Introduction

The following article discusses how to assess the quality and adoption of your dashboard.

Introduction
Table of Contents
How to Measure a Dashboard's Quality?
Planning Makes Perfect
Assessing a Dashboard's Quality

How to Measure a Dashboard's Quality?

Let's begin by defining what a "Good Dashboard" is.
To assess the quality of your dashboard, you should measure the following aspects:

Is the dashboard practical? Does it serve its purpose?
Does the dashboard display relevant and correct information?
Is the information in the dashboard displayed correctly?
Was the dashboard adopted by the end-users?
Is the dashboard intuitive for use?

Planning Makes Perfect

Correct dashboard planning is the key to its success!

I recommend you read this article discussing a dashboard's (high-level) development cycle.
It breaks the process into easy measurable steps that start from the initial KPI planning to maintaining and adjusting your end product.

Reading through it might help identify flaws during the dashboard's initial rollout process.

Assessing a Dashboard's Quality

The "Practical" Test

The practicality of a dashboard focuses on whether the dashboard serves its purpose.

Going back to a dashboard's planning phase, try to recall the reason for building this dashboard.
What were the end-users looking to achieve? What insight were they seeking?

Possible answers are:

Monitoring a person's or a group's achievements
Monitoring a measurable process
Help decide what to do next based on historical information
Assess future steps in various scenarios (What-If Analysis)

A good dashboard: Answers its purpose!

How to Test This?

To check whether your dashboard is practical, you should:

Set up a call with one (or more) end-users and/or stakeholders using the dashboard
Ask them whether they can make the intended decision based on it
If they are - What is the process of making the decision?
If they aren't - What are the obstacles that stand in the way of making it? (e.g., missing/incorrect data, bad user experience, etc.)

Next, revisit the dashboard's goals:

What question should it answer?
What action can the users make based on it?

Compare the information you got from your stakeholders and the plan made when designing the dashboard - Define the gaps.

How to Resolve This Type of Issue?

Work towards re-planning your dashboard to meet its goals. Focus on:

Aligning the "Call for Action"
Redefining the KPIs and the user story
Redesigning and restructuring the KPIs
Optimizing/adjusting the data model to be able to answer the different KPIs

Resolving this type of issue will most likely require a complete dashboard redesign, followed by an entire UAT cycle.

The "Data Relevance" Test

The term "Data Relevance" breaks into two aspects:

Am I displaying relevant data - Considering the audience of the dashboard?
Am I displaying relevant data - Considering the decision to be made?

Does It Address the Right Crowd?

Like any deliverable, one of the fundamental things to know is its audience.
The answer to this question will affect the following:

The type of dashboard you create
The granularity of data you present
The way information is presented

A good dashboard: Is built with the right audience in mind.

Does it Contain the Right Amount of Data?

Making a decision requires having the right amount of data:

Too little data - Could prevent the person from making the right call.
Too much data - Could confuse the person and throw them off-track.

A good dashboard: Has the right amount of data required to serve its purpose.

How to Test This?

To check whether your dashboard has relevant data, you'll have to:

Assess who are the end-users and/or stakeholders using this dashboard
Set up a call with one (or more) end-users and/or stakeholders using it
Ask them what the process of making their decision is?
Check whether the information they require is presented in the dashboard.
Check whether the dashboard has redundant or irrelevant data.

How to Resolve This Type of Issue?

Incorrect Dashboard Type

Check your dashboard's type - Based on the person using this dashboard, should it be operational, analytical, tactical, or strategic? Compare the dashboard type to the intended crowd to determine if it was designed correctly (for example, a "Tactical Dashboard" is aimed towards upper management and will usually present long-term KPIs and high-level metrics).

Resolving this type of issue will most likely require a complete dashboard redesign, followed by an entire UAT cycle.

Missing/Redundant Information

If you've identified that certain information is redundant or missing, consider adding, modifying, or removing widgets from the dashboard.

