Messy handwriting, then hours spent re-entering data into spreadsheets—doubling the risk of mistakes. Human errors are estimated to be 6-12%, and delays the abiltiy to check for and fix errors.

If you don’t have in-house data science experts, expect to spend $60,000–$150,000+ per project—and still start from scratch next time. You’re paying for their time. Isn’t it smarter to use that time wisely? Get clean, pre-processed data from the start.

Just one data scientist can take 8 months to execute even a small data project—at $100/hour, that’s $140,000. Shouldn’t their time be spent on important tasks, not on basics like data cleaning and exploratory analysis?

Nothing is ‘free’. A lack of research-grade ethics, especially in nature data, pose risks to species, places, or people if they are selling data or making the data open access. *Imagine a free nature-logging app that exposed or sold the exact sites of sharks or abalone to poachers.

Tons of data are collected but never used or are under-used. Without analysis and action, it’s wasted effort—costing you time, money, credibility, and smart decisions.

Many organizations have hotlines for public reports—sharks, jellyfish, turtles, seals, invasive species, and more—but the experience is frustrating, leading to apathy, underused data, and growing conflict.