The most impactful thing a data team can do is establish an experimentation program.

The most impactful thing a data team can do is establish an experimentation program.

My formative data science background was at Airbnb. I joined in 2012 as the 4th data scientist and left in 2017. Over that time, we collectively produced our Experimentation Platform (ERF) and the Knowledge Repo. Most importantly, we had an easily detectable impact that lifted the team's brand internally and eternally.



The most crucial piece of that journey was our experimentation platform, ERF. And it's not just me saying how vital experimentation was; you can read feature Riley Newman, the original head of the data team, to see a similar takeaway. Experimentation plugged the data team's work into the ethos of the company. Suddenly, metrics mattered.



And it's not just Airbnb. Look at the resources spent for every data team that has demonstrated clear ROI. You can read reams of articles about the experimentation practices at Airbnb, Netflix, Spotify, Facebook, and Uber, or even earlier companies likeTubiTV and GetYourGuide. Whether in infra development, compute expense, or worker hours of procedural analytics tasks, these companies heavily invest in pervasive experimentation.



The emergence of a modern data stack has led to an explosion in data teams. These teams easily spin up reporting capabilities, but their contributions are limited to data pulls for board meetings and post-hoc rationalization for product teams. It's easy to serve numbers to teams, but it's hard to find situations where those numbers change anyone's thinking.



How can data teams more meaningfully affect the org and deliver ROI that justifies the presence of the team? The key is experiments.



Every data team should remember why they exist: improved decision-making. Most of the data worker headcount is dedicated to analytics work. Analytics doesn't produce dollars via products that you sell. It comes transitively from better decisions that lead to products that sell. Across every data team, the ultimate goal is to define good choices in the voice of the customer, i.e., via metrics.



The problem is that metrics reporting doesn't itself lead to good decisions. The CEO might be happy knowing how business KPIs moved, but these KPIs don't do much for product teams. Given a revenue dashboard, a product team's only response is to pray that revenue goes up. There's no connection from revenue to product. It's not enough to know that there was an overall 20% YoY increase in revenue; you need to know which product decisions specifically increased revenue.



Data teams need a decision layer. Just like data teams establish a ground truth of metrics, they must establish a ground truth of decision quality. Good decisions incontrovertibly improve the customer experience. They are immortalized in tactics to emulate, teams that get resourced, and people that get promoted. When people tell the story of a company's growth, it's usually in the form of good decisions, and any analysis that doesn't lead to a decision is ultimately forgotten.



Generic metric dashboards rarely show which decisions were good or bad. This is because of two reasons:



The experimentation practices at Airbnb, Spotify, and Netflix simultaneously solve both problems. They seamlessly isolate a product change from every possible effect while providing analytical capabilities to separate the quality of product thinking from the quality of engineering. They deliver these capabilities publicly and widely, aligning the organization with standard interfaces and centralized practices.



In effect, the tooling at these companies don't democratize data computations, they democratize good decision making.



At Eppo, we believe experimentation is a skeleton key to unlocking a culture of metrics. Companies start data teams out of an aspiration to be more quantitative and scientific. But hiring data scientists won't lead to a culture of empirical thinking. To have an impact, data scientists need an experimentation platform to inject metrics into product development.

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