June 2, 2020

Minimum viable solutions

The term MVP (minimum viable product) is commonplace in the startup space. I suspect most business people are familiar with it. We also see a number of variations on this term. Each of these pivots around the question: what’s the smallest/simplest version of x?” This is for good reason. Humans tend to complicate matters. It takes deliberate action to not do so.

Much like we complicate the products we build, we complicate our problems. You’ve probably had a not-so-complicated purchase spiral into hours, days, or weeks of research. I’ve done this with cameras, bikes, and even sleeping bags. Software purchases are even worse.

We like the idea that one solution will check all of the boxes. Some of these requirements are non-essential, though. Others are imagined. For example, You buy a travel adapter for Europe, and contemplate getting the whole set… just in case you someday visit the Cook Islands. Adding requirements obscures your situation, adds cognitive weight, and lengthens the search process.

Lately, I find myself asking myself: What’s the dumbest way we could fix this?” The word dumbest” is inaccurate, but it functions better than simplest”, which carries some baggage. Saying dumb” makes me think of stupidly simple possibilities—that might not otherwise occur to me.

During this lockdown, my kids and I are sharing a workspace. This is fine when they do schoolwork, but challenging when they play networked Minecraft with their friends. They grow enthusiastic and start cheering with and shouting at one another. I first told them to be quiet. I then tried working from elsewhere in the house. Neither worked. Later, a friend suggested I buy noise-canceling headphones. I did, and I can now work in my space while they play—and neither is interrupted.

I know: Headphones are an obvious solution to my noise problem. Viable solutions are often obvious in retrospect. This doesn’t make them any less elusive. My bet is that obvious solutions are more difficult to spot for those who work hard. They tend to conflate effort with quality of outcome, which isn’t always the case. In fact, a deliberately lazy mindset might serve us better—because it prioritizes efficiency.

For years I looked for adaptable software that’d help me with personal planning (goals, objectives, habits). Eventually, I created a text document for doing this. Every morning I email the previous day’s notes to myself and revise today’s. This makes it a living document that I regularly reflect on. Could custom software help me do this better? Probably, but everyone would have different ideas for how it should work.

I also wanted a way to track my habits. There are seemingly countless habit tracking apps, all with their own benefits and drawbacks. I tried many of them, but none seemed to stick. So, I created a simple spreadsheet for tracking my habits/metrics. Over time it evolved into something that’s quite functional for my unique needs.

Even this iteration of my blog is simplified. It’s a set of text documents in a Dropbox folder, served up by Blot. It does very little but is highly adaptable. It’s also more useful than my previous solution—because it fulfills a single purpose. It houses ideas I wanted to think through, so, I could get them out of my head.

Finding a minimum viable solution requires you to first identify the root issue. This can’t be a wishlist of possibilities. It needs to be one short sentence. Then look for the smallest (possibly cheapest) solution. Once you’ve identified it, install the product/system and see if it does what you need. If you hesitate along the way, remind yourself that the real cost of switching to another option is probably trivial.

Not every solution you test will be workable. That’s OK, though. These simple solutions are useful because they help break analysis paralysis. You could read a thousand books on how to ride a bike, but that wouldn’t be as useful as trying to ride one. The same goes for solving problems. Any solution—even a bad one—will teach you something. This gives you real data you can apply to the next test.

Think smaller.

efficiency problems productivity solutions

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