When you cross the street, you constantly assess the situation for hazards and time your movements carefully. Now imagine you’re blindfolded, and have to navigate the street as follows: whenever you want to know about your surroundings, you need to ask for a report. Some time later, a guide will rattle off useful information like the density of cars in the immediate vicinity, how that density compares to historical averages, or the average mass and velocity of recent cars. Good substitute for vision? I doubt it would get anyone across the street.
This is how many companies operate: swimming in a sea of data about their products and teams. Too much of this information isn’t actionable, meaning that we can’t use it effectively to make decisions. Yet even those who have access to good actionable metrics don’t take maximum advantage of data. It’s a condition I call datablindness, and it’s a painful affliction.
The condition is especially common in startups, thanks to the extreme unknowns (customer needs, competition, market dynamics) inherent to new companies. In order to use data effectively, we have to find ways to overcome this blindness. Periodic or on-demand reports are one possibility, but we can do much better. We can achieve a level of insight about our surroundings that is much more like vision. We can learn to see.
Curing datablindness isn’t easy, because many people find it refreshingly comfortable. When we only have selective access to data, it’s much easier to be reassured that we’re making progress, or even to judge progress by how busy our team is. For a lean startup, this lack of discipline is anathema. So how do we reduce datablindness?
- Let your data interrupt you. If the results of decisions regularly confront the decision-maker, the decision-maker can constantly evaluate the impact of their choice. Whether this means text alerts when new users sign-ups for a newsletter, or a literal bell that rings every time a sale is made, these reminders can be a great way to place results directly in the forefront of a team’s conversations and decisions. If the volume is too high for these kinds of tricks (no one can work with a bell ringing constantly!), we can still create effective interruptions. Imagine if the creator of a new split-test received a daily email with the results of that test, including the computer’s judgment of which branch was winning. Or an automatic system that sends daily updates on a new feature’s usage to it’s creator during its early phases.
- Show your work. Whenever someone makes a decision, ask them what data supported it (whether that’s qualitative or quantitative). Just the act of asking can have powerful effects. It serves as a regular reminder that data-based decisions are possible, even if they aren’t easy. Companies that ask questions about the decision making process tend to be more meritocratic than those that don’t. Any human organization is vulnerable to politics and cults of personality. Curing datablindness is not a complete antidote, but it can provide an alternative route for well-intentioned people to advocate for what they think is right.
- Use pilot programs. Consistently pilot new initiatives before full-scale release, and in general, avoid make big all-at-once changes. Prove that the idea works in micro-scale, and then roll it out on a larger scale. There are a lot of advantages to piloting, but the one that bears on datablindness is this: it’s extremely difficult to argue that your pilot program is a success without referring back to the expectations that got it funded in the first place. At a minimum, the pilot team will have to consult a bunch of data right before their final “success” presentation. As people get more and more used to piloting, they will start to ask themselves “why wait until the last minute?”