By Ben Mellaerts, Sophie De Waele
Many organizations have embraced data reporting to foster a more data-driven culture and democratize data. However, ensuring maximum impact and usability for the organization’s investments requires more than just focusing on the data. In this two-part series, we will guide you through a comprehensive end-to-end process of building user-focused BI dashboards, addressing challenges in data reporting adoption. From empathizing with end-users to delivering training and support, learn how to transform reporting into a valuable asset.
In this first part, we will concentrate on adoption initiatives that should be implemented in the early stages of reporting development (before launching an MVP). The second part will focus on adoption initiatives that should be implemented after launching the MVP. In real life, this rarely is a sequential process. However, for the sake of clarity, we present it as a step-by-step guide.
1. Empathize with end users
Understanding the needs and perspectives of the future end-users is the starting point of building successful reporting. Additionally, understanding the direct or indirect impact the insights will have on decision-making processes is key to prioritizing the most important data and visualizations.
Gather requirements by asking questions during interviews with target users. This is also an excellent time to challenge assumptions and misconceptions about data.
In addition to interviews and discussions, observation can be used to understand processes and gather ideas where a dashboard might come in place to assist end users. Explore any existing reporting products or tools that your users may currently be using. Dive into activity logs to gain insights into their data consumption behavior. Consider the following aspects:
- Which data has already been visualized?
- Why are end-users adopting or not adopting existing dashboards?
Understanding what data is already in use can help you identify patterns and preferences among your end users. Investigating the reasons behind user adoption or rejection of existing dashboards can provide insights into unmet needs.
2. Map requirements
After getting a solid grasp on what the end users need and expect, the next step is to effectively map these requirements in order to translate them into a comprehensive and actionable plan.
Make sure that reporting is the right solution
Evaluate whether a dashboard is the optimal data product for meeting the identified needs. Consider alternative data products such as machine learning models, A/B tests, and others.
Check for data availability and quality assessment
Assess the data landscape. Check for available dimensions and metrics to determine if the necessary data is accessible to fulfill the identified user requirements. Additionally, ensure that the data quality meets the standards necessary for generating reliable insights.
Integrate previous efforts
Recognize and integrate the aspects of the data landscape that have already been covered in the past. Evaluate how previous reporting initiatives have addressed user needs and identify any gaps that still need to be fulfilled. This integration ensures continuity and coherence within the data reporting framework.
Define user personas
Acknowledge the diverse target groups that might have varying requirements and priorities. Consider the distinct needs of different user segments, ensuring that each group can access the relevant data and insights crucial for their decision-making processes.
Define the obvious objectives that each dashboard aims to achieve. Additionally, carefully decide on the priority Key Performance Indicators (KPIs) that align with the strategic objectives of the organization, ensuring that the most critical metrics take precedence in the design and presentation of the dashboard.
3. Develop or extend an information architecture
The process of building an information architecture is key for a comprehensive data exploration experience.
Create an ideal number of dashboards and a coherent hierarchy
Too few (and thus too extensive) dashboards can lead to information overload while having too many dashboards can create confusion. Consider the user personas and their requirements to decide on the optimal number of dashboards necessary. A hierarchical structure may be suitable for instances where there are layers of insights and varying levels of detail needed by different user groups.
Aim for a MECE overview of dashboards
Strive to maintain a structure that is both mutually exclusive and collectively exhaustive (MECE). Achieve this by categorizing the dashboards in a manner that avoids overlap and duplication of information, while simultaneously ensuring that all essential aspects and data categories are thoroughly covered.
4. Build a template (+ components) and focus on consistency
To achieve an optimal user experience, dashboard branding plays an important role. Creating a template that follows existing reporting initiatives and corporate identity guidelines will make end users feel more comfortable and familiar with the reports.
Implement best practices in visualization
These include using the right type of visualization for the data (line charts to show evolutions over time, scatter plots to show the relationship between two variables,...), using color and contrast effectively, keeping the visualizations simple, labeling the visualizations clearly, and testing the visualizations with users.
Streamline navigation for intuitive user interaction
Ensure that users can effortlessly access and explore different sections of the dashboard, enabling seamless transitions between various data sets and visualizations. Integrate user-friendly navigation features that promote easy exploration and enable users to delve deeper into the data without encountering any navigational complexities.
5. Build wireframes and validate with end-users
Building wireframes that incorporate the components of the dashboard template, along with dummy or real data, will help validate design choices and gather feedback from end users.
Construct wireframes with template components
Incorporate the defined fonts, colors, rounding techniques, and other visual elements outlined in the template to create a structure that aligns with the standardized dashboard design. Utilize intuitive navigation features within the wireframes.
Utilize dummy or test data
Populate the wireframes with sample datasets that mimic the structure and characteristics of the expected data, allowing end users to gain insights into how the dashboard would function in real-world scenarios. This approach enables users to better understand the potential implications and benefits of the dashboard in their decision-making processes.
Validate design choices through user feedback
Facilitate user feedback sessions that encourage end users to interact with the wireframes and provide their insights and suggestions. Actively solicit feedback on the usability, intuitiveness, and overall effectiveness of the dashboard design, while also encouraging users to share their perspectives on the visual appeal and data representation.
6. Prototyping and testing
Prior to the finalization of the dashboard design, rigorous testing procedures must be implemented to verify the functionality, usability, and overall performance of the dashboard. Implement user testing sessions to gather direct feedback from end users, enabling the identification and resolution of any potential usability issues or design inconsistencies. By prioritizing thorough testing, you guarantee the delivery of a robust and user-friendly BI dashboard that effectively fulfills the intended objectives and requirements.
In this first part of our series on developing user-focused BI dashboards for business adoption, we've delved into the crucial early stages of reporting development.
Join us in the upcoming second part, where we'll explore adoption initiatives post-MVP launch. Stay tuned for more insights and practical tips on transforming reporting into a valuable asset for your organization!