I have a lot of sympathy for the view expressed in the following tweet
Good CS expert says: Most firms that thinks they want advanced AI/ML really just need linear regression on cleaned-up data.
— robin hanson (@robinhanson) November 28, 2016
Many organizations who dive into machine learning haven’t even started to extract value from their data.
I understand why they want to get on the train. If you’ve not managed to draw value from your data yet why not just shovel it all into these amazing algorithms and let insights flow out the other end. Jump to the latest technology. Makes sense.
Unfortunately that’s not how data science works. The success of any data science project depends on you understanding your business and your data.
Some things you might do before adopting machine learning
- Work out what data you need to help you manage and improve your business
- Introduce your managers to your data people
- Clean and curate your data
- Put systems in place that give you real-time access to useful simple statistics—averages, totals, trends, etc.
- Use simple statistical tools—like linear regression—to get deeper insights
- Make use of decision science techniques (e.g. game theory)—not everything is amenable to running masses of data through algorithms
If you’re ready for it, machine learning is fantastic. However, if you deploy it as a magic bullet, don’t be surprised when you shoot yourself in the head.
