Is data quality an obstacle for predictive analytics optimization?
First of all, If you try to put any data that you have into the predictive algorithm, it is going to predict some results, but they’re not going to be what you need. Instead, the algorithm is going to deliver you a set of very low-quality predictions. In other words, it follows the principle “garbage in, garbage out”, in which the decision-making might be flawed due to incomplete, or imprecise data. Improving the quality of the historical logistics data is extremely difficult but it is a must before you even start thinking about predictive optimization.