Tag Archives: visualization


A dynamic dashboard in Excel allows you to visualize changing data in real-time or near real-time to gain insights and track key performance indicators (KPIs). It allows non-technical users to see their constantly updating data in an easy-to-understand format without needing to regularly refresh or update their reports manually. Creating a dynamic Excel dashboard involves the following steps:

Plan your dashboard – The first step is to plan out what type of data you need to display and the key metrics or KPIs you want to track. Determine things like the data sources, the frequency with which the data will update, the visualizations needed, and how the dashboard will be accessed and updated. Sketch out on paper how you want the dashboard to look and operate.

Setup data connections – You’ll need to connect your dashboard workbook to the underlying data sources. For Excel, common data connection types include connecting to other worksheets or workbooks within the same file, connecting to external data stored in text/CSV/XML files, connecting to external databases like SQL Server, and connecting to online data sources through OData web queries. Use things like Excel’s built-in Get Data tools and functions like power query to automatically import and structure your data.

Automate data refreshes – For a true dynamic dashboard, you need the data visualizations to update automatically as the underlying data changes. This is done by setting up scheduled data refreshes using Excel’s Data Refresh tool. you can refresh the queries and pivot tables on a schedule linking to external data. For example, you may want to refresh the data daily at 6 AM to pull in the previous day’s data. You can also trigger refreshes manually.

Design interactive visuals – The dashboard should display your key metrics through various interactive visualizations like charts, gauges, maps, pivot tables and more. You can use Excel’s wide range of built-in chart types as well as more advanced types through add-ins. Ensure the visuals are formatted properly for readability and aesthetics. Add relevant titles, labels, data labels, colors, tooltips etc.

Filter and slice data – Enable users to filter the visuals by parameters to drill-down into subsets of the data. For example, allow filtering a chart by region, product, date range etc. You can add slicers, filters or combo boxes linked to pivot tables/queries for this.

Add KPIs and metrics – KPIs are critical data points that need to be prominently displayed and tracked over time. Use gauge charts, traffic lights, meter charts etc to visualize KPI values against targets. Add relevant background colors, icon graphics and call-outs. Power BI also allows building KPI scorecards from Excel data.

Format for mobile – Consider if dashboard needs to be accessed on mobile screens. Use responsive design principles like auto-fitting charts, larger text, fewer/simpler elements on mobile views. Explore tools like Power BI for reports accessible on any device.

Protect and share – Password protect or restrict access to the file if needed. Publish Power BI dashboards securely online. Share workbook links for read-only external access. This allows distributed teams to monitor metrics remotely.

Test and refine – Thoroughly test all the interactivity, refreshing, formatting on different systems before implementing the dashboard for actual use. Monitor for issues, get feedback and refine design iteratively based on user experience. Consider automation add-ins for enhanced formatting, lay-outing and governance.

Maintain and evolve – As needs change, the dashboard should evolve. Streamline the maintenance processes by version controlling the file, documenting procedures and changes. Train others to extend, refresh or make modifications as required. Monitor dashboard usage and determine if new metrics/visualizations need to be added or obsolete ones removed over time.

This covers creating a robust, dynamic Excel dashboard from planning to implementation to maintenance. Some key advantages are easy creation without coding for business users, familiar Excel interface, interactive data exploration within the sheet itself and mobility across devices. With latest tools in Excel and Power BI, sophisticated dashboards can now be built directly in Excel to drive better business decisions through data. Regular refinement keeps the dashboard aligned to the evolving needs.


Microsoft Excel: Excel is one of the most widely used tools for data visualization. It allows users to easily create basic charts and plots like bar charts, pie charts, line graphs, scatter plots, histograms etc. using the built-in charting functionalities. Excel supports a variety of chart types that help identify patterns, trends and relationships during the initial exploration of data. Some key advantages of using Excel include its ease of use, compatibility with other Office tools and the ability to quickly generate preliminary visualizations for small to moderate sized datasets.

Tableau: Tableau is a powerful and popular business intelligence and data visualization tool. It allows users to connect to a variety of data sources, perform calculations, and generate highly customized and interactive visualizations. Tableau supports various chart types including bar charts, line charts, scatter plots, maps, tree maps, heat maps etc. Additional features like filters, calculated fields, pop ups, dashboards etc. help perform in-depth analysis of data. Tableau also enables easy sharing of dashboards and stories. While it has a learning curve, Tableau is extremely valuable for detailed exploratory analysis of large and complex datasets across multiple dimensions.

Power BI: Power BI is a data analytics and visualization tool from Microsoft similar to Tableau. It enables interactive reporting and dashboards along with advanced data transformations and modeling capabilities. Power BI connects to numerous data sources and helps create intuitive reports, charts, KPIs visually explore relationships in the data. Some unique features include Q&A natural language queries, AI visuals and ArcGIS Maps integration. Power BI is best suited for enterprise business intelligence use cases involving large datasets from varied sources. Its integration with Office 365 and ability to publish reports online make it a powerful tool for collaborative analysis.

Python (Matplotlib, Seaborn, Bokeh): Python has emerged as one of the most popular languages for data science and analysis tasks. Key Python libraries like Matplotlib, Seaborn and Bokeh provide functionalities to create a variety of publication-quality charts, plots and graphics. These help gain insights through visual exploration of relationships, trends and anomalies in datasets during EDA. Python libraries enable higher level of customizations compared to Excel or Tableau. They also have extensive documentation and an active developer community supporting advanced use cases. Jupyter Notebook further enhances Python’s capabilities for iterative and collaborative data analysis workflows.

R: Similar to Python, R is an extremely powerful and versatile programming language tailored for statistical computing and graphics. Base plotting functions and various contributed packages like ggplot2, lattice, shiny etc. in R enables sophisticated and publication-ready data visualization. R supports a wide range of static and interactive plots including histograms, scatter plots, box plots, density plots, maps, networks etc. It is especially useful for statistical and computational exploratory analysis involving modeling, forecasting and other predictive analytics tasks. R is a popular choice in academic research due to its statistical capabilities.

Qlik: Qlik is a business intelligence platform to explore, visualize and analyze enterprise data. Its associative data model engine allows users to intuitively interact with data using selections, filters and motions across multiple associated analyses. Qlik supports creating dashboards, apps and stories to visually represent key metrics, relationships and patterns in the data. Key features like expressions, flows and multi-dimensional analysis make Qlik extremely powerful for comprehensively exploring large datasets. Its ease of use, security and deployment models position it well for self-service analytics and governed data discovery in organizations.

So Excel, Tableau, Power BI, Python/R, and Qlik are some of the most common tools utilized by data scientists and analysts for the initial exploratory data analysis and hypothesis generation stage of a project. They enable visual data profiling through charts, graphs and dashboards to understand trends, outliers and statistical relationships present in datasets. The right choice often depends on factors like dataset size, required functionality, collaboration needs, existing tool expertise and deployment scenarios. A mix of these tools is also embraced in modern analytics workflows for seamless data exploration.