Advanced data science and analytics capabilities are becoming increasingly accessible through business intelligence and data visualization tools. This makes it easy to get caught up in all the feature-rich bells and whistles of the tool and lose track of the essentials of good data delivery. Often, the difference between a dashboard that drives critical insights and one that ends up lost and forgotten is a strong focus on business needs and context.
The framework and process behind dashboard design is even more important than the technology behind it. In this three-part series, we'll focus on a proven methodology for dashboard design, evaluation, and reporting.
Part 1: This blog will focus on how to design a dashboard
When planning a dashboard, consider these three crucial steps: identifying the audience, determining the views, and choosing the metrics.
1. Identify the audience
The first step in building a good tool is determining who the audience is and what they care about. Whether the dashboard will be used by the senior executives in your company or the junior analysts that report to you, ask the following questions to make your dashboard as relevant as possible:
Which distinct audience(s) needs to drive what business decisions?
What specific pain points do they have related to the topic?
What information do they need and what noise could they do without?
How much overlap exists in the needs of the various members of your audience and how much is unique?
Which audience(s) should I prioritize?
It’s a basic step — but it’s critical to success. Many technically impressive dashboards have failed because their creators didn’t keep in mind what their audiences truly needed to see.
2. Determine the views
Creating views is about identifying the 2-4 topics that summarize the information the audience needs.
If you have just one audience, this is usually simple. If you have multiple audiences, it may take a bit more work. In either case, consider their priorities and pain points to look for the broader areas of the business that will help address their needs.
Views are just a chain of information that connect to tell the broader story
For example, if you’re creating a dashboard for the CEO, there may be four separate views she needs to run the company: HR, operations, marketing, and sales. Alternatively, if you’re building a dashboard for a sales executive, it may be sales rep productivity, customer buying behavior, and all-up sales. By identifying these topics, you’ll develop a picture that tells a cohesive story and avoid simply coming up with a set of disparate metrics.
3. Choose the metrics
Determining which metrics to use is about telling a connected story, not simply throwing data on a page.
This isn’t always an easy step, so don’t worry if you struggle a bit. It’s common to come up with way more metrics than you should actually report on. As with any good brainstorming activity, come up with as many ideas as you can, then work to narrow the list.
Out of the steps we’ve covered, this one is the most difficult and the important. To make this process a bit easier, we leverage a framework called the “4 Ts”:
Totals: Totals are sums of individual pieces of information. Frequently it is the total number of employees or the total revenue; it is the aggregate of the data. For instance, "The total revenue in the US is $900 million."
Trends: Trends are patterns that suggest general movement and tendencies. It is commonly a rate of change over time, which can be a growth rate or an average rate of occurrence. Trends can also lead to a trigger, such as a downward trend in sales leading to the issuance of new product sales incentives. For instance, “LatAm revenue is growing at an average rate of 11% YoY in the last three years and 14% QoQ this year”
Triggers: Triggers are reactions to defined business thresholds being met. An example we often see is when a salesperson hits a certain number, things like bonuses or additional benefits are triggered. For instance, “The SoCal sales team hit their quota in Q3, so in Q4 an additional 0.5% bonus kicks in”
Targets: Targets are goals. Often, a target may be a total, trend, or trigger. It could be the number of customers sold to (total), a mix of what is sold (trend), or even the number of times an action is completed (Trigger). For instance, “While Germany is growing at 7%, they are at risk of missing their target revenue number of $300 million.”
Once you have a good list of potential metrics, Consider the following questions on each proposed data point:
Is it realistic? Is it possible to get the data and be able to utilize it with the desired audience(s)?
Is it accurate? Is it based on reliable information, or is there a potential for large gaps or errors?
Is it measuring what we think it measures? Is the metric truly related to the point you’re making, or is it only circumstantially or tangentially connected?
Does it contribute to the story or decision making? If the target audience member didn’t have this information, would it change their action, or is it perhaps a more granular point that is interesting, but not critical?
Putting it all together
Once the audience, views, and metrics are defined, the goal is to step back and look at everything holistically. If you’re building a single executive scorecard, the goal should be to get things down to about 4 views that have 12-16 metrics total. If you’re building a more robust set of dashboards, the goal is to understand how to group views and metrics into logical categories that support different audiences or lines of inquiry.
At the end of this process of blending your business knowledge with the technical expertise of your dashboard architects, you’ll be well on your way to creating real insight for your audience. Users will walk away with a deeper understanding of their business and be armed with the information they need to drive the business forward. They’ll also want to come back for more.
Luke is a leader in our channel management and business intelligence practices. With over 10 years of industry experience he brings a background in customer service and a passion for data based decision making to collaboratively solve client challenges.