Vending machine, automated choice and delivery.
Vending machine, automated choice and delivery.
Data analysis systems should be systematic, like vending machines. Your question or request goes in, and a Useful Data Artifact comes out.

A brief introduction to Useful Data Artifacts — and the next generation of data analysis systems

Jesse Paquette
Mar 11, 2020 · 4 min read

For starters, I’d like to acknowledge Josh Dunn for first using the term “Useful Data Artifacts” in a conversation over lunch at the Boston Seaport a few weeks back. I’d been using the term “Data Artifacts” for some time, but what’s the value of one, if it’s not useful?

What is a Useful Data Artifact?

First and foremost, a Useful Data Artifact is an actual digital thing. It is not an idea, a thought, a realization, or an insight. It’s not in your brain — it’s a structured data object, created when you or an algorithm do something with data.

More technically — a Useful Data Artifact is a nonrandom subset or derivative digital product of a data source, created by an intelligent agent (human or software) after performing a function on the data source.

Our industry loves acronyms, so I’m going to use UDATs to refer to Useful Data Artifacts from here on out. It’s fun to say — give it a try.

Why the generation and management of UDATs are critically important right now.

  • Because data is expanding at an exponential rate, both in scale and complexity.
Messy office
Messy office
Within most organizations, data artifacts are scattered around like this.

Some key properties of UDATs:

  • Digitally storable in a structured schema.

You are probably already creating UDATs now, here are some examples:

  • Analysis parameters — the arguments and settings by which you configure and run an analysis — i.e., the question or request you asked of the data.
Tidy office
Tidy office
Tidy that stuff up.

How to make software systems and processes produce, manage and utilize UDATs:

  • Systematic parameterization and explicit recording of user intent during analysis — i.e. please stop making hyper-interactive dashboards that don’t record anything.

What did I miss?

There’s clearly a lot more to discuss around the definition, utilization, and value of Useful Data Artifacts as the industry evolves.

I should note that at Tag.bio we’ve been producing, storing and utilizing UDATs for some time now — we just never had a good name for them.

Image credits:

Tag.bio — Your data. Your questions. Your answers.

The latest news and updates from Tag.bio.

Jesse Paquette

Written by

Full-stack data scientist, co-founder at Tag.bio, pick-up soccer junkie, located in Brussels and San Francisco.

Tag.bio — Your data. Your questions. Your answers.

Tag.bio is a San Francisco, CA startup solving the last mile problem in data analysis for Healthcare and Life Sciences — with a distributed data mesh architecture, a domain-native user experience, full reproducibility, automated cloud orchestration, and enterprise-grade security.

Jesse Paquette

Written by

Full-stack data scientist, co-founder at Tag.bio, pick-up soccer junkie, located in Brussels and San Francisco.

Tag.bio — Your data. Your questions. Your answers.

Tag.bio is a San Francisco, CA startup solving the last mile problem in data analysis for Healthcare and Life Sciences — with a distributed data mesh architecture, a domain-native user experience, full reproducibility, automated cloud orchestration, and enterprise-grade security.

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