Three critical mindsets – business case, entrepreneur’s and adoption – close the gap between big data investment and production.
Analytics has gone mainstream in large enterprises. Walking around the office halls, you hear talk of tensor flow, DNN, artificial intelligence (AI) and machine learning (ML), but there is still a large gap between expected and realized ROI from analytics projects.
While you might think technology is the hardest part of an enterprise analytics project, most don’t fail due to inefficient neural networks or bad machine-learning models. They fail because of people.