Doing so might be a simple task (especially when removing data). However, it might also end up as a more extensive project requiring a partial redesign of the dashboard and a partial/complete UAT cycle.

The "Data Correctness" Test

Presenting inaccurate or outdated data is worse than displaying partial or excessive data.
Two possible outcomes of stakeholders, basing their decisions on incorrect data, are:

Experiencing negative events (such as revenue loss)
Compromising the trust relationship between Sisense and the end-users

Frequent causes for showing outdated data in your dashboard include:

An ETL process that is not run frequently enough
An ETL process that often/occasionally fails
An ETL process using an incorrect table update behavior (e.g., "Accumulative" where it should be "Full")
An ETL process that isn't synchronized with your Data Warehouse
A source database being offline for an extended period

Frequent causes for showing incorrect data in your dashboard:

Wrong data modeling leading to unexpected Many-to-Many relationships
Business questions that don't align with the data model (e.g., causing "Random Paths")
Unexpected / Missing inheritance of filters
Wrong data security configuration
The use of multiple data models (in the same dashboard) built at different schedules

A good dashboard: Displays precise and up-to-date data

How to Test This?

To check whether your dashboard has correct data, you'll have to:

Assess the different widgets on the dashboard to see the granularity of data displayed
For each widget, check what data model it relies on - Use the Usage Analytics "Usage - Builds" dashboard to monitor the behavior of historical builds.
Set up a call with one (or more) end-users and/or stakeholders using this dashboard
Ask them if they trust the data on the dashboard and if the data refresh frequency is sufficient.

How to Resolve This Type of Issue?

Showing Outdated Data

Ask your stakeholder how frequently they expect the data to be refreshed.

Perform the following actions:

Check the data model's build frequency - Should the build frequency be modified?
Check the build time - Should the data model be optimized? Is the data model too heavy?
Check the build success rate - Why are builds failing? Is the system running low on resources? Should "Data Groups" be applied?
If using a Data Warehouse (DWH) and an Elasticube - Check the DWH build frequency; Check the synchronization between the DWH ETL finish and the Sisense ETL beginning.
If you can't achieve the required build frequency, check whether all widgets require the same data refresh rate - Can a "Live Model" or a "Hybrid Dashboard" be considered?

Showing Incorrect Information

Ask a stakeholder to generate a report with correct data or ask them to point you to "defective widgets."

Perform the following actions:

Check the formula behind the figure(s) showing the incorrect data and correct them. Consider the different filters, inheritance behavior, etc.
Examine the query path used to calculate each figure (look out for unexpected "Many-to-Many relationships" or "Random Paths") - Use the "Visualize Queries" add-on (Link) to compare Sisense behavior against the expected query path.
Run the calculation in the data model using a custom table and a SQL statement that emulates the dashboard's calculation.

The "Visual Correctness" Test

Data alone isn't enough; it must be visualized correctly.
Visualizing data requires using the correct widget type and visual aids to convey the message.

For example, say a KPI is showing the company's revenue. See the different visualization options below:


Option #1	Option #2	Option #3

Transitioning from option #1 to #2 provides an added value of "Good" / "Bad"
Transitioning from option #2 to #3 provides an added value of revenue ranges

A good dashboard: Has widgets that are visualized correctly and convey a clear message.

How to Test This?

To check whether your widgets are visualized correctly, you'll have to analyze each widget individually:

Categorize each widget to find out its aim (e.g., show a single figure, compare values, show behavior over time, visualize data to show distribution, etc.)
Verify the visualization matches the widget type (e.g., An indicator widget is perfect for displaying a single figure, a line chart is ideal for showing a behavior over time, etc.)
Make sure each widget has visual aids (such as conditional formatting) to convey its message clearly - Each widget should tell a small puzzle of the story (which the dashboard should put together to a larger picture)
Set up a call with people who are not familiar with the dashboard
Ask them to describe each widget (separately) and their conclusion from looking at it.

How to Resolve This Type of Issue?

