Create Custom 360 Reports with Visuals and Dashboards (Beta)

How many columns should you include in a report? The number of columns in your table depends on the data being presented, your goal, and the medium (online vs. print). Some best practices when building reports are:

• Avoid overwhelming the user and include only the columns necessary to understand the data or complete a task.

• When building a report for print, consider the physical page width and font legibility.

How should you configure your vertical bar graph for clarity? A vertical bar graph is excellent for comparing a numeric value (like Cost or Count) across distinct categories (like Assignee or Project Name). To ensure your chart is effective and easy to read, consider these best practices:

• Limit the Number of Bars: Avoid overwhelming users with too many bars. Use the Max Bars Displayed setting to focus on the most relevant data (e.g., the Top 10 or 15 categories).

• Sort Your Data Logically: Since categories on the Horizontal Axis (like 'Name' or 'Assignee') often have no natural order, sort the chart by the Bar Measure (i.e., numeric value) in Descending or Ascending order. This makes it easier to compare values by identify highs and lows.

• Use Clear Formatting: Use the Display Units (e.g., 'Thousands', 'Millions') to simplify large numbers on the Y-axis, and consider turning the Show Value Labels toggle ON if precise values are important to your audience.

When is a horizontal bar graph the right choice? A horizontal bar graph is good for comparing a numeric value (like 'Cost' or 'Count') across categories that have long labels (e.g., 'Assignee' or 'Project Name'). The vertical layout gives ample space for the text, preventing it from being angled or cut off. Some best practices when building horizontal bar graphs are:

• Prioritize Readability for Labels: Use this chart type specifically when your Vertical Axis (category) labels are long. This is its primary advantage over a vertical bar graph, as it gives more space for longer labels..

• Sort by Value, Not Name: For clear insights, set the Sort by setting to your Bar Measure (the numeric value) and select Descending or Ascending. Sorting categories alphabetically is rarely as useful as seeing them ranked by value.

• Limit the Number of Bars: Avoid cluttering the chart, which can make it harder to read. Use the Max Bars Displayed setting to focus the user's attention on a manageable number of items, such as the "Top 15" or "Bottom 10" categories.

When is a line and bar graph the right choice? This "combo" chart is ideal for showing the relationship between two different measures over the same category, which is almost always time (like 'Month', 'Quarter', or 'Year'). It allows you to plot a primary measure as a bar (e.g., 'Actual Cost' or 'Actual Hours') and a secondary, related measure as a line (e.g., 'Estimate at Completion' or 'Actual Production Rate') on the same visual.

Some best practices when building line and bar graphs are:

• Use a Dual Vertical Axis: The bar measure (e.g., 'Cost' in dollars) and the line measure (e.g., 'Count' in units) will almost always have different scales. Turn the Use Dual Vertical Axis option on so each measure has its own Y-axis (one on the left, one on the right).

• Assign Measures Logically: Use the bars for volume or magnitude data (like 'Unit Quantity'). Use the line to represent a trend, rate, or average (like 'Unit Cost' or 'Unit Sales').

• Use Time on the X-Axis: The Horizontal Axis should represent a continuous progression, with time ('Received Date' or 'Si) being a common and effective category. This shows how the two measures trend together over time.

• Keep it Simple and Clear: Always Show Legend so users can easily distinguish the line measure from the bar measure.

When should you use a vertical stacked bar graph? Use for tracking both the total of a measure and its composition across different categories. It is most powerful when used to show a part-to-whole relationship. For example, you could track the total RFI Count (the bar’s total length) for each RFI Assignee (the category) and stack by Status (the component parts of the bar). 

Some best practices when building vertical stacked bar charts are:

• Limit Stacked Segments: To avoid a cluttered and unreadable chart, use a Stack by or Legend field that has a limited number of distinct values (ideally five or fewer). Too many colors can make the chart seem cluttered.

• Compare Categories and Subcategories: This chart's best use case is to compare categories and their internal breakdown. For example, you could show the distribution of submittals by Responsible Contractor (the main category) and then stack by Status (the subcategory).

• Consider a 100% Stacked Bar: If your main goal is to compare the proportion of the segments (the percentage breakdown) rather than the total, a 100% Stacked Bar (where every bar is the same height) is a much clearer and more effective visual.

