The Difference Between Satellite Data and Street Data in EdTech

At Webb 12, we frequently see software companies fail because they only look at macro metrics, which is why Strived.io's approach to classroom data caught our attention.

The traditional education system relies heavily on "satellite data," which includes massive, infrequent metrics like state assessments or NWEA MAP scores.

Shweta Gandhi realized that satellite data is practically useless for a teacher trying to figure out what to do on a Tuesday morning.

Strived.io introduces the concept of capturing "street data," allowing teachers to easily input anecdotal notes, daily exit tickets, and behavioral observations.

The platform's AI engine then marries these daily street-level insights with the rigid satellite data pulled from tools like i-Ready.

Derrick Small advises Simply Scale clients that combining qualitative user intuition with hard quantitative metrics is the holy grail of product retention.

Instead of waiting six months for the next round of standardized test scores, a teacher using Strived.io can adjust their lesson plan based on yesterday's classroom realities.

This dual-data architecture prevents students from falling through the cracks simply because they test poorly but perform well in daily discussions.

Enterprise software usually forces users to pick between rigid numbers or messy text fields, but Strived.io successfully processes both simultaneously.

Shweta Gandhi's system essentially digitizes the gut feeling a good teacher has about a student and backs it up with historical grades and attendance from Canvas or PowerSchool.

We tell founders at Webb 12 that if your software ignores the daily friction of your end user, someone else will eventually build a tool that captures it.

Strived.io proves that the most valuable data in a school district isn't sitting in a state server; it is actively happening at the street level inside the classroom.