Many have emphasized the need for data for artificial intelligence (AI) and machine learning (ML) algorithms, and metaphors from "data is the new oil" to "data is the new sun" further exacerbate the dire need for better data. However, one aspect of data that is often not explicitly mentioned in these circumstances is the role of master data and how it fundamentally impacts the quality of data that is driving the ML algorithms. In the spirit of paying tribute to management guru Peter Drucker, who's credited with the saying, "culture eats strategy for breakfast," this article explores: According to The DAMA Guide to the Data Management Body of Knowledge, master data represents "data about the business entities that provide context for business transactions." Simply put, for any enterprise, it is the customers whom they sell to, the brands they market, the products they sell, the consumers who use their products, the materials used to make the products, the plants that manufacture their products, the suppliers that supply the materials, the employees who build the products directly or indirectly, and the list goes on. Why is there a lack of awareness in enterprises about master data?

In the face of a global health and economic crisis, many traditional companies have suffered tremendous losses, and some have shuttered their doors. Those that heavily rely on physical capital (for example, stores, goods) and human capital (for example, services) were already vulnerable in economic downturns. The pandemic has exacerbated the lack of resilience in these business models, which have struggled to compete against digitally centric companies that can leverage data and machine learning to create valuable insights, intelligence, and capabilities across the organization. For instance, compare companies whose products are like air (customers rely on them all day long for business, personal, or financial use) with those that are like haircuts (customers use them sporadically; they are nice to have but are not critical to their needs). Those in the former category that are being used constantly with little effort have proved to be resilient even in times of crisis.

Ability to quickly learn how to use software applications.

The world is getting smaller. Transportation routes are getting faster, storage times shorter. Resources are available anytime, anywhere. The worldwide trade knows no bounds, and it comes in many shapes and forms. In the future, intelligent international trading companies will shift from focusing on transactions and wares to the interaction with customers and suppliers.

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. What is new is the idea of "trading data" to overcome that hurdle and use as a catalyst for Digital Supply Chain transformation. 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?