Underscore VC Interview: The Tricks to Designing Data Products with IOpipe Product Engineer

[Editor’s note: Underscore VC recently featured IOpipe Product Engineer Katie Poe Bayes who shared her thoughts on data design at scale.

Read below for an excerpt from the full interview.]

In today’s data-rich world, data products that turn data assets into actionable insights are becoming increasingly valuable. Technologies that help users surface, process, organize, store, share, and act on data have defined a new generation of products.

That’s why we sat down with Katie Bayes, a product engineer at IOPipe. Katie has deep experience designing data products, and her advice will prove valuable to any teams designing data products.

How do you think about design in the context of data?

Katie: With data products, you’re constantly walking a fine line between simply being a point of access (i.e. the data dump) and doing useful things with that data. You have to strike that balance of doing helpful things like aggregating and organizing massive amounts of data, without obscuring the source. You want to be talking to your users and collecting information about their wants and pain points; however, if you make too many assumptions about what kind of information a user might be looking for, you risk getting into the territory of obscuring.

How do you think about raw vs. interpreted data?

Katie: When your product centers around data, it’s important to keep in mind that: *this is not your data*. That obviously has significance around things like privacy and security, but also how you think about what you’re reporting. When your source data is a metric from a computational process, that’s inherently objective data. It’s a measurement. But when you’re making millions or billions of metrics into a readable and usable product, at some point you have to impose some opinion. Sometimes in the form of hierarchy, sometimes in how you perform necessary aggregations. This makes it absolutely crucial that you never skimp on your discovery process with the product team. To form those opinions of how to usefully present that data, you have to first understand it, both in what it actually means, and how it could be used in a user’s workflow. But through it all, particularly with a technical audience, you don’t want to risk abstracting away from the source data. Aggregate, clean up the view, make it useful, but keep the source data available to your audience in some way.

For a technical/data-literate audience, you have to tread carefully in determining what conclusions you’re making for a user. They might want shortcuts of getting at the data and how it’s structured, so consider carefully: “When do you fall back on the root data and when do you make the root data available?”

For a less technical audience (e.g., marketers), they may not be as interested in the source data. They often times don’t need to see every granular detail behind their data and are rather look for the trend data/insights between the discrete points.

Read the full article on the the Underscore VC blog.

About IOpipe

As the leader in serverless dev tooling for monitoring and observability, IOpipe offers real-time visibility into the most granular behaviors of today’s serverless applications on AWS Lambda.

Founded in 2016 by Erica Windisch and Adam Johnson, IOpipe reduces debugging time from hours to seconds, delivers transparent insights into the behaviors and performance of your serverless functions, and reduces risk for enterprises shifting to serverless.

Working with global brands like Matson, Rackspace, and APM Music, IOpipe empowers engineering teams to deliver with confidence, debug intelligently, and get busy building the impossible.

In other words, IOpipe makes it a lot more fun to be a developer. Visit www.iopipe.com to learn more and try it for free.