1) properly define the problem to be solved (don’t shoot in the dark); 2) identify a key target variable to predict (must be a good decision-making metric in the company); 3) determine what “good” means, success-wise (what is the baseline for success?); 4) identify the appropriate data that can aid in prediction. There’s also: 5) finding the right algorithms, but this doesn’t matter unless 1-4 are nailed.
I also plan on talking about the importance of proper perspective in building models. While we want predictive models to be good, even excellent, but in the end, we need the models to improve decision-making over what is done currently. I'm not advocating low expectations, just reasonable expectations.