Working at MayaData will expose you to situations of various levels of difficulty and complexity. This requires focus, and the ability to defer gratification. We value the ability to maintain focus and motivation when work is tough and asking for help when needed.
We prefer making results count over perfecting the process. We assume nothing and we keep pushing ourselves until the job is done. We keep our promises and make sure our people keep their promises.
A winner knows deep down that she or he deserves to win and that confidence itself - no matter what the situation - is crucial to achieve a positive outcome. There is rarely success without commitment and rarely commitment without confidence.
We win as a single team and we take pride in contributing towards make each other succeed. If anyone is blocked by you, on a question, your approval, or a merge request review, your top priority is always to unblock them, either directly or through helping them find someone else who can, even if this takes time away from your own or your immediate team's priorities.
At an exponentially scaling startup time gained or lost has compounding effects. Try to get the results as fast as possible so the compounding of results can begin and we can focus on the next improvement.
While we iterate with small changes, we strive for large, ambitious results. You keep picking yourself up, dusting yourself off, and quickly get going again having learned a little more.
It's important that we keep our focus on action, and don't fall into the trap of analysis paralysis or sticking to a slow, quiet path without risk. Decisions should be thoughtful, but delivering fast results requires the fearless acceptance of occasionally making mistakes; our bias for action also allows us to course correct quickly.
The ability to accept that there are things that we don’t know about the work we’re trying to do, and that the best way to drive out that uncertainty is not by layering analysis and conjecture over it, but rather accepting it and moving forward, driving it out as we go along. Wrong solutions can be fixed, but non-existent ones aren’t adjustable at all. The Clever PM Blog.
We value constant improvement by iterating quickly, month after month. If a task is too big to deliver in one month, cut the scope.
We do the smallest thing possible and get it out as quickly as possible. This value is the one people most misunderstood when they join MayaData. People are trained that if you don't deliver a perfect or polished thing you get dinged for it. If you do just one piece of something you have to come back to it. Doing the whole thing seems more efficient, even though it isn't. If the complete picture is not clear your work might not be perceived as you want it to be perceived. It seems better to make a comprehensive product. They see other people in the MayaData organization being really effective with iteration but don't know how to make the transition, and it's hard to shake the fear that constant iteration can lead to shipping lower-quality work or a worse product.
However, if we take smaller steps and ship smaller simpler features, we get feedback sooner. Instead of spending time working on the wrong feature or going in the wrong direction, we can ship the smallest product, receive fast feedback, and course correct.
The way to resolve this is to write down only what you can do with the time you have for this project right now. That might be 5 minutes or 2 hours. Think of what you can complete in that time that would improve the current situation. Don't write a large plan, only write the first step. Trust that you'll know better how to proceed after something is released. You're doing it right if you're slightly embarrassed by the minimal feature set shipped in the first iteration.
People might ask why something was not perfect. In that case, mention that it was an iteration, you spent only "x" amount of time on it, and that the next iteration will contain "y" and be ready on "z".
We believe great companies sound negative because they focus on what they can improve, not on what is working. Our first question in every conversation with someone outside the company should be: what do you think we can improve? This doesn't mean we don't recognize our successes, for example see our Say Thanks value. We are positive about the future of the company; we are present day pessimists and long term optimists.
In some cases, rapid iteration can get in the way of results. For example when adjusting our marketing messaging (where consistency is key), product categories (where we've set development plans), sales methodologies (where we've trained our teams) and this values page (where we use the values to guide all MayaData team-members). In those instances we add additional review to the approval process, not to prohibit, but to be more deliberate in our iteration. The change process is documented on the page and takes place via merge request approvals.
Enable everybody involved to come to the same conclusion as you. This not only involves reasoning, but also, for example providing raw data and not just plots; scripts to automate tasks and not just the work they have done; and documenting steps while analyzing a problem. Do your best to make the line of thinking transparent to others even if they may disagree. Increases accountability when making decisions and difficult choices.
Every job has work that’s less fun than other parts. Every team has projects that succeed, and projects that fail. One of the key determinants of winning individuals is to embrace what you can learn from failure and what you can learn from the parts of the job that you don’t like. Is there a rote task that needs doing and no one wants to do? Figure out how to automate it, how to make it more efficient, or how to do without it at all. Is something wrong with your project and everything is on fire? What can you learn? It’s only a mistake if you do it twice; otherwise it’s just something that you learned from.