# The Trouble With Multiple Truths In Business Intelligence

The goal of business intelligence (BI) is to achieve a “single version of the truth,” so you can make more informed business decisions.

But, as I’m about to share, the truth can change depending how you ask the question.

### An Example Of Multiple Truths:

1. What is the highest mountain on Earth?
2. What is the tallest mountain on Earth?
3. Which mountain on Earth has the biggest elevation gain?

Did you guess Mount Everest for any of the above questions? I will give you a hint: each of the above questions has a different answer… all because of how the metric is defined. Let’s look at each of them individually.

### Highest Mountain

It turns out that while Mount Everest gets all of the press, it’s not the highest mountain on our planet. There is a mountain in Ecuador called Mount Chimborazo which—due to its proximity to the equator—actually extends further into space than its more famous Himalayan cousin.

The areas around the Earth’s equator bulge out due to the centrifugal force applied by the planetary rotation. That means that Mount Chimborazo, while only the 30th highest peak when measured from sea level, is actually more than 7,000 feet further from the center of the planet! Keep in mind that it’s the entire area around the equator that’s impacted by the centrifugal force, so sea level is going to be further from the center of the Earth there as well.

The established way to measure the height of a mountain is to measure the distance from sea level. But by using a different metric definition, I can establish a different “highest” mountain.

The point I am trying to make is that it’s very important to define metrics—and enforce the definition consistently. If I don’t define a consistent “zero point” for my metrics (sea level versus center of the Earth in this example), then any information could be biased at best or flat out wrong at worst.

### Tallest Mountain

It turns out that Everest isn’t able to claim this title either! The volcano that forms the base for the “big island” of Hawaii is also taller than Everest. Everest has about 13,000 feet of elevation gain from base to peak. Mauna Kea has a 33,000-foot rise. The difference, of course, is that Mauna Kea starts on the bottom of the Pacific Ocean.

So what’s my “best” year for sales? Is it the year that had the highest total revenue? Highest net revenue? Best increase from prior year to current year? Was that increase measured in raw numbers (dollars) or percentages? Getting all of these definitions nailed down is often the most difficult part of building out a BI solution.

I’ve been picking on Everest. It’s not the highest mountain, and it’s not the tallest mountain, but surely it must have the biggest elevation gain, right? At least for mountains that start on land?

### Biggest Elevation Gain

It turns out that Everest once again loses out. From the base of the mountain to the peak, the elevation gain runs about 13,000 feet. It turns out that the north face of Denali—the tallest (or is that highest?)  mountain in North America—has a recognized elevation gain of over 18,000 feet, or almost a mile more than Everest.

Denali is incredibly dramatic, as it almost seems isolated. The base of the mountain sits on a series of plains that are between 1,000 and 3,000 feet from sea level. (See what I did there? I qualified my metric.)  From that base, the mountain rises to over 20,000 feet. Everest sits high up on the Tibetan Plateau with a base elevation of 4,200 to 5,200 feet. Even though the peak of Everest has a higher altitude than Denali, Denali is actually a bigger mountain.

Then again, everything is bigger in Alaska, right?

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