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Matt Forrest

Matt Forrest

Director of Customer Engineering & Product Led Growth

Matt Forrest has spent over a decade building reliable geospatial data systems, from pipeline design to cloud-native architecture. With 80K+ LinkedIn followers, he’s a recognized voice in the data and geospatial space.

10+

Years in data & geospatial

80K+

LinkedIn followers

Building a Data Stack the Right Way

A deep dive into how Matt Forrest approaches geospatial data architecture and built foundations that are reliable, simple, and scalable

Q & A with Matt Forrest

Q

What was the moment that made you want to pursue a career in geospatial data?

A

I stumbled into the geography department at University of Wisconsin, took a cartographic design course, and built an election map by manually colouring 3,000 counties. When my professor saw it he said "you took everything we talked about and put it on paper." That moment of validation was what started everything. It also made me realise what it feels like to not have the right foundation, and why I now start every project from the bottom up.

Q

When designing a data stack, where do most teams go wrong?

A

They jump to the end. Everyone wants the dashboard and the application, and then the data piece becomes a mess in the background. I always start with three questions: where is your data coming from, how are you storing it, and what does it need to come out as. Get those right first and everything on top becomes scalable and efficient.

Q

You default to reliability over speed. What is the real tradeoff?

A

Accurate, trustworthy data on an hourly update is far more valuable than fast data you cannot trust. With geospatial data the decisions being made are significant. Floods, fires, climate events require a high level of accuracy and a small mistake could be a big issue. I always make sure the data is correct first, then slowly reintroduce speed. Once you have that foundation of trust, you can build everything else on top.

Q

What would you tell someone just starting their data journey?

A

Pick a few technologies and go deep on them. The one I chose was SQL and databases, processing data at its core. If you do that part well, everything else gets easier from there. Don't be afraid of the unclear path. Making mistakes teaches you the right and wrong way to do things, and those lessons carry you further than any shortcut ever could.

Focus on insights. Leave the data Pipelines to Hevo