Our mission was compelling as we launched to fanfare and raised $11.5M within 10 months of founding the company. However

But with hindsight, I could look back to my original research notes and see I had ignored several fatal warnings. I had listened to what they said — exactly as they said it, but I did not realize until much later that I failed to actually understand what they meant.

As you listen to your target market describe what they do today to solve their problems, you can better understand potential customers’ existing incentives, behaviors, and desires in anticipation for how they’d react to a new solution.

Data scientist: The sexiest job of the 22nd century Ask these 3 questions during a job interview to make sure your employer is ready to make data scientists effectivetowardsdatascience.com

While it’s true that you’re a key customer for data engineering, you’re probably not the only customer. Modern businesses use data to fuel operations, often in ways that can hum along nicely enough without your interference. When your contribution to the business is a nice-to-have (and not a matter of your company’s survival), it’s unwise to behave as if the world revolves around you and your team. A healthy balance is healthy.

When we started grain.co two years ago, we began with a specific product solution in mind, built prototypes, and got feedback from users. They told us they’d love to use it but after months turning prototypes into a product, few actually did.

Since data science is at the mercy of data, merely having data engineering colleagues might not be enough. You might face an uphill struggle if those colleagues fail to recognize you as a key customer for their work. It’s a bad sign if their attitude reminds you more of museum curators, preserving data for its own sake.

If you’re considering taking a job as a head of data science, your first question should always be, “Who is responsible for making sure my team has data?” If the answer is YOU, well, at least you’ll know what you’re signing up for.

Your ideas are not nearly as important as your process — and the best process starts with understanding what the customers you wish to serve already do to solve their problems today and even more importantly, understanding why.

In other words, if a company doesn’t have any data or data engineers, then accepting a role as Chief Data Scientist means putting your data science career on hold for a few years in favor of a data engineering career — that you might not be qualified for — while you build a data engineering team. Eventually, you’ll gaze proudly at the team you’ve built and realize that it no longer makes sense for you to do the nitty-gritty yourself. By the time your team is ripe for those cool neural networks or fancy Bayesian inference that you did your PhD on, you have to sit back and watch someone else score the goal.

It doesn’t matter how smart or experienced we may be, statistically speaking, most of our ideas are simply not going to work. And the successful ones take time and hard work to turn into a real product that gets widely adopted by a market.

Our goal was not to validate whether the merit of a specific solution but to observe existing customer behaviors and desires as a means of generating new ideas for potential product solutions.

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ing about the infatuation phase. Instead, I’m referring to not getting fed up with each other in the long run. What I mean is having something to talk about even as the years pass, or something to do, and always looking forward to it.

Generative research questions are focused on understanding existing behavior. For example, here are some questions from an interview guide we used at Grain to understand how our prospective users already document and share information from live meetings:

Rob Fitzpatrick has famously coined this generative research phase “ The Mom Test,” which is a set of simple rules to ask good questions so that even your Mom can’t lie to you in her answers to protect your ego.

Before signing up for your new gig, consider negotiating for ways to hold your data engineering colleagues accountable for collaborating with you. If there are no repercussions to shutting you out, your organization is unlikely to thrive.