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Like a lot of fads in IT, Big Data sounded like "if you have a lot of data you can monetize it" so companies threw 7+ figures at the technology and then realise

Like a lot of fads in IT, Big Data sounded like "if you have a lot of data you can monetize it" so companies threw 7+ figures at the technology and then realised that you can have too much data to know what to do with and couldn't really monetize it (obviously some did/still do). Even at a simple level, working at a data collection company, it is very clear that lots of people want to collect as much as possible only then to do precisely nothing useful with the results. Then Machine Learning comes along and these same people think that means that you can just feed the beast with your big data and it will be clever enough to tell you what you want to know and then the same companies will realise that they still don't have the skills to work out what to tell the ML algorithm to do.

brainwipe 4y ago
Rather than the skills, the difficulty most companies have with ML is that they don't understand their data before they begin. There's a data engineering piece of work required before the data science.
BeefWellington 4y ago
> Then Machine Learning comes along and these same people think that means that you can just feed the beast with your big data and it will be clever enough to tell you what you want to know and then the same companies will realise that they still don't have the skills to work out what to tell the ML algorithm to do. My favorite of one of these was an ML model demo we got was the incredibly insightful analysis: "As customer dissatisfaction increases, customer satisfaction decreases."
- monkeybutton 4y ago
  Best one I've encountered is: "Customers whose accounts are up for renewal, are the ones most at risk of churning"
  - eb0la 4y ago
    One insight we found (with just a calculator) is that 85% of our customers that say they will leave, will stop beign our customers. Sometimes you don't need a huge machine learning model. Just ask the customer, record answer, and outcome.

brainwipe 4y ago

Rather than the skills, the difficulty most companies have with ML is that they don't understand their data before they begin. There's a data engineering piece of work required before the data science.

BeefWellington 4y ago

> Then Machine Learning comes along and these same people think that means that you can just feed the beast with your big data and it will be clever enough to tell you what you want to know and then the same companies will realise that they still don't have the skills to work out what to tell the ML algorithm to do. My favorite of one of these was an ML model demo we got was the incredibly insightful analysis: "As customer dissatisfaction increases, customer satisfaction decreases."

monkeybutton 4y ago
Best one I've encountered is: "Customers whose accounts are up for renewal, are the ones most at risk of churning"
- eb0la 4y ago
  One insight we found (with just a calculator) is that 85% of our customers that say they will leave, will stop beign our customers. Sometimes you don't need a huge machine learning model. Just ask the customer, record answer, and outcome.