There are lots of cool examples in the use of math, models and machines to predict, classify and recommend. Some can be found on econsultany's site. Within the domain of marketing analytics there is the application of machine learning whereby we are simply trying to discern cues about our business from the data. It works, we can predict content you might like on NetFlix or recognize a motorcycle from a car (most of the time). But it is hard to do; NetFlix offered a million dollar prize if someone could improve on their approach by 10%.
Why is analytics hard?
Computers can't see.
The more complex the problem and/or approach, the more time we spend on working out how not to generate spurious results. Something as simple as correlation needs human oversight. Here is one of my favorites from a great collection of spurious findings.
It appears that Nicholas Cage movies and drowning in swimming pools go hand-in-hand.
Computers lack what seasoned marketers bring to the table - judgement. At present a lot of the work in the "big data" space is focused on how to best capture human knowledge in the analytics. In image recognition for instance how do we find that there are in fact cats on YouTube if we don't there are cats to begin with?
In short, we are trying to find ways to learn what we don't know we need to learn. This unsupervised learning can be a lot like kindergarten recess where we need an adult on the playground to keep us safe.
Thinking and judgement will never go out of style.
Always ask: Does it make sense?