Messy handwriting in the field, then hours spent re-entering data into spreadsheets—doubling the time and risk of mistakes. Human errors are estimated to be 6-12% —digitizing in real-time reduces errors and increases the chance of finding them.

Using R and GIS can easily eat up hundreds to thousands of hours per project each year—it’s expensive, delays publications, limits impact, and slows success.

Millions of dollars 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.

Nothing is ‘free’. A lack of research-grade ethics, especially in 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, pangolin, or abalone—they’d be gone pretty quick.

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, use their time wisely.

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?

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.