Wrong Widget Type

Resolve the issue by fixing the visualization type - Use the following chart to help out:

Type	Use Case
Indicator	Show a single figure (numerical) Show a single figure and a gauge representing its range
Column Chart	Show a comparison among different sets of data Track data sets over time Track individual values + Their sum (stacking)
Bar Chart	Compare many items Track individual values + Their sum (stacking)
Line Chart	Reveal trends, progress, or changes that occur over time The data set is continuous rather than full of starts and stops
Area Chart	Displaying absolute or relative (stacked) values over a time period Analyzing a Part-to-whole Relationship
Pie Chart	One static number, divided into categories that constitute Represent numerical amounts in percentages
Table	Display RAW granular data
Pivot	Display RAW granular data Display aggregative data in a table format
Scatter Plot	Comparing large numbers of data points without regard to time Identify a potential relationship between two variables
Calendar Heatmap	Show relative number of events for each day in a calendar view

Missing Visual Aids

Visual aids help the end-users identify "Good" or "Bad" values. Possible visual aids include:

Adding colors to numerical labels (e.g., Green figure vs. a red one)
Adding background colors to table cells (e.g., Color negative cells red)
Adding a line chart representing a threshold (e.g., A red line to indicate a lower threshold)
Converting numerical values with text (e.g., Showing a "Revenue increased" label rather than a positive figure)
Adding trend lines
Adding a forecast range

The "Intuitiveness" Test

Dashboard intuitiveness refers to the ability of a non-technical person to:

Access the dashboard
Understand and conclude the dashboard
Understand how to customize the dashboard (using filters, drilling, etc.)

Easy Access to Data

There are two methods of accessing a dashboard:

Sisense offers a complete HTML5 platform (a.k.a. Sisense Web Application) that can be white-labeled and customized to meet the company's look & feel (i.e., color pallet, logo, links to internal support and documentation pages, etc.).
Sisense offers three different embedding deployment options (conventional iFrames, an embedding SDK, and a complete JS-based embedding solution). Embedding the dashboard (or individual widgets) allows infusing analytics into web pages and applications.

Choosing the proper access method will affect how end-users access data and benefit the BI solution. To improve intuitiveness - Make sure to streamline the process of consuming data as much as possible. The need to switch between multiple applications will result in a lack of efficiency, a complex adoption, or even a lack of adoption.

Ease of Comprehension

The comprehension of individual widgets was discussed earlier. However, is "the whole" greater than the sum of the parts? Does the dashboard tell a story? An excellent example for a dashboard:

Widget #1 shows the revenue is low.
Widget #2 shows a revenue breakdown per department and points out one department losing money.
Widget #3 provides an income/expanse category breakdown and points out non-proportional marketing expenses.
Widget #4 provides a detailed transactional income/expanse breakdown and points out the individual expenses

Customizability and Interactivity

Stale reports tell one story. However, a person looking at the dashboard may want to filter, sort, and pivot the data to tell the same story about a specific segment or individual.

The tools for customizing/interacting with a dashboard include:

Filtering data to a specific segment of interest (Link)
Drilling into a measure to be able to extract more information about it (Link)
Adding explanations (Link) and narratives (Link)

A good dashboard: Is easy to access, self-explanatory, easy to customize, and play around with.

How to Test This?

To check whether your dashboard is interactive, you'll have to:

Set up a call with one (or more) end-users and/or stakeholders using it
Ask them when they use this dashboard and their workflow of consuming its information (e.g., A customer requires the data when building a financial report, and his workflow includes opening another browser tab and logging into the Sisense Web Application).
Open the dashboard and ask them to explain it. Try to identify widgets that were over-explained and widgets they skipped. Track the questions you have to ask to understand what you see. Track the amount of "mouse scrolling" they perform when explaining the dashboard flow.
Ask them what interaction they have with their dashboard and lay out the tools to increase interaction. Note the tools they are interested in (e.g., extra filters, adding narratives to widgets, etc.)

