When I started Cataluv, a mobile shopping app for home décor, I was determined to be a data–driven entrepreneur. I spent time reading market research and assessing competitors. I made sure our product launched with a dashboard and customer analytics in place so we could measure user response. I trained my team to quantify the success of our marketing campaigns to identify which channels yielded the best results.

This should’ve worked well, but there was one big problem: Sometimes we just didn’t have enough data to make meaningful decisions.

This became clear when we spent a full week redesigning our dashboard to showcase our sales data. Among other things, we wanted to see a histogram of purchases to find out if there was a price point that was especially attractive to users. But after going through the whole exercise of building this new custom interface, our data wasn’t even actionable. We didn’t see clustering around any particular price points, and our sales range was super wide.

In that moment, the conversation changed from one about sales to one about target customers. As someone on the team put it, how can you tell what products are going to be your bread and butter when you don’t even know that you’ve got the right customers at the table?

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In retrospect, we spent too much time in our first three months trying to analyze our small sample set of data, when we should’ve just been trying to grow. Data became a crutch and a procrastination tool: It made us feel like we were doing a lot of work to understand our users, when what we should’ve been doing was getting more users and talking to them one on one.

I’m not sure why it took me so long to learn this lesson.

Before starting my own business, I spent years observing other startups—first working at Endeavor, an international organization that supports entrepreneurs in emerging markets, and then at Wharton, where I focused my studies on entrepreneurship and quantitative marketing.

At Endeavor, I watched entrepreneurs schooled in the shoot-from-the-hip ideology. Given the uncertainty of emerging markets, these individuals were often forced to make quick, seismic shifts to their business plans, generally without the kinds of detailed market research that might be available in countries with large formal economies. They learned to be fast to overcome their blind spots. Sometimes this resulted in a crash–and–burn situation, but other times, these entrepreneurs were able to zip around obstacles and course correct like a NASCAR driver.

At Wharton, I encountered a different type of entrepreneur. In classrooms and MBA internships, I watched as data proliferated to every decision. From the business–planning process, to product development, to marketing, data collection and analysis were the silver bullets.

It’s clear to me now that a good entrepreneur balances data–driven decision making with gut instinct. And different stages of a business might require you to weigh one of these factors over the other. In the very first stages of a startup, it’s certainly better to be a speed racer focused on growth so that you might one day have enough data to make smarter decisions.

Allison Berliner is the founder and CEO of Cataluv, a mobile shopping app for home decor. Cataluv grew out of lessons learned from past retail marketplace startups PopInShop and Spot It Buy It, as well as Berliner’s experience as a Dreamit Ventures-backed entrepreneur. Allison received her undergraduate degree from Princeton University and an MBA from the Wharton School of the University of Pennsylvania.

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