Quotes like these can be applied to internal customers too. After all, A Business Intelligence dashboard is a product created for a purpose - a foundation for data-informed decision-making (and data-driven decision-making).

You've interacted with Business Intelligence regardless of your industry and role today.

Business Intelligence (BI) serves as the compass guiding businesses toward success. It's not just about collecting and presenting data; it's about enabling informed decisions that fuel growth. Visit the world of BI with me, unravel its significance, and understand how to distinguish an effective BI dashboard.

In this edition, I want to introduce my BI Brilliance Blueprint Framework, a compass I've used over the years to enhance the dashboards I built and to reap all the benefits that I've elaborated on later in this article.

What is Business Intelligence?

Business Intelligence (BI) is the process of gathering, analyzing, and presenting data to assist businesses in making well-informed decisions. It's the key that unlocks the door to data-driven success.

With BI being a foundation for every business, it’s absolutely critical to have access to vital (if not all) information that you need to make decisions today. Every business has some form of BI (manual processes included) but not every BI dashboard is a good one. I've built my share of poor dashboards. It wasn't until I shifted my mindset from "building what is asked" to "building what is needed" that I became truly effective.

So, What Is a Good BI Dashboard?

You’ll find several varying opinions, and recommendations on which BI tools, which chart to represent what kind of info, and how to optimize the performance of data refresh but all of them are more on improving the appearance of the dashboard user and less on the value it adds.

I’ve written about it before and will reiterate it again.

That is the focus of this edition - the value of a BI dashboard and not cosmetics.

You’ll learn to evaluate any Business Intelligence dashboard with just 2 fundamental questions.

1. What happened?

2. Where did it happen?

With advances in data visualization tools, dashboards today can also answer a lot more questions. My favorite is - What if. A scenario builder that can be self-serviced (actually I am going to write about it soon!). However, the 2 questions above are the bare minimum as they’ll generate value for the user.

Question 1: What Happened?

Any BI dashboard should be able to answer this basic question about metrics showing historical trends, and an ability to slice it by some attributes. It should enable you to look back in time to where a metric stood. Answering the title question is the foundation of a BI dashboard.

Here are some examples

1. What were the sales in Jan 2023?

2. What was the number of units sold in Q4 2022?

3. What was the traffic to our website last week?

'What happened' is not the beginning of a question but rather a way to inquire about the past tense. It also marks the first stage of Analytical Maturity, which will be my next topic for publishing.

As a user, you should be able to discern how one or more metrics have been trending recently, whether it's on a daily, weekly, monthly, quarterly, or yearly basis.

Understanding a metric's trend is often a high-level question. In my 14 years of experience in analytics, I've never encountered a situation where a user simply stopped at overall trends. It's as if, in a matter of seconds, minutes, or sometimes a mere two days, the same user wished to dissect the overall metric by various attributes.

Put simply, an attribute is a means of dissecting the metric within the same time frame, whether it's a day, week, month, or year. Let's use your personal expenses as an example. If you spent $7k last month, that's your primary metric. The attribute, in this case, would answer the question of where you spent that $7k. You could break it down into categories like Rent, Utilities, Groceries, Shopping, and so on. Now, envision a BI dashboard that displays your spending over the past 12 months. It provides you with an overview of your expenditures (the metric), and your curiosity drives you to explore further by examining the composition of your monthly spending.

In the business world, attributes might include demographics, platforms, acquisition channels, or other industry-specific categories. They allow you to slice and dice your metrics to gain a deeper understanding.

Question 2: Where Did It Happen?

When you begin to analyze the historical performance of your business metrics, you'll encounter both spikes and drops. Some metrics are better when they're higher, such as $Sales or %Customer Satisfaction (CSAT), while others are better when they're lower, like $Refund or %Downtime of the website. Regardless of whether a metric spikes or drops, you'll always want to understand the cause. Does the question 'Why is our revenue dropping month over month?' sound familiar?