Google Data Supply Chain: AI Supply Chains In A Nutshell

A classic supply chain moves from upstream to downstream, where the raw material is transformed into products, moved through logistics and distributed to final customers. A data supply chain moves in the opposite direction. The raw data is “sourced” from the customer/user. As it moves downstream, it gets processed and refined by proprietary algorithms and stored in data centers.

Traditional supply chains

The supply chain is the set of steps between the sourcing, manufacturing, distribution of a product up to the steps it takes to reach the final customer. It’s the set of step it takes to bring a product from raw material (for physical products) to final customers and how companies manage those processes.

In a traditional, and physical supply chain, we move from upstream to downstream, as we go from raw materials to the finished good, distributed in the hands of customers and consumers.

While the physical supply chain is structured by starting from sourcing, manufacturing and logistics. Distribution (intended as the finished product ready to be sold) comes at the end of this process.

There is another kind of supply chain, which for tech and AI companies is integrated within their business models, that is the data supply chain.

From physical to data supply chains

Let’s take a simple example. When Google manufactures its phone, the Pixel, while the phone is designed by Google, its manufacturing is outsourced in China (even though Google is moving it to Vietnam).

This means that up to sourcing and manufacturing, the process is outsourced, where Google instead takes control of the process as the phones produced are shipped to Google and ready to be distributed, either through its online stores, through carriers or other indirect channels.

So the whole physical supply chain might look like the following:

Yet, if we flip the perspective and look at the data side of Google, as an AI company, the whole “data supply chain” changes.

Data supply chains: flipping upside down the physical supply chain

In a data supply chain the closer the data to the customer the more we’re moving downstream. For instance, when Google produced its own physical devices. While it moved upstream the physical supply chain (it became a manufacturer) it moved downstream the data supply chain as it got closer to consumers using those devices, so it could gather data directly from the market, without intermediaries.

Where the Pixel phone moves from upstream to downstream, as we saw, it follows a classic supply chain path.

However, once the device is in the hands of customers/consumers/users, they suddenly become the sources of data, and the whole supply chain flips upside down.

A few things to notice here:

  • In the Google’s Pixel case, the whole data supply chain is controlled by Google.
  • Customers become also the sources of raw data.
  • As the raw data move downstream it gets refined by Google’s algorithms and it gets used for several purposes (from products’ personalization, to monetization of its assets through advertising).
  • That data gets stored in the Google data centers
  • And the Google data centers will need to source renewable energies and materials to maintain, and run its facilities, that keep the whole infrastructure going.

From traditional supply chain to AI supply chain

  • AI supply chains start with the sourcing of data. This flips them upside down.
  • Where a traditional supply chain would start with sourcing and manufacturing with a top-down approach, an AI supply chain starts bottom-up.
  • The source of raw data is the customer/user, as the raw data moves downstream the supply chain, it gets processed, refined and stored.

Read: Google Business Model

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