The well-worn adage that a company’s most valuable asset is its people needs an update. Today, it’s not people but data that tops the asset value list for companies. And it will become increasingly difficult to compete with large enterprises that are amassing huge volumes of this asset.
By “large enterprises” I mean corporate behemoths such as Amazon, Facebook, certain leading financial institutions, and telecommunications companies. These organizations are collecting data on customers on an unprecedented scale. When combined with advances in artificial intelligence and machine learning, and ever-expanding sensor networks, they will establish a grip on markets that will be extremely difficult to break.
In some cases, they have already carved out market positions that to all intents and purposes are impregnable.
This is not a dystopian vision, but a fact of life as we move deeper into the age of digitalization. My professional field, supply chain management, offers a glimpse of this new reality.
For example, one of the most taxing challenges facing supply chain professionals is matching supply with demand, especially in notoriously volatile businesses such as fashion apparel. The dramatic growth of e-commerce has compounded the challenge. Get this balancing act wrong – and companies frequently do – and the result is excess inventory or lost sales and customers, both of which can be immensely costly. Create large imbalances on a regular basis over an extended period, and your days as a viable business are numbered.
Companies that are able to create granular, accurate demand forecasts that can be modified on the fly in response to unexpected demand shifts, and closely tie these to manufacturing and delivery operations, can avoid such missteps and stay ahead of the competition.
Some companies, notably the retailer Zara, have become masters at responding speedily to demand changes with new designs and moving products to store shelves to take advantage of new selling opportunities.
But big data and advanced analytics can take these capabilities to unimaginable levels. These technologies will yield insights into customer buying behaviors that enable companies to track market fluctuations more precisely and to anticipate shifts in buying preferences with remarkable prescience.
This is happening in so many areas of the supply chain that it’s impossible to adequately chronicle the changes in this piece. But here’s a scenario taken from a blog post written by some of my colleagues at MIT, that illustrates the possibilities that are emerging.
A consumer is conversing with a retailer via a digital assistant such as an Alexa or Facebook Portal. Perhaps the person is interested in buying a garment. All the while, the camera on the device or on her smartphone relays images of the jacket she is wearing to the retailer, as well as her body measurements for a perfect fit. The retailer analyzes the images and combines the analysis with other data, such as weather forecasts and fashion trends in that area. The person’s buying history is also added to the data mix. During the conversation, the retailer searches its proprietary supplier capabilities data bank and sends the customer information to a number of suppliers who have relevant inventory or the ability to custom-make garments on the fly using 3D printing. The suppliers send digital images of their offers to the retailer. The retailer, in turn, sends images of garments that the buyer might want to purchase, with special coupons offered by the suppliers to sweeten the deal (of course, the entire supply chain information exchange and transactions are handled digitally). She makes a choice, and the item is made and delivered. Details of the transaction become part of a demand pattern analysis that is used to hone future demand forecasts for the garment purchased. Relevant intelligence from the conversation enhances the retailer’s market database and provides valuable pointers for designers.
This is just one, relatively small example of how data and the AI algorithms derived from it can integrate a company, its suppliers, and its customers. Companies that possess this type of data wield significant competitive advantage.
It can be rightly argued that technologies such as these will spawn more innovators, who could ultimately compete with the big boys. No doubt there will always be innovators, but the degree to which they can compete with data-rich market leaders is narrowing. The better algorithms in the age of machine learning are owned by those organizations who have more data to train the algorithms. And better algorithms result in stronger business results.
Even if a company does not make products, it can sell this type of data – Facebook is an obvious example (note the New York Times’ expose of secret data trades between Facebook and companies such as Amazon) – increasing its profit potential and consolidating its position as an unassailable source of consumer data.
In the science fiction movie Rollerball, the world of business has been reduced to a handful of mega-corporations such as the Energy Corporation. We are a long way from such a world, but the primacy of data and its impact on the competitive landscape should give us pause for thought.