Best Practices when Building a Databoard

BEST PRACTICES

Best Practices using Visualization Types

After aligning your team around the appropriate KPIs too include in your reports, the next major piece to consider as you’re building out your Databoards is which Visualization Type will work best for each Metric. 

For every Metric, there are multiple Visualization Types that  could work, but we have to consider what the intent is behind this Datablock being added to the Databoard and what visualization will ultimately tell the intended story with the data.

Pro Tip: The most popular Visualization Types include the Number Block, Line Chart, Pie Chart, Table, Progress Bar, Pipeline and Text Block.

IN THIS SECTION

Number vs Line/ Bar Chart

A Number Block is designed to display simple data. This Visualization Type is compact, clear and precise. Line/ Bar Charts are better used to highlight growth over time or patterns in your data. 

You should aim to have at least one Number block and at least one Line/ Bar Chart per Databoard to allow for both basic and advanced analysis.

Line Chart vs Bar Chart

Line Charts are great when you want to visualize patterns over a longer period of time and compare data to a previous period. The Line Chart visualization is familiar to most people and can therefore be easily interpreted. 

Bar Charts are similar to Line Charts, but they really prove valuable when you want to compare different Dimensions to each other. All Charts in Databox have a time series as the x-axis. For Attributed Metrics, Bar Charts allow you to change the x-axis to display each Dimension as an individual bar instead. This is a great way to proportionately analyze your data. 

Line/ Bar Chart vs Combo Chart vs Multi-Tab Chart

The simplicity of Line/ Bar Charts is one of the reasons why these are often preferred over the Combo Chart and Multi-Tab Chart. These should be your "go to" Chart visualizations. However, there are cases where Combo Charts and Multi-Tab Charts are preferred as well.

Combo Charts allow for two Metrics that are related to be tracked against one another. This chart displays the trends and movement of one Metric in direct relation to the trends and movement of another. This helps clearly highlight the correlation between Metrics. 

The Multi-Tab Chart is beneficial due to its space-saving design. Since you can have up to 4 different tabs displaying unique Line/ Bar Charts, there is a lot of data that can be put in a small space. However, there are two main considerations with this visualization. 

First, this visualization only supports 1 Date Range. This can affect your ability to get both high level and granular views of your data. Another consideration of the Multi-Tab Chart is that all of your data is not visible at once. Therefore, the 2nd, 3rd, and 4th tabs on the Multi-Tab Chart will not be visible on Scheduled Snapshots

Pie Chart vs Table

Pie Charts and Tables are both extremely valuable and popular visualizations, but they have different strengths. 

Pie Charts great for showing the distribution of data. This may be the distribution of individual Dimensions, like when the Metric  "Sessions by Source" is added to a Pie Chart and each "Source" is a different slice of the Pie Chart. On the other hand, a Pie Chart may be populated by individual Metrics. An example of this is adding "Total Followers" Metrics from each Social Source (Facebook, Twitter, LinkedIn, etc.) to a Pie Chart in order to produce a Pie Chart that clearly displays "Total Social Following."

Tables, on the other hand, show exact values and comparison percentages (performance trends). Tables allow you to view comparison percentages (Pie Charts do not), and individual Metric or Dimension values are visible without hovering over it.

Table vs Leaderboard vs Advanced Table

The main difference between these Visualization Types is the number of columns and the types of Metrics that are supported.

A Table can have a maximum of 1 Metric column and 1 comparison column. This Visualization Type allows you to select "Data From: Single Metric" or "Data From: Multiple Metrics" in the Datablock Settings. Because of that, both Standard and Attributed Metrics can be added to Tables. Learn more here

Leaderboards and Advanced Tables can only accept Data From: Single Metrics, meaning only Standard Metrics are supported. 

The Leaderboard has a limit of 2 customizable columns. Comparison percentages cannot be displayed on Leaderboards. 

The Advanced Table has a limit of 10 customizable columns. Comparison percentages can be displayed for each column. However, Advanced Tables only support 1 Date Range. This can affect your ability to get both high level and granular views of your data.

Due to the considerations outlined above, your default table visualization should be the Table. After that, the Leaderboard, followed by the Advanced Table. The decision between which of these 3 Visualization Types you should use is mainly dependent on the number of columns needed and the Metric(s) you're using.

Gauge vs Progress Bar

Both of these Visualization Types are relatively straightforward, and are great for showing progress towards reaching a goal. 

The main difference between the two is that a Metric visualized on a Progress Bar requires a Goal to be added and displayed for each Date Range. Since all Date Ranges don't support Goals, all Date Ranges aren't recommended when using the Progress Bar. Learn more here

Gauges, on the other hand, allow a "Max Value" to be set. This can be an automated value that's always higher than the Goal, a manually entered maximum, or another Metric as the max. Because of this field, the Gauge is more robust than the Progress Bar. 

