Best Practices when Building a Databoard
This document outlines things to consider and other best practices for building effective Databoards.
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The first thing to consider as you’re building out a Databoard is which Visualization Type will work for each Metric. In every case, 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 will ultimately produce the most value for the end viewer.
NOTE: The 7 most popular Visualization types are the Number Block, Line Chart, Pie Chart, Table, Progress Bar, Pipeline and Text Block.
- Number vs Line Chart / Bar Chart
A Number Block is designed to display simple data. This Visualization Type is compact, clear and precise. A Line/ Bar Chart is better used to highlight growth over time, daily granularity, or patterns in the data. You should aim to have at least one Line/ Bar Chart per Databoard to allow for more 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 common to most people and can therefore be easily be analyzed.
Bar Charts are similar to Line Charts, but they really prove valuable when you want to compare different Dimensions to each other. For example, Sessions by Source on a bar chart would show proportionally how each channel is performing and where your website Sessions are coming from.
- Pie Chart vs Table
The Pie Chart is the more basic of these two Visualization Types. Pie Charts are great for showing the distribution of the metric, but they do not show comparison percentages. For example, Sessions by Source on a Pie Chart would clearly show the distribution of traffic coming in from different channels, but not whether each channel is performing better or worse than it did in the previous period.
The Table Visualization Type allows for a more granular view of the data, mainly because you can see exact values and comparison percentages. This would allow the viewer to easily see exactly how many sessions each source had, and how that compares to the previous period.
- Gauge vs Progress Bar
Both of these Visualization Types are relatively straightforward, and are great for showing progress towards reaching a goal. However, the difference is that a metric visualized on a Progress Bar requires a goal being added to the Databox Account, while the Gauge allows the user to set a “max value” in the Designer that may or may not be equal to the Goal.
- Funnel vs Pipeline
Both Visualization Types are meant to show progression or movement of a contact or deal. A typical Sales Pipeline is a specific sequence of actions that a sales rep needs to take in order to move the prospect from a Lead to a Customer. Once the stage is complete, the prospect is advanced to the next stage. It shows the value and quantity of all deals in each stage of the Pipeline when the report is run. A typical Sales Funnel visually communicates the conversion rates of the contact/ deal through the Pipeline stages. This view is expected to be wide at the top as prospects/ deals enter, then increasingly narrow as they are disqualified or decide not to buy. In Databox, the Funnel and Pipeline have similar functionalities because users have used them interchangeably on Databoards.
Visually, a major difference between these two visualization types is that a Funnel displays the data vertically while a Pipeline displays the data horizontally. Both the Funnel and Pipeline have a limit of 10 “rows” that can be added and uniquely mapped. The Funnel allows the user to select Data From: Single Metric or Data From: Multiple metrics ( learn more here), while the Pipeline only allows users to add data from single metrics. Both visualization types have a “Sort” option in the Property Manager. By turning the “Sort” functionality on you are telling the system to automatically rearrange the “row” items based on their value (highest → lowest) in order to create the typical “funnel” or “pipeline” image. Most users have a predetermined order of events, so this is not often enabled.
Finally, both Visualization Types have “Step Conversion” and “Show Percent” options in the Property Manager. The options for the “Step Conversion” are either “Step by Step,”, which shows the conversion percentages from stage 1 → stage 2, stage 2 → stage 3, stage 3 → stage 4, etc., while the “From First Value” option shows the conversion percentages from stage 1 → stage 2, stage 1 → stage 3, stage 1 → stage 4, etc. Users more commonly select the “Step by Step” option. The “Show Percent” toggle allows you to turn these conversion percentages on or off. We have had users use these Visualization Types for unique use cases that aren’t progression-based, so for them the comparison percentage is not necessary.
- Table vs Leaderboard vs Advanced Table
The main difference between these Visualization Types is the number of columns that can be added, and the types of metrics that can be included. A Table can have a maximum of 1 metric column and 1 comparison column (showing the comparison percentage). This Visualization Type has an option of Data From: Single Metric or Data From: Multiple Metrics. Data From: Single Metric means that the system will automatically split the metric selected into different row items. Because of that, the only metrics that you can select have the “L” (or dropdown) icon next to them. This denotes that there is a list stored for this metric. An example of a metric that you can add as a Single Metric is Sessions by Source. Each source (i.e., Direct, Referral, Organic, etc.) will be a different row in the table, but in the Property Manager you only had to select the one metric (Sessions by Source). Data From: Multiple Metrics signals to the system that you want to manually map each row of the Table. You can select any metric for these rows, but if the metric has the “L” dropdown icon next to it you will be prompted to select a single Dimension. So, if you choose Sessions by Source you will then need to select one Dimension (Direct or Referral or Organic, etc.) to be displayed in that row. This would be useful if the user only wanted to show certain sources in the Table.
Leaderboards and Advanced Tables can only accept Data From: Single Metrics. The Leaderboard has a limit of 2 customizable columns, while the Advanced Table has a limit of 10 customizable columns. Advanced Tables have further limitations that are similar to those of the Multi-Tab Chart; you can only select 1 Date Range for an Advanced Table Datablock (this will possibly be different after Charts 2.0) and all of the data is not always visible when the Databoard naturally loads (clicking/ scrolling is required), so it’s not as valuable for users who want to Schedule Snapshots of their Databoards.
