April 9, 2019 at 13:25Is your company data valuable in the AI era?
- Transfer
Hello again! Today we continue a series of publications dedicated to the launch of the Big Data for Managers course . So, let's begin.
“AI is close.” This is what we have been hearing since 2017 and, most likely, we will continue to hear further. For established companies that are not Google or Facebook, a natural question arises: what do we have that will allow us to survive this transition?
In our experience, the answer is “data.” The business press also adheres to this point of view. Hundreds of articles have been written claiming that “data is new oil,” meaning that it is fuel that will drive the AI economy.
If so, then you can assume that your company is lucky. You collected all this data and when the AI finally appeared, it turned out that you were sitting on oil reserves. But if you're really so lucky, maybe you should ask yourself: “Are we really so lucky?”
In the analogy “data is oil” there is some truth. As fuel for an internal combustion engine, data is needed for AI to work. The AI takes raw data and turns it into something useful for decision making. Want to know the weather for tomorrow? Let's use the weather data for the previous period. Want to know yogurt sales next week? Let's use data on past sales of yogurt. AI is a data driven forecasting machine.
But do your AI needdata? Today, it is believed that all data can potentially be useful for AI, but in fact this is not so. Yes, data is needed for the daily operation of your forecasting machine. But most likely this is not the data that you have now. Instead, your company is accumulating data that will be used to build a forecasting machine , and not for its operation.
You now have training data. They can be used as material for learning the algorithm. And already this algorithm is used to generate forecasts for actions.
That is, yes, it means that your data is valuable. But this does not mean that your business will survive the storm. Once the data is used to train the prediction machine, it depreciates and becomes useless for this kind of prediction. Continuing the analogy with oil, data may burn out. They are lost after use. Scientists are aware of this. They spend years collecting data, but as soon as they produce results, they begin to collect dust on a shelf or a forgotten flash drive. Your business may be sitting on an oil well, but its reserves are limited. This does not guarantee you anything more in the AI economy than just a more profitable resale value.
Regardless of how valuable your data can be, the ability to benefit may be limited. How many sources of comparative data are there? If you are one of the many suppliers of yogurt, then your databases that contain information about the sale of yogurt over the past 10 years and related data (price, temperature, sales of related products, for example, ice cream) will have less market value than if you would be the sole owner of this data. In other words, as with oil, the more suppliers that have data similar to yours, the lower the value from your training data. The value of your training data is further influenced by the value obtained through increased accuracy of forecasts. Your training data will be more valuable
Moreover, the current value of the data usually depends on the actions taken in everyday business - new data obtained every day that allows you to use your machine for forecasting after training. It also helps improve it through training. 10 years of yogurt sales data are useful for training an AI model to predict future yogurt sales, but real predictions used to manage the supply chain require ongoing data on an ongoing basis. And this is an important point for today's companies.
An AI startup that acquires past yogurt sales data can train the AI model to predict future sales. He will not be able to use the model for decision making unless he receives current operational data for training. Unlike startups, large corporations generate operational data every day. This is valuable. The more operations, the more data. In addition, the owner of the operation can actually use the prediction to further improve future operations.
In the AI economy, the value of your accumulated data is limited to the one-time benefit of learning the AI model. And the value of training data, like oil, depends on the total amount - the more people own it, the less valuable they become. In contrast, the value of your current operating data is not limited to a one-time gain, but rather provides a permanent benefit in the operation and subsequent improvement of the predictive machine. Therefore, despite all the talk that the data is new oil, your old accumulated data is not the main thing. However, they can lead to the main thing. Their value for your prospects is low, but if you can find ways to generate a new, constant data stream that provides a functional advantage in terms of the predictive ability of your AI,
Ask questions, write your comments, and don’t forget that tomorrow, April 10, will be an open day , which will be held by Denis Afanasyev , CEO, CleverDATA .
“AI is close.” This is what we have been hearing since 2017 and, most likely, we will continue to hear further. For established companies that are not Google or Facebook, a natural question arises: what do we have that will allow us to survive this transition?
In our experience, the answer is “data.” The business press also adheres to this point of view. Hundreds of articles have been written claiming that “data is new oil,” meaning that it is fuel that will drive the AI economy.
If so, then you can assume that your company is lucky. You collected all this data and when the AI finally appeared, it turned out that you were sitting on oil reserves. But if you're really so lucky, maybe you should ask yourself: “Are we really so lucky?”
In the analogy “data is oil” there is some truth. As fuel for an internal combustion engine, data is needed for AI to work. The AI takes raw data and turns it into something useful for decision making. Want to know the weather for tomorrow? Let's use the weather data for the previous period. Want to know yogurt sales next week? Let's use data on past sales of yogurt. AI is a data driven forecasting machine.
But do your AI needdata? Today, it is believed that all data can potentially be useful for AI, but in fact this is not so. Yes, data is needed for the daily operation of your forecasting machine. But most likely this is not the data that you have now. Instead, your company is accumulating data that will be used to build a forecasting machine , and not for its operation.
You now have training data. They can be used as material for learning the algorithm. And already this algorithm is used to generate forecasts for actions.
That is, yes, it means that your data is valuable. But this does not mean that your business will survive the storm. Once the data is used to train the prediction machine, it depreciates and becomes useless for this kind of prediction. Continuing the analogy with oil, data may burn out. They are lost after use. Scientists are aware of this. They spend years collecting data, but as soon as they produce results, they begin to collect dust on a shelf or a forgotten flash drive. Your business may be sitting on an oil well, but its reserves are limited. This does not guarantee you anything more in the AI economy than just a more profitable resale value.
Regardless of how valuable your data can be, the ability to benefit may be limited. How many sources of comparative data are there? If you are one of the many suppliers of yogurt, then your databases that contain information about the sale of yogurt over the past 10 years and related data (price, temperature, sales of related products, for example, ice cream) will have less market value than if you would be the sole owner of this data. In other words, as with oil, the more suppliers that have data similar to yours, the lower the value from your training data. The value of your training data is further influenced by the value obtained through increased accuracy of forecasts. Your training data will be more valuable
Moreover, the current value of the data usually depends on the actions taken in everyday business - new data obtained every day that allows you to use your machine for forecasting after training. It also helps improve it through training. 10 years of yogurt sales data are useful for training an AI model to predict future yogurt sales, but real predictions used to manage the supply chain require ongoing data on an ongoing basis. And this is an important point for today's companies.
An AI startup that acquires past yogurt sales data can train the AI model to predict future sales. He will not be able to use the model for decision making unless he receives current operational data for training. Unlike startups, large corporations generate operational data every day. This is valuable. The more operations, the more data. In addition, the owner of the operation can actually use the prediction to further improve future operations.
In the AI economy, the value of your accumulated data is limited to the one-time benefit of learning the AI model. And the value of training data, like oil, depends on the total amount - the more people own it, the less valuable they become. In contrast, the value of your current operating data is not limited to a one-time gain, but rather provides a permanent benefit in the operation and subsequent improvement of the predictive machine. Therefore, despite all the talk that the data is new oil, your old accumulated data is not the main thing. However, they can lead to the main thing. Their value for your prospects is low, but if you can find ways to generate a new, constant data stream that provides a functional advantage in terms of the predictive ability of your AI,
Ask questions, write your comments, and don’t forget that tomorrow, April 10, will be an open day , which will be held by Denis Afanasyev , CEO, CleverDATA .