After attending the Toronto Machine Learning Micro-Summit this past week, one theme came up repeatedly during the presentations - communicate with the business team early, and often, or you'll need to go back and re-do your work.
There was the story of an insurance company that created a model that recommended whether to replace or fix a car after a damage claim. It sounded great - the Data Scientists got a prototype up and running and had business team buy-in. But, the problem was that their models weren't very accurate. Usually when that happens it means that your data is noisy or the algorithm isn't powerful enough. They went back to their business team and it turns out that they missed 2 key features: the age of the vehicle and if it's a luxury model.
Another example was a telecom that built a model to optimize call center efficiency. The data science team spent a month building the model and everyone was excited to get it in production. Then, they were told that the call center runs on an outdated application. It turns out that integrating with the application would cost more than the ROI of the project.