Due to the limitations outlined above, we can rank the three Visualization Types in order of functionality (from most to least) as 1. Table 2. Leaderboard 3. Advanced Table. The decision between which of these 3 Visualization Types should be used is primarily based on the number of columns needed. So, if a table needs 2 customizable columns it should be a Leaderboard, not an Advanced Table.
- Line Chart / Bar Chart vs Combo Chart vs Multi-Tab Chart
The benefit of the simplicity of line/ bar charts is outlined above. Often, these are preferred over the Combo Chart and Multi-Tab Char and should therefore be your “default” chart visualizations. However, there are cases where Combo Charts and Multi-Tab Charts are preferred.
Combo Charts allow for two metrics that are related to be tracked against one another. The real value is the ability to compare the movement/ patterns of one metric directly to the movement/ patterns of another. This allows the viewer to easily recognize the correlation between these metrics.
The Multi-Tab Chart is beneficial due to its space-saving design (you can have up to 4 different tabs displaying unique Line or Bar Charts). The limitation of this visualization type is that you can only have 1 Date Range selected per tab. While this is excluded from the Master Date Switcher logic (meaning you can still use the Master Date Switcher even with a Multi-Tab Chart on the Databoard), it does take away the ability to display both high level and granular views of the data like you’re doing in other Datablocks. Because of that, we do not encourage heavily using this visualization type. Another limitation of the Multi-Tab Chart is that when the Databoard loads, you cannot see all of the data. Therefore, Scheduled Snapshots aren’t as beneficial for Databoards where Multi-Tab Charts are used.
- Compare vs Interval
Both of these Datablocks are relatively straightforward and allow for you to display multiple metrics at once. 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 Type 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.
We’ve seen users find value from the Image Datablock for multiple purposes. One is that if we don’t have a native integration with a Data Source, users often take a screenshot and upload it to a Databoard with a white background. Once an image has been added to the Databoard, you can link it to an external URL in the Property Manager. This allows the user to display the data in the same presentation as everything else, while giving them the ability to continue steering the conversation elsewhere.
This Datablock gives us the opportunity to communicate with the end user directly on the Databoard. For example, if a Progress Bar and subsequent Goal was added to the Databoard, you can add a note prompting the user to update the Goal with the actual Goal value. Without this, the user may be confused what that random Goal number is and where it came from.
You can also add a Text block prompting the user to add context to the Databoard. We do not encourage users to add Text referencing specific metrics, because the metrics are constantly changing. For that, they can use Annotations.
Want more information on the capabilities and limitations of the Visualization Types available in Databox? Follow this link.
- Datablock Title 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 Line Chart is showing Sessions for This Month, the Datablock Title should be “Sessions” as well (not Traffic). There are two exceptions for this:
A) 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” (Sessions or 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,” I would recommend adding a Datablock for Sessions and keeping the Datablock title the same as the metric name (“Sessions”).
B) 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 displayed and name the Datablock accordingly. So, for this example, we could name the Datablock “Campaign Overview” or “Campaign Performance."
- Column Title Naming Convention
Similar to the Datablock Title Naming Convention, all Column Titles should be named after the metric itself. However, you can choose to shorten the name if you see fit. 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 concise and clear. In this example, the Datablock Title (“Campaign Overview” or “Campaign Performance”) will 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 top 5 Date Ranges Agencies use in Databox (when applicable) are Month to Date, Quarter to Date, Year to Date, Last Month, and Last 30 Days (Month to Date is typically selected as the Primary Date Range). This gives a good variety of high level and granular views of the data.
- Primary Date Range
The Primary Date Range is the Date Range that the Datablock naturally loads to when the Databoard is opened. This should be consistent across all Datablocks so the viewer has a clean, consistent view right off the bat.
- Master Date Range Switcher
The Master Date Range Switcher allows you to switch between Date Ranges on multiple Datablocks at the same time. The only Date Ranges that will be available in the Master Date Switcher are those that are used in every Datablock on the Databoard. Since Multi-Tab Charts and Advanced Tables can only have 1 Date Range selected, they are excluded from this functionality. Ideally, every Date Range used on the Databoard is an option in the Master Date Switcher. The only time this is not the case is if a metric does not have a Date Range available and you’re not able to add it via the Query Builder tool.
- Compare To
By default, the Compare To functionality is set to “previous period.” Displaying the progress compared to previous periods / Goals with red/ green indicators is extremely important to ensure the Databoard is easy to read/ interpret. Double check that this is enabled for each Datablock where the functionality exists.
- Limit Rows
When using the Table Datablock with Data From: Single Metric selected, there is a possibility that hundreds of rows of data are being returned. As a standard rule, we should aim to have a maximum of ~15 rows on a Table. This ensures the user won’t be overwhelmed with the data being displayed, and also improves the experience tremendously for mobile users.
- Naming the Databoard
The Databoard should be named based on common themes in the data on the Databoard. Are the metrics all tracking Website Traffic? Do they report on Pipeline Performance? 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” and “Performance.”
A white background is the most popular choice of Databox users.
To further customize your Databoard, you can add your logo to the bottom left corner. This is a great way to tie everything back to your business.
- Carousel Mode
If you have created multiple Databoards, you can loop them in Carousel Mode and monitor the data through the Streaming URL.
- How many different Visualization Types are used on the Databoard?
- We'd recommend including a variety of Visualization Types. As a general rule, aim for 3 different types.
- 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
- Is this Databoard optimized for Mobile use?
- To optimize for Mobile, the most important Datablocks should be at the top in the Mobile view.
- Does the size and spacing of Datablocks on the Databoard follow our 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 tel the story of your data