Analytical Dashboards: Powering Decisions with Detailed Data
In recent articles, we have explored the different types of data dashboards in more detail. So far, we have investigated a little more about Strategic Dashboards and their Operational counterparts. If the former are tools that help the company monitor the performance of its strategies and the latter help to monitor regular operations and processes, Analytical Dashboards help to check different causes that could lead to performance below plan and/ or identify opportunities to improve current performance.
In other words, analytical dashboards are essential tools that provide a detailed and in-depth analysis of data, with the aim of, through complementary analyses, helping to identify opportunities for improving performance, solving recurring and common problems, identifying the cause of specific problems, or even finding insights. Unlike strategic and operational dashboards, which focus on high-level monitoring and monitoring of daily operations, respectively, analytical dashboards focus on analyzing data from known situations and contexts (which may or may not happen) to increase the speed of reaction. They allow quick and easy visualization of important information for more informed and assertive decision-making.
Additionally, analytical dashboards also make it easier to visualize opportunities, such as cost reduction, evaluating market trends, and replicating positive behaviors. Once built, these dashboards simplify complex analyses and present results clearly and quickly, reducing the time needed to make decisions and increasing the company’s ability to react. Take, for example, a user who always seeks to compare the results of certain categories with the average result of all categories in order to know which categories are performing below average. This entire analysis process can be replaced by a graph or set of graphs in order to avoid the hassle of downloading the data, analyzing it, and making a decision.
In general, analytical dashboards have several characteristics that make them easily identifiable. Among them, we can mention the following:
• Capabilities to perform detailed and investigative analyses and thus allow a detailed analysis of data, from the most aggregated to the most specific level.
• Interactive Visualizations that allow the user to filter a visualization by clicking on another one, facilitating cross-exploration of data, and providing a deeper understanding.
• Vertical and Horizontal Analysis that allows you to understand performance at different levels and make comparisons to identify areas for improvement.
• Color palettes that seek to quickly highlight who is performing below what was desired and who is performing above what was planned.
• More sophisticated visualizations (such as Sunbursts and Sankey Diagrams) are more commonly found in analytical dashboards, precisely because they allow you to visualize more data and data in a less usual way
Note that this does not mean that these characteristics only appear in Analytical Dashboards, but that, in general, they can be seen in these types of dashboards. Regarding the main types of analysis, it is common to find, among other types of analysis:
• Time Series Analysis: To track trends over time.
• Distribution and Correlation Analysis: To understand the dispersion of data and the relationships between different variables.
• Segmentations and Rankings: To identify the best and worst performers.
• Geographic and Composition Analysis: To understand regional variations and the composition of different metrics.
• Conjoint Analysis: Using multiple graphs to find insights from comparing different levels of data.
• Comparison Analysis: Using graphs that allow you to compare a category with others, a category in relation to the whole, or a segment with other competitors.
Regarding the target audience, analytical dashboards are useful for a wide range of professionals, including technical team members, data analysts, data scientists, business analysts, and team managers, among others. Even because they are products aimed at specific teams, it is always important to count on the collaboration of the client team to understand their true informational needs before creating the dashboard or planning its design — only in this way is it possible to avoid design errors that lead to the product not being successful, or being used.
Speaking of common design mistakes in Analytical Dashboards, some common errors include:
• Excessive Complexity: Making the interface too complex can make it difficult to understand the data.
• Lack of Focus on Relevant KPIs: Including too many indicators can scatter attention.
• Lack of Interactivity: The absence of interactive elements can limit data exploration.
To avoid these errors, it’s important to maintain a clean and intuitive interface, focus on the most relevant metrics, and ensure the ability to explore the data in detail.
In the next article, we will finish our first moment of exploring Dashboards, seeking to understand a little more about the specificities of Tacit Dashboards. Until then, tell me: are the differences between the types of Dashboards increasingly clear? Can you think of good practices for each of the different types of Dashboards? Tell us a little about your experience.