A data-first approach to data science
Data science and analytics teams spend 80% of their time searching, cleaning, connecting, and coding their way through data sources to find the ones that train effective models. In a world where access to algorithms is equal, the only way to bring your models from the sandbox to making an impact in production is with the right data — data you don’t have.
Give your data context
Your data only tells part of the story — what happened to your business. But there’s a great, big world out there that can tell you so much more about what’s going on. You can’t make reliable predictions in a silo.
Train your ML models on relevant data
The data you need is often not the data you have. The old truism “garbage in, garbage out” does not lie. Your models can’t learn what they need to if they’re fed irrelevant data.
Complete the puzzle
Without external data, you only see a fraction of what’s happening to your business and in your industry. No one ever completed a puzzle with half the pieces and said: “it’s perfect!” The same goes for your ML models.
