The Key To Unlocking The Power Of AI: Data Trading
One of the major hurdles companies face in transforming to a Digital Supply Chain is their inability to get data from customers and suppliers—or even from other departments in their own company. Nothing new, right?
What is new is the idea of “trading data” to overcome that hurdle and use as a catalyst for Digital Supply Chain transformation. Let me explain.
Companies are aggressively turning to artificial intelligence and machine learning (AI/ML) to gain a competitive advantage. But for that strategy to succeed, companies must develop algorithms that rely on AI/ML technology to run their business. And what is the life force behind algorithms? Data. Lots of data. That makes data trading, internally and with customers and suppliers, essential to unlocking the power of AI/ML.
The critical management question is how to do it?
Understanding how to value and trade data with other departments and with value chain partners starts with thinking about data as you would money. Once you think about data like money, it becomes clear that you have to be strategic in using it.
Consider the negotiations you have with other departments in your organization. Maybe it’s about budgets and who’s going to pay for something from their budget. Just like money, your supply chain department has data that may be really valuable to other departments such as product development or sales. And other departments certainly have data that would help your supply chain to gain more visibility into demand and risks.
The real power of data trading, however, comes from your supply chain. One of the most important aspects of the Digital Supply Chain is collaboration that extends beyond the boundaries of your organization. Just as you exchange money for goods and services, you can exchange your data for data from your suppliers and customers. This is where it gets interesting because unlike money, the value of data is relative. It depends on the context and how it fits into each company’s strategic puzzle.