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Hear ye, hear ye! My name is Kyle, and I present free thoughts on the scaling of orgs, and how decision paralysis manifests as they scale! While Square is better than most, things have definitely slowed down in ways that do not benefit the end result. (1/13)
First thing’s first: As your org scales, its decisions will definitely slow down. This is natural and fine and normal. Expect it. The cost of large decisions will always scale with your org size. (2/13)
Cost refers to both input cost (getting buy-in), and output cost to build the thing (engineering, support, maintenance, etc). Cost usually maps to risk. It’s actually good that decisions are harder at scale, because the risk of getting things wrong at scale is large. (3/13)
As your org/company scales, you will have more people that each know less. You therefore also have more people that need to provide input to reach a sound decision. This is the cost of scale and is inevitable, without turning your org into a dictatorship. (4/13)
But – and this is where my main point comes in – not all decisions are created equal. Jeff Bezos (sorry) has a good framework for this. (5/13)
Type 1 decisions are not reversible – they’re expensive to fix if wrong (“bias towards correctness”). Type 2 decisions by contrast are easily reversible – not free, but cheap. You can try a bunch of them out and see what works best (“bias towards action”). (6/13)
...Earlier I mentioned that you now have more people that know less. This also means they have less context on the scope and impact of each decision they make. When faced with this, most people tend to elevate the impact of each choice they make. (7/13)
Failure #1: This means that you can end up with trivial choices (change some copy! update an empty state! show an alert!) that can easily be reverted or further updated instead treated with an intensity not worthy of their actual scope by product owners. (8/13)
Failure #2: Ignoring context and instincts of people that have worked on the product for a long time. Your data and research is cool, but people who have been talking to customers for years understand the product needs well. Listen to them! Pattern matching is powerful. (9/13)
Failure #3: Be data-informed, not data-driven! Your company hired you because they trust your judgement and your ability to problem solve. Use data from customers to inform your choices, but don’t use it as a crutch. Same reason A / B tests usually suck. (10/13)
Failure #4: There is not one right answer! Many product owners seem to think there’s one right and true answer in every decision, but this isn’t usually true. Everything can be built a million ways, many within the margin of error of “great”. (11/13)
Failure #5: Not everyone has to agree with your choice. This is statistically impossible as a decision group scales. You do not need buy-in from all your peers to move forward. You should probably have more than half, but you’ll never get everyone. (12/13)
TL;DR: Be reasonable, look at data, but not too much. Listen to peers, but not all of them! Trust your gut and institutional memory. Rely on pattern matching. Your choice is probably fine – bias towards action and moving forward! (13/13)
