Designing Dashboards That Drive Decisions
By Tom Pallas & Shawn Reed
Last month’s Perspectives emphasized that leaders must begin analytics design by first determining the decisions they need to make. This month’s article turns to the execution side of dashboard development—how can an organization design mission-aligned dashboards that are intuitive, automate them to scale, and ensure leadership adoption?
As organizations develop dashboards, they should always keep the dashboard’s original purpose in mind. Often, teams conflate operational dashboards with executive dashboards. Executive dashboards should supply the minimal amount of information needed for leaders to make a decision and avoid metric overload.
As a key test of a dashboard’s effectiveness, if a dashboard development team demonstrates the dashboard to leadership and leadership needs someone to walk them through how to use it, the dashboard probably isn’t intuitive enough. When designing the dashboard, the team should also keep in mind that each leader is different and processes information in different ways. Finally, the team and leadership should place a strong emphasis on the value of data quality and governance—data owners often need assistance with data management, which ultimately improves quality, supports dashboard design, and builds leadership trust.
Automation as the Hidden Enabler
In organizations that are beginning the analytics journey, staff analysts often spend disproportionate amounts of time pulling data, building spreadsheets, developing slides, and staffing the slides for review. By automating dashboards, analysts are freed to spend more time solving the problem. As a result, analytics automation is the next key tenet of analytics development. Organizations should ensure development includes scheduled data refreshes, standardize the data transformations, and reuse the visual template for consistency. They should also work with the data owner(s) to incorporate changes and help with data management. These actions will keep dashboards automatically updated with trusted information (for which the data source is a subject matter expert) and in a format that leadership is comfortable with.
Technology adoption and change management is also a vital aspect of automating analysis. When automating dashboards, analysts may feel like they are being replaced. Leaders should address these concerns by involving these analysts in the dashboard’s creation. This will help analysts understand the datasets and problems and become invested in the development; as a result, they will understand how they can reposition themselves to analyze, interpret, and advise.
A Practical “Start Small” Roadmap for Agencies
Implementing analytical tools and broader digital transformation can seem daunting. Best practice for an organization is to start small with the following repeatable, low-risk approach to dashboard use case development.
Phase 1: Focus
Pick one mission critical decision area supported by high-quality, accessible data
Intentionally limit scope with clear requirements
Phase 2: Build
Build a dashboard that answers: “What is happening?”
Validate definitions with stakeholders
Phase 3: Sustain
Automate refresh and routine analysis
Document assumptions and logic into a user manual
Phase 4: Expand
Only after trust and adoption exist
Layer in deeper analysis or additional use cases
Dashboards arm leadership with faster insight delivery, more consistent metrics, and reduced single-point-of-failure risk. Ultimately, they enable faster, higher-quality decisions. As your organization builds the initial dashboards, leaders and staff will begin to have an “aha” moment: they will understand the intent, trust the dashboards, and recognize the multiplier effect they have for the organization. As more people buy in, the use case ideas will proliferate, creating the right moment to define a more comprehensive analytics and digital transformation strategy.