If you’ve been following me for a little while (in which case, hey, thanks!) you know that I am committed to helping build and grow companies in as data-driven a way as possible.

From the early days of a company, I encourage founders to seek out experts in data analysis. That means both outside the company— through taking advantage of analytics tools and software— and inside, by hiring an in-house data expert. Sometimes, that’s me. So, I thought it would be fun to share some thoughts about what it means to be a data-driven enterprise.

R-E-S-P-E-C-T. For Data.

I came by my deep respect for data early in my career. I got my start analyzing aviation safety and flight metrics for airlines and the FAA at MITRE. In that world, showing a healthy regard for data can literally be the difference between life and  death, not to mention billions of dollars. But, in case you’re curious, US Commercial Aviation is outrageously safe. Be more worried about a car crash on the way to the airport than about anything happening on  your flight.

Working for a startup, appreciating data and understanding what it can (and can’t) teach you, can mean the difference between the life and death of your company. But, wait, you might say: what about good old fashioned human ingenuity? Those strokes of genius that gave us everything from Newtonian physics to lightning rods to gene therapy? Excellent question, one which brings us to:

Hunches and Instinct at the Beginning and End, Data in the Middle.

Hunches are great, going with your gut can be fantastic. That said, think of these instinctual processes as an option at the beginning and the end of a decision-making process. When most founders come up with the idea for a company, it’s not due to a research-backed report on the industry. Instead, it’s an experience: a bad conference call, a bad cab ride, or a bad hotel stay. She has a hunch that if you take advantage of emerging technology and outside-the-box thinking, then there is a shot at doing something revolutionary.

That sort of gut call is great, and then you want to follow it up with looking at the cold, hard facts. A hunch is a starting point. As you test your original idea in view of new information, you should never be too afraid (or too arrogant) to reconsider your thesis.

Inevitably, at a certain point decisions have to be made:  What’s  your target market? Should you prize the highest level of technical wizardry in your product, or ease of use for the masses? What color should your web page be?

And at that point, after studying the data, it’s time again to go with your gut. One thing years of analyzing data has taught me is that you will rarely get a black-or-white, 100%-sure-thing recommendation. Ultimately, you have to throw the dice and trust your gut, but first make sure your gut is as well-informed as possible.

Which brings us to:

“Data-Driven” Doesn’t Mean “Data-Directed”.

Your data analysis is only as good as your ability to learn from it and use it. Key Performance Indicators (KPIs) are the measurements by which you judge success. For example, a 25% conversion rate, or keeping user attrition below 10% over six months. It can be anything  really, and that is where the “K” as in “key” comes in. You can judge  success by lots of different standards, and it is up to actual living,  breathing humans to decide which of those goals matter most.

Let’s look at a specific example. Say you’re trying to figure out how best to use your marketing budget. You find that $1000 spent at Source A produces 500 registrations, while at Source B, $1000 results in only 300 registrations. It would seem to be a  no-brainer: you go with Source A any day of the week.

BUT:
What if you find that, of those initial registrations, 10% of Source A regestered userss become loyal, while 20% of registered users from Source B do.

The data aren’t arguing, and they aren’t lying to you, either. This brings up a point that it is important to remember: “The data don’t care what you do.” Data analysis is a tool, a powerful tool to respect, but not blindly obey. The choice between Source A or Source B in this example should depend on whether you find it expedient at this point in your company’s growth to focus on registration volume or user loyalty. That’s a decision that only you and your coworkers (the carbon-based ones who eat and drink and sometimes hum annoyingly and then you get that stupid song  stuck in your head for the rest of the day) can make.

And once you’ve made it, you’ll want to gather the results of that decision …so you can analyze the hell out of them.