When should you use a horizontal stacked bar graph? Use for tracking both the total of a measure and its composition across different categories. It is most powerful when visualizing data that has a part-to-whole relationship. For example, you could track the total RFI Count (the bar’s total length) for each RFI Assignee (the category) and stack by Status (the component parts of the bar). 

Some best practices when building horizontal stacked bar charts are:

• Limit Stacked Segments: To avoid a cluttered and unreadable chart, use a Stack by or Legend field that has a limited number of distinct values (ideally five or fewer). Too many colors can make the chart seem cluttered.

• Compare Categories and Subcategories: This chart's best use case is to compare categories and their internal breakdown. For example, you could show the distribution of submittals by Responsible Contractor (the main category) and then stack by Status (the subcategory).

• Consider a 100% Stacked Bar: If your main goal is to compare the proportion of the segments (the percentage breakdown) rather than the total, a 100% Stacked Bar (where every bar is the same height) is a much clearer and more effective visual.

When is a donut graph the right choice? A donut graph is an ideal visual to display part-to-whole relationships regarding project health. It is best used when you need to show the status breakdown of a specific tool (e.g., 'RFIs by Status' or 'Punch List Items by Status') to identify bottlenecks quickly. Unlike a pie chart, the center "hole" allows you to show totals, giving users immediate context about the relationship.

Some best practices when building donut graphs are:

• Limit Categories for Clarity: Donut graphs become unreadable if you try to display every single trade or cost code. A better option is slicing the donut by Status (e.g., 'Open', 'Closed', 'Draft', 'Void').

• Show Totals in the Center: The defining feature of a donut graph is the empty center. Turn the Show Total toggle on to display the Total Count (e.g., '150 Total RFIs'). This allows users to see the total scope of work immediately, while the ring shows how much of that work is completed versus outstanding.

• Use Colors: Color carries meaning. Match your slice colors to the status message. For example, when visualizing 'Observations by Status,' use Red for 'Overdue,' Yellow for 'Ready for Review,' and Green for 'Closed.' This allows users to assess risk without reading the labels.

When is a scorecard the right choice? A scorecard is an effective visual for displaying a single, critical Key Performance Indicator (KPI) for at-a-glance monitoring. It provides a snapshot of a metric's current status (e.g., 'Total Revenue', 'Open Cases', or 'Avg. Uptime'). Its ideal position is at the top of a dashboard.

Some best practices when building scorecards are:

• Focus on One Key Metric: Each scorecard tile should represent one, and only one, primary value. Its power is in its simplicity. Avoid cluttering it with secondary information that belongs in a different chart.

• Provide Essential Context: Always pair your Primary Value (the main KPI) with a Secondary Value for comparison. A number by itself is not insightful.

• Use Clear and Concise Labels: Use the Title to clearly name the KPI (e.g., 'YTD Sales') and use Display Units (like 'Thousands' or 'Millions') to make large numbers easy to digest.

• Use Conditional Formatting: Apply color to signal performance. Set up rules to change the value's color (e.g., green for good, red for bad) based on whether it's meeting a target. This allows viewers to assess status without even reading the number.

When is a line and bar graph the right choice? This "combo" chart is ideal for showing the relationship between two different measures over the same category, which is almost always time (like 'Month', 'Quarter', or 'Year'). It allows you to plot a primary measure as a bar (e.g., 'Actual Cost' or 'Actual Hours') and a secondary, related measure as a line (e.g., 'Estimate at Completion' or 'Actual Production Rate') on the same visual.

Some best practices when building line and bar graphs are:

• Use a Dual Vertical Axis: The bar measure (e.g., 'Cost' in dollars) and the line measure (e.g., 'Count' in units) will almost always have different scales. Turn the Use Dual Vertical Axis option on so each measure has its own Y-axis (one on the left, one on the right).

• Assign Measures Logically: Use the bars for volume or magnitude data (like 'Unit Quantity'). Use the line to represent a trend, rate, or average (like 'Unit Cost' or 'Unit Sales').

• Use Time on the X-Axis: The Horizontal Axis should represent a continuous progression, with time ('Received Date' or 'Si) being a common and effective category. This shows how the two measures trend together over time.

• Keep it Simple and Clear: Always Show Legend so users can easily distinguish the line measure from the bar measure.