Database results are often complex and hard to understand, especially when they involve multiple tables, joins, filters, and calculations. Visualizing them can help you gain insights, communicate findings, and make decisions. But how do you choose the best way to visualize your database results? Here are some tips and examples to help you.
The first step to visualize your database results is to know your audience and their needs. Who are you presenting to? What are their goals and expectations? How familiar are they with the data and the concepts? How much detail and explanation do they need? Depending on your audience, you may want to use different types of charts, graphs, tables, or dashboards to convey your message. For example, if you are reporting to senior executives, you may want to use high-level summaries, trends, and key indicators. If you are working with analysts or developers, you may want to use more detailed and interactive visualizations that allow them to explore and manipulate the data.
In my experience a data environment needs to be configured so that it can reach many audiences. The same data can be leveraged in different ways for different consumers. Don't lock down to a particular set of visualizations. Build a view for a specific audience.
The next step to visualize your database results is to choose the right format for your data and your story. There are many types of visualizations available, such as bar charts, pie charts, line charts, scatter plots, heat maps, histograms, and more. Each one has its own strengths and weaknesses, and some are more suitable for certain kinds of data than others. For example, if you want to compare proportions or percentages, you may want to use a pie chart or a stacked bar chart. If you want to show changes over time, you may want to use a line chart or a time series chart. If you want to show relationships or correlations, you may want to use a scatter plot or a bubble chart. The key is to choose the format that best represents your data and your message, and avoid clutter, confusion, and distortion.
The final step to visualize your database results is to use the right tools to create and share your visualizations. There are many tools available, both online and offline, that can help you connect to your database, query your data, and generate your visualizations. Some of the most popular ones are Tableau, Power BI, Excel, SQL Server Reporting Services, and Google Data Studio. Each tool has its own features, advantages, and limitations, and you may need to try different ones to find the one that suits your needs and preferences. The important thing is to use the tool that allows you to create clear, accurate, and engaging visualizations that can be easily shared and understood by your audience.
Visualizing your database results can be a powerful way to enhance your data analysis and communication skills. By following these tips and examples, you can choose the best way to visualize your database results and deliver your insights effectively.
Not just tooling, but the data itself. The visualization will only be as good as the data provided. Too many times I’ve seen “all the data” being presented and the meaning gets lost.
Jason B said it incredibly well, visualization is only as good as the data sourced. From products like reporting and Business Intelligence and Analytics, you can get some incredible results in a wide variety of metrics and views from textual to 3D! However, the value of the results depends on the quality data it is based upon. The use of PowerBI is prevalent among many organizations that I work with, but can be challenged for effectiveness with the multitude of erroneous data that’s being collected and identification of ways to strip away the information that is not requested or required to find quality insights.
Additionally, while visualizations can be a powerful way to communicate data, it's important to complement them with contextual information and explanations. Often, visualizations alone may not tell the full story, and providing accompanying text or annotations can offer deeper insights and prevent misinterpretations. Clearly label axes, provide units for measurements, and include a concise summary of the main findings. This practice helps your audience understand the context and meaning behind the data visualizations, fostering better decision-making and understanding of complex datasets.
Centralize as much of your data as possible. It may live in disparate sources across your enterprise. Make sure you have efficient access to it all. And be prepared to change your mind as to how your data is visualized. As much as possible, leave room for change in the environment you create.