AI is coming. That is what we heard throughout 2017 and will likely continue to hear throughout this year. For established businesses that are not Google or Facebook, a natural question to ask is: What have we got that is going to allow us to survive this transition?
There are hundreds of articles claiming that “data is the new oil” — by which they mean it is a fuel that will drive the AI economy. The “data is oil” analogy does have some truth to it. Like internal combustion engines with oil, AI needs data to run. AI takes in raw data and converts it into something useful for decision making.
Even to the extent that your data could be valuable, your ability to capture that value may be limited. How many other sources of comparable data exist? If you are one of many yogurt vendors, then your database containing the past 10 years of yogurt sales and related data (price, temperature, sales of related products like ice cream) will have less market value than if you are the only owner of that type of data. In other words, just as with oil, the greater the number of other suppliers of your type of data, the less value you can capture from your training data. The value of your training data is further influenced by the value generated through enhanced prediction accuracy. Your training data is more valuable if enhanced prediction accuracy can increase yogurt sales by $100 million rather than only $10 million.