From Data-Driven to Language-Driven Product Decisions

Today there is heavy emphasis on data in product management. And that makes a lot of sense when your data is relevant, reliable and timely. Data can help make the decision environment less ambiguous and more precise.

For example, likes, comments, reactions, reposts and other data offer valuable insights into social media user engagement. These applications are designed from the ground up to elicit such data from members of the social network, which means that these actions are not just the point of using the app, they are also the means of measuring its success.

Nevertheless, when we really get into what it means to be 'data driven' more broadly, it’s not really about data per se, but about the insights that data can provide. And that means interpreting data to make decisions, which is language-driven.

Let's shift our perspective from viewing the world as constructed out of data to seeing it as constructed out of language.

Once we change our assumptions in this way it becomes easy to see something we already know (but in many cases forgot): what ultimately drives product decision-making, good or bad, is the quality of our conversations.

And so what I’d like to explore is whether we can find ways of using language more precisely in the sphere of product decision-making. I want to see if there is a way to do this enough to obtain the benefits of data without the need to instrument systems for collecting and interpreting data in a large and effortful way. Because at the end of the day even after collecting all this data we still have to interpret it, share findings, obtain consent from stakeholders, etc. all which bring us back to the field of language anyway.

Stay tuned for future posts in which I will explore this topic more.