Data-Driven Development

Companies are also developing new markets and making profits by analyzing data to better understand their customers. This is transforming conventional business models, as explored in chapter 2. For years, users paying for calls funded telecommunications. Now, advertisers paying for users' data and attention are funding the internet, social media, and other platforms, such as apps, reversing the value flow. The share of the value extracted by the network providers is shrinking, threatening future investment. Good business models for investment in telecommunication networks typically have high up-front sunk costs, but very long-term returns. Twenty to thirty years ago, companies that built networks – such as NTT, China Mobile, AT&T, or Deutsche Telekom – were the champions of their respective national stock markets. Their assets, like the infrastructure that they put in place, represent the backbone services operate on. But their market values have fallen in comparison to the businesses gathering and storing data – such as Google and Alibaba Global – thanks to these existing infrastructures. Stock markets, in turn, assign huge potential to these data-rich companies, and undervalue the