Data we collect about our products are really just a summary of the thousands of stories our users would tell us if they could. Part of our job as product managers is gathering and processing these stories, and then converting them into the products and tools that enhance the human experience. Taylor Murphy provides some insight into how product managers can approach data science in this episode.

In this episode of the Product Momentum Podcast, Sean and Paul are joined by Taylor Murphy, Head of Product and Data at Meltano, an open-source data platform whose mission it is to make data integration available to all by turning proprietary ELT solutions into true open-source alternatives.

Part of the PM’s role is to be the conduit through which data are shared, what Taylor refers to as being “the glue and message broker between everyone to make sure folks are aligned.” But data are only one part of the message. And not all data are created equal.

“We’re gathering insights from the market. We’re listening to consultants. And we’re digesting what others are saying about our space,” he adds. “The challenge for PMs is integrating all those data points into “Okay, now we’re going to build this feature; now we’re going to fix this bug.”

Catch the entire pod to hear Taylor’s straightforward approach to data management and data science for product managers –

Importance of working with anonymized data
When, in the product life cycle, to use qualitative vs. quantitative data (see below)
Applying the golden rule to data sharing
Risks associated with over-indexing your data
Role of the scientific method in our decision-making process
Knowledge of SQL in the PM skill set

graph-application-qualitative-data-quantitative-date — As Taylor explains, reliance on qualitative data works well when your company is pre-Product-Market Fit. “But it can take you only so far,” he adds. “As time passes and you grow and mature, you can lean more on quantitative because you have more data to work with.”

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