by Rahil Virani
What are data dashboards?
Imagine driving a car without a dashboard. You wouldn’t know your speed, fuel level, or engine temperature, making it nearly impossible to drive safely or efficiently. In much the same way, organizations need a way to monitor their operations and performance—this is where data dashboards come into play.
Stephen Few defines a dashboard as: “A data dashboard is a visual display of the most important information needed to achieve one or more objectives, with the data consolidated and arranged on a single screen so the information can be monitored at a glance” (Few, 2004). Dashboards are used in many fields like business, healthcare, education, and government to help people quickly understand data, track progress, and make informed decisions. For researchers, dashboards are also valuable, making it easier to analyze large, complex datasets. By organizing data visually, dashboards help researchers spot trends and key details faster, supporting quicker and better decision-making.
Importance of Data Dashboards in Research Domains
Using data dashboards has several benefits for researchers:
- Real-Time Data Monitoring: Dashboards allow researchers to track and visualize data as it is collected, enabling them to spot trends and anomalies early on. This is particularly useful in fields like healthcare, environmental science, and clinical trials, where timely data analysis can be crucial for the success of a study.
- Efficient Data Management: Dashboards can handle large datasets from different platforms by integrating multiple data sources into a single interface. Researchers can customize their views to focus on specific metrics, which helps refine their hypotheses or adjust experiments based on real-time feedback.
- Enhanced Collaboration: Research often involves teams working across different locations or institutions. Data dashboards provide a centralized, accessible platform where all stakeholders can view the same up-to-date information. This fosters better communication and ensures that everyone is aligned on the progress and findings of the study.
- Improved Decision-Making: By presenting complex datasets in charts, graphs, and summaries, dashboards enable researchers to make informed decisions more quickly. They reduce the cognitive load of sifting through raw data, allowing researchers to focus on drawing insights and moving their research forward.
- Transparency and Reporting: Dashboards make it easier to share research findings with stakeholders, including funding bodies, regulatory agencies, and the public. The visual nature of dashboards allows for clearer, more transparent reporting, essential for credibility and accountability in research.
Examples of data dashboards
UW-Madison has some excellent examples of research data dashboards. Below are a few key tools:
- Research at UW-Madison Dashboard
Developed by DAPIR, this tool gives a comprehensive view of ongoing research, highlighting trending topics, recent publications, awarded grants, and faculty leads across departments. - Stream Monitoring Data Dashboard
Provides nutrient and temperature data from stream stations across Wisconsin, helping guide conservation efforts and resource management decisions. - Wisconsin Cover Crop Citizen Science Data Network
Displays data from Wisconsin farmers on cover crop and soil health practices, offering an interactive map to explore specific field data. - SARS-CoV-2 (hCoV-19) Genomic Dashboard
Tracks COVID-19 variants through genomic sequencing and displays data by county, using pie charts to show variant distribution.
Resources at UW-Madison
If you want to create a dashboard or you’re beginning to analyze your data, take a look at the Resources for Analyzing & Visualizing Data for your research. You can also find support from the Data Science Hub, the Center for High-throughput Computing, and DoIT’s Research Cyberinfrastructure group. These organizations can answer general questions, while other resources cater to more specific needs.
Rahil Virani is currently pursuing his Master’s Degree in Information Science. Through his role as a Research Data Analyst & Initiatives Assistant for UW-Madison Libraries, he provides technical and data support for researchers and teams, particularly focusing on MINDS@UW and Research Data Services (RDS). His passion lies in improving library digital services through automation and data-driven research, aiming to enhance efficiency and reach to the diverse UW student community. His work involves gathering data and delivering actionable insights to support strategic planning and targeted initiatives