Data is an unreliable friend, and hardly anything about it is actually scientific. So what, Data Science?
Over the past 5 years, I have interviewed more than 1,000 candidate data scientists for an apparently highly coveted set of jobs at Evo Pricing. In the process I have learned that the media are portraying a fundamental lie about this profession: throwing data at off-the-shelf algorithms is really not the point.
A fundamental rethink would be appropriate, and it is likely overdue.
At its heart, data science is a noble name for a broad set of number crunching activities that were mostly invented long ago, but recently received a new lease of life from being applied with greatly enhanced technical devices: more data, more processing power, more reasonable outcomes at a cheaper price. …
Machine learning elevates the importance of people using the data. More data enables more automation, which requires more leadership.
Use more data, need more leadership.
In a pyramid-shaped hierarchy, each function is a pillar — each team is independent; its people collaborating to produce analyses that shape decisions at the top.
This pillar management structure only works if machine-assisted companies democratize their data. For people in a machine-assisted company to get the most out of each others’ insights, everybody must be able to access both the raw data and the actionable insights.
More importantly, after accessing the data, every team must be able to act independently on the insights it provides. Having to report up the chain of command means wasting time and allowing valuable insights to stagnate and become irrelevant. So having a team leader who understands how to read the data is vital. …
The best SatNav can even drive your car, but not tell you where to go. Asking the right question is the ultimate, supreme human capability.
Ever since science fiction movies have suggested that machines or robots will someday take over the world, people have entertained the notion that this may actually happen.
Technology has advanced in amazing ways, with artificial intelligence and machine learning pushed to the forefront of innovation. However, the idea that machines could transcend their need for humanity seems patently ridiculous.
This discussion often includes the question of relative value:
Are machines better than people, or are people better than machines? …