How to Resolve This Type of Issue?

Data is Hard to Access

Ease of access is easy to measure - Count the number of clicks the user has to make when they want to consume the data in this dashboard - Fewer clicks = Easier to access.

If data is hard to access or requires too much "Clicking around":

Integrate SSO or WAT to prevent the user from logging in to the Sisense Web Application
Embed the dashboard (or a single widget) into the application/webpage
Have additional data imported to Sienese - Allowing the user to have a "One Stop Shop" for their entire workflow
Enable sending periodic reports to the user's email
Enable NLQ to allow the user to interact with Sisense easily (Link)
Infuse data into G-Suite applications (Link)
Enable Pulse alerts to send push notifications to the user (Link)

Data is Hard to Comprehend

If your widgets tell the right story, but the complete picture doesn't make sense:

Move widgets around to make the story clearer
Add visual separators between certain widgets (requires scripting)
Reduce the number of widgets by splitting your dashboard into multiple stand-alone dashboards
Use add-ons to simplify dashboard navigation (e.g., Accordion)
Bring in a UI/UX designer to help visualize correctly
Redesign the dashboard to avoid scrolling the mouse

The Dashboard isn't Customizable / Interactive

Make the dashboard more customizable by allowing the user to:

Filter data based on predefined filters
Define hierarchies to make filtering more intuitive
Integrate filtering abilities into the dashboard (e.g., BloX buttons, drilling options, clickable widget values)
Add additional widgets (e.g., Accordion, Switchable Dimensions, Tabber, etc.)
Add premium widgets (e.g., Advanced Input Parameters - Link)

The "Adoption" Test

So you've created a great dashboard; it has all the correct data and visualizations, a person can access it quickly and draw the proper conclusion by just looking at it, but it wasn't adopted. Has the company done enough to drive its adoption (e.g., sufficient training, decommissioning the old dashboard, integrating it into the users' application, etc.)?

A good dashboard: Is measured by its adoption and usage.

How to Test This?

To check whether your dashboard has correct data, you'll have to:

Find out who this dashboard's audience is
Use the Usage Analytics "Usage - Dashboards" dashboard to monitor who accesses this dashboard and how often.
Set up a call with one (or more) end-users and/or stakeholders who are not using this dashboard
Find out why this dashboard isn't being used.
Find out what alternative ways they use to collect the data

How to Resolve This Type of Issue?

Once you're sure the dashboard is perfect and the only thing missing is adoption:

Involve users in the design meetings and UAT loop to make them feel they are part of the process
Create an adoption plan!
Get executives to buy-in - Causing them to promote their usage
Monitor what people are using the dashboard and which aren't - Target the right people
Reeducate your end-users on the benefits of the dashboard and the value of using it
Decommission old dashboards and reports
Refresh your dashboards from time to time (design, contents, etc.)

Introduction

Table of Contents

Table of Contents

How to Measure a Dashboard's Quality?

Planning Makes Perfect

Assessing a Dashboard's Quality

The "Practical" Test

How to Test This?

How to Resolve This Type of Issue?

The "Data Relevance" Test

Does It Address the Right Crowd?

Does it Contain the Right Amount of Data?

How to Test This?

How to Resolve This Type of Issue?

Incorrect Dashboard Type

Missing/Redundant Information

The "Data Correctness" Test

How to Test This?

How to Resolve This Type of Issue?

Showing Outdated Data

Showing Incorrect Information

The "Visual Correctness" Test

How to Test This?

How to Resolve This Type of Issue?

Wrong Widget Type

Missing Visual Aids

The "Intuitiveness" Test

Easy Access to Data

Ease of Comprehension

Customizability and Interactivity

How to Test This?

How to Resolve This Type of Issue?

Data is Hard to Access

Data is Hard to Comprehend

The Dashboard isn't Customizable / Interactive

The "Adoption" Test

How to Test This?

How to Resolve This Type of Issue?