The “quickly” part of that lesson can lead to a culture of “good enough.” Your features should be good enough to attract some early adopters. Your design should be good enough to be usable. Your code should be good enough to make your product functional.
While this might drive a lot of perfectionists nuts, I’m all for it. Good enough means that you can spend your time perfecting and polishing only the parts of your product that people care about, and that means a much better eventual experience for your users. It may also mean that you stay in business long enough to deliver that experience.
I think though that there’s one part of your product where the standard for “good enough” is a whole lot higher: Data. Data are different.
You Can’t Make Good Decisions With Bad DataThe most important reason to do good research is that it can keep you from destroying your startup. I’m not being hyperbolic here. Bad data can ruin your product.
Imagine for a moment an a/b testing system that randomly returned the wrong test winner 30% of the time. It would be tough to make decisions based on that information, wouldn’t it? How would you know if you were choosing the right experiment branch?
Qualitative research can be just as bad. I can’t tell you how many founders have spent time and money talking to potential customers and then wondered why nobody used their product. Nine times out of ten, they were talking to the wrong people, asking the wrong questions, or using terrible interview techniques.
I had one person tell me, “bad data are better than no data,” but I strongly disagree here. After all, if I know I don’t have any data, I can go do some research and learn something.
But if I have some bad data, I think I already know the answers. Confirmation bias will make it even harder for me to unlearn that bad information. I’m going to stop looking and start acting on that information, and that may influence all of my product decisions.
If I “know” that all of my users are left handed, I can spend an awful lot of time building and throwing out features for left handed people before realizing that what I got wrong was the original premise. And, of course, that problem is made even worse if I’m not getting good information about how the features are actually performing.
You Have To Keep Doing ItUnlike any given feature or piece of code, collecting data is guaranteed to be part of your process for the life of your startup.
One of the best arguments for building minimum viable products and features is that you might just throw them out once you’ve learned something from them (like that nobody wants what you built).
This isn’t true of collecting data. Obviously you may change the way you collect data or the types of data you collect, but you’re going to keep doing it, because there’s simply no other way to make informed decisions.
Because this is something that you know is absolutely vital to your company, it’s worth getting it right early.
Data Collection Is Not a MysteryMost of your product development is going to be a mystery. That’s the nature of startups.
You’ve got a new product in a new market, possibly with new technology. You have to do a lot of digging in order to figure out what you should be building. There’s no guide book telling you exactly what features your revolutionary new product should have.
That’s not true of gathering data. There is a ton of useful, pertinent information about the right way to do both qualitative and quantitative research. There are workshops and courses you can take on how to not screw up user interviews. There are coaches you can hire to get you trained in gathering all sorts of data. There are tools you can drop in to help you do a/b testing and funnel tracking. There are blogs you can read written by people who have already made mistakes so that you don’t have to make the same ones. There is a book called Lean Analytics that pretty much lays it out for you.
You don’t have to take advantage of all of these things, but you also don’t have to start from scratch. Taking a little time to learn about the tools and methods already available to you gives you a huge head start.
Good Data Take Less Time Than Bad DataHere’s the good news: good data actually take less time to collect than bad data. Sure, you may have to do a little bit of upfront research on the right tools and methods, but once you’ve got those down, you’re going to move a hell of a lot faster.
For example, customer development interviews go much more quickly when you’re asking the right questions of the right people. You don’t have to talk to nearly as many users when you know how to not lead them and to interpret their answers well. Observational and usability research becomes much simpler when you know what you’re looking for.
The same is true for quantitative data collection. Your a/b tests won’t seem nearly so random when you’re sure that the information in the system is correct. You won’t have to spend time as much time figuring out what’s going on with your experiments if you trust your graphs.
Good Data Does Not Mean Complete DataI do want to make one thing perfectly clear: the quest for good data should be more about avoiding bad data than it is about making sure you have every scrap of information available.
If you don’t have all the data, and you know you don’t have all the data, that’s fine. You can always go out and do more research and testing later. You just don’t want to put yourself into the situation where you have to unlearn things later.
You don’t have to have all the answers. You just have to make sure you don’t have any wrong answers. And you do that by setting the bar for “good enough” pretty damn high on your data collection skills.
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