Funnel vs Pipeline

Both Visualization Types are meant to show status or progression of a Contact or Deal. 

Visually, a major difference between these two Visualization Types is that a Funnel displays data vertically while a Pipeline displays data horizontally. Both the Funnel and Pipeline have a limit of 10 “rows” that can be added and mapped uniquely. 

Similar to a Pie Chart or Table, when populating a Funnel you have the ability to choose Data From: Single Metric or Data From: Multiple Metrics. This can help efficiently populate your Funnel. A Pipeline, on the other hand, can only accept Data From: Multiple Metrics. Learn more here

Compare vs Interval

Both of these Datablocks are relatively straightforward and allow or you to display multiple Metrics on a Datablock. However, the use case behind them differs. 

The Compare visualization places 2 metrics side by side. These metrics can be from 2 unique Data Sources, or from the same Data Source. Regardless, they should be related in some way so a “comparison” of the values makes sense. 

The Interval visualization is intended to show values of the same Metric for different Date Ranges on one Datablock. This allows the user to see Daily // Weekly // Monthly // Quarterly // Yearly values of the same Metric in one view. This Visualization Type can have a maximum of 10 rows added to it.

Best Practices using Datablock Settings

BEST PRACTICES

Databoard naming convention

Your Databoard should be named based on common themes surrounding the data on it. Are the Metrics all tracking Website Traffic? Do they report on your Sales activities? This is an area for you to take what you see and name the Databoard accordingly. Some common words used in Databoard names are “Overview” (i.e., "Traffic Overview") and “Performance” (i.e., "Sales Performance").

Datablock naming convention

In the majority of cases, the name of the Datablock should be the same as the name of the Metric. For example, if a Line Chart is showing Sessions, the Datablock title should be “Sessions” (not Traffic). There are two exceptions for this:

  • If the recipient of the Databoard specified otherwise. 

    You may get a request saying “I want to show Traffic from Google Analytics.” As long as you are certain which Metric they’re referring to when they say “Traffic” (i.e., "Sessions" vs "Users"), you can name the Datablock “Traffic” instead of Sessions to produce a better experience for the recipient of the Databoard. If you’re not sure what they mean by “Traffic,” it's recommended that you keep the Datablock title the same as the Metric name (“Sessions”) to avoid any confusion. 
  •  If the Datablock has multiple metrics displayed on it. 

    There are cases where it’s not as straightforward as the example above, like an Advanced Table that shows "Impressions by Campaign," "Keywords by Campaign," "Reach by Campaign," and "Clicks by Campaign." In a case like this, you’ll want to find the commonality between the Metrics and name the Datablock accordingly. For this example, we could name the Datablock “Campaign Overview” or “Campaign Performance." 

Column naming convention

Similar to the Datablock title naming convention, all Column titles should be named after the Metric itself. However, you may choose to shorten the name. This can help ensure the Column title is visible, while also helping with spacing on the Datablock.

For example, if you have an Advanced Table that shows "Impressions by Campaign," "Keywords by Campaign," "Reach by Campaign," and "Clicks by Campaign," you can name the columns "Impressions," "Keywords," "Reach," and "Clicks" in an effort to be clear and concise. In this example, the Datablock title should be “Campaign Overview” or “Campaign Performance” to make it clear that you are viewing Campaign Metrics.

Date Ranges

Selecting the right Date Ranges is imperative to ensure this Databoard is going to be valuable for the end user. We recommend adding 4-5 Date Ranges per Datablock, if possible.

The most popular Date Ranges are "Month to Date," "Quarter to Date," "Year to Date," "Last Month," and "Last 30 Days." This gives a good variety of high level and granular views of the data.

It's recommended that all Datablocks on your Databoard have the same Date Ranges selected (whenever possible). This will make the Databoard easier to analyze and will populate the Master Date Range Switcher with your Date Ranges. Learn more here. 

Lastly, the Primary Date Range should be consistent amongst all Datablocks on your Databoard. Otherwise, the Databoard could be difficult to analyze or misleading. 

Best Practices Databoard Checklist

  1. How many different Visualization Types are used on your Databoard?

    We recommend including a variety of Visualization Types on your Databoards. As a general rule, aim for a minimum of 3 different visualizations.
     
  2. Are all of the Date Ranges you’d expect to see in the Master Date Range Switcher showing up?

    If not, check the setup of each Datablock individually. The Master Date Range Switcher helps strengthen your reports by providing easier access to multiple views. 
  3. Is your Databoard optimized for Mobile use?

    To optimize for Mobile, your most important Datablocks should be at the top in the Mobile view. 
  4. Does the size and spacing of Datablocks on the Databoard follow these general guidelines?

    - The only Visualization Type that should be 1x1 is a Number Block

    - Charts and Tables should be no less than 2x2

    - If there is extra space on the Databoard, include a Text block to add context and tell the story of your data
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