Cause and Effect

TL;DR

We are obsessed with collaboration—tools, methods, best practices. This obsession stems from an idea that ambitious projects, like building a successful company, are mostly about groups of people working well together. This is true, but it is not the full story. In the real world, big projects require not just collaboration but a shared understanding of causality; how each individual decision ripples, wave-like, through the sea of the business. And while there are lots of tools for collaboration, there are shockingly few that model the causal components of a business.

We believe the future of building great companies will focus not just on helping people work together, but on giving people the tools to understand how their decisions affect the entire business.

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How do so many things happen, so much of the time?

Though we may not always feel it, every waking second of our lives is in some ways miraculous. Brewing a hot cup of coffee in the morning is an act only made possible by the scientists and engineers that designed and built the components for your electric kettle; the coffee made possible by the sweaty labor of people in the mountains of Nicaragua, or Ethiopia, or Brazil. To build the Pyramids, Ancient Egyptians not only needed thousands of hardworking laborers but also the operational discipline to organize and execute on such a complex idea. Even getting to the place where any of this is possible was beautifully complex: our unlikely little universe, the first seeds of life, then millions of years of lucky evolution that brought us to the place we are today. 

Life and the things in it are teeming with complexity—even the simplest things are made possible by delicate webs of cause and effect, so many of them deeply interconnected and interdependent.

Who cares, though, right? It would be nice to investigate the causality, the labor, and the ambition behind the fact that you brewed a damn good cup of Nicaraguan light roast this morning. Unfortunately though, that information is not particularly useful—you may have a middling piece of banter to bring up at drinks on Friday, but that’s as far as this rabbit hole goes.

And yet soon as you decide you want to pull off an ambitious project, like building a great company, causality starts to matter a whole lot. All at once those intricate webs of cause-and-effect become of paramount importance. They’re important because you want your company to succeed, and every decision that every single person working for you makes will have some positive, negative, or neutral impact on the probability that you will eventually win.

Causality is especially relevant in business

In some ways, understanding cause and effect is how you win at ambitious projects. When Microsoft decided to put “A computer on every desk and in every home” in 1980, they had a deep understanding of the causal chain—the positive feedback loop—linked to making PCs more accessible. The same could be said about Amazon launching AWS, or Google making Android open source. Understanding causality is important for more than just major decisions, of course: it’s just as relevant when a junior software engineer decides what to spend their Tuesday on, or when a team decides to go in direction X for a feature when they could have gone in direction Y.

Strangely, though, business success rarely gets pinned on a shared understanding of causality. Ask yourself how many times you’ve read a sentence like: the lack of shared understanding of causality is a bottleneck to progress. That might be the first time you’ve read it. 

That’s partly because over the past couple of decades, people building software have been obsessed not with causality but with collaboration—with making it easy for people to work together. This is one immensely powerful step, and today there are many collaboration tools that help to unlock progress. Sadly, we have not seen a similar explosion in tools for understanding cause and effect; tools for business thought. And it takes more than just collaboration to build something that lasts.

Most businesses today cannot model causality in a productive way

If you work at or run a company today, there are very few decisions you can make that do not have cause-and-effect relationships with other components around the company. Your decision to hire a new software engineer might mean you have less money to spend on a marketing campaign that’s working, which means you acquire fewer customers this year—but not hiring the software engineer means it’ll take longer to build the new product feature that expands your TAM.

The hiring example you just read is a drastically reductive, oversimplified summary of the different outcomes associated with hiring a new software engineer. In reality, modeling causality is so complex that most companies just can’t do it well. Instead, decision-making tends to be arbitrary and siloed:

1. The Head of Marketing looks at some old historical campaign data. She finds a campaign that worked pretty well, so she decides to rally the team to launch something similar. In meetings, she justifies this by pointing to the success of the last campaign—and the conversation ends there.
2. The team gets to working on the campaign, and at certain points needs to pull in a couple of software engineers to help. The engineers have no idea what this campaign is or why it’s being worked on, but they do it anyway. This takes their time away from product work.
3. The campaign flops, and at the end of the quarter the CEO comes to the Head of Marketing and asks why they spent so much money on a failed campaign. It turns out that the CEO paused hiring a software engineer because the marketing budget was so high, and now the CEO is wondering why that tradeoff was made. The Head of Marketing doesn’t have a great answer.

The problem here is not collaboration—the teams collaborated! While the Head of Marketing used Figma and ClickUp to get things done on this campaign, the actual problem is separate and twofold: 1. That the decision to run the campaign happened in the first place, and 2. That nobody at the company, not even the marketing team, had a great understanding of the ripple effects of working on this campaign. A junior graphic designer could have told you what they were working on, but they couldn’t have accurately told you what the business outcome was. Nor could they have described the product impact of pulling in software engineers to help.

This scenario, or some version of it, is not rare. It is instead the default mode of operation at most companies. People do their best to make decisions using the limited data and models that they have, and then they use many of humanity’s wonderful collaboration software to carry out those decisions. This can work sometimes, but it’s terribly inefficient—and leaves most everyone at the company in the dark.

We need better business tools for revealing causality 

Today, there are very few good “What If” tools for business thought. Decisions are made on whims, arbitrarily, and without the whole picture. This is especially frustrating because the potential for better business thinking is there—it’s just in a place that has been historically hard to unlock.

That place is finance.

Finance tells the story of a business. If a company has accurate and well-organized financial data, it is theoretically possible to link together all of the cause-and-effect components of a business. And if you can do that, then it is not difficult for a marketing manager to plug Campaign X and Campaign Y into the model, see what impacts it has across the entire business, and make a decision. Nor is it difficult for the CEO, or a junior-level engineer, to open that software and see exactly why the decision to launch a marketing campaign was made, and what tradeoffs are involved.

Of course, that is not the reality today. Most finance people are instead viewed as magical spreadsheet monkeys, smart numbers people who work in their own siloed corner of the business. The people at the company aren’t supposed to understand what finance does or how it works—even if they’d like to.

The rift between finance and everyone else is partially, or perhaps entirely, because existing finance software is more like glorified Excel than a universally accessible tool for thought. Finance software today is very literally not not built to be a black box for making decisions, understanding causality, and asking “What If” questions. It is not built to be used by the broader team as a way to make decisions.

At Runway, we are building the tool for business thinking. Not Excel 3, or Anaplan 2, but a new kind of tool that simulates your business. With this tool, anyone at your company can make decisions not based on whims or partial data, but based on cause-and-effect; on the way their decisions ripple like waves throughout the sea of the business.

The world needs a tool for business thought. For a shared understanding of causality. Having this tool will unlock, and accelerate, progress at every single company.