the output of the analysis is unreliable insights that decision makers can’t trust, which ultimately reduces the use of data visualization tools. Data quality and ensuring the collection of the appropriate data should be part of the iterative methodology for dashboard and analytics projects.

Finding the Best Fit Tool

Overall, organizations tend to rely on purchasing their data visualization tools, rather than custom building them in-house. Which makes sense given the array of available tools and vendors ensuring tools are easy to use, have a wide variety of visuals (e.g., charts or graphs), include interactivity, support large datasets, and support end-users with training and tutorials. Consequently, respondents indicated that integration and usability of their data visualization tools are the primary considerations when picking a new tool.

• Integration—the goal of data visualization tools, as stated throughout this article, is to provide decision makers with easy to understand insights in a timely fashion. This means that organizations need to ensure their visualization tools are integrated with the organization’s data sources—its data bases and systems.
• Usability—because dashboards and other data visualization tools need to support an array of end-users, they have bake flexibility into them. The ability to drill in for analysis and interactivity gives stakeholders the self-service freedom to play with the data and turn it around to answer their questions.

Conclusion

Overall data visualization tools are an important component of process teams’ toolkit. They help drive objective decision making around the performance of work and related processes. If done correctly they not only create transparency around performance but make it easier for decision makers to identify opportunities for improvement. But to ensure that the business actively uses these insights teams need engage decision makers on their needs and applications for the outputs of these tools and leverage other tools to increase the cycle time of extraction, transformation, and loading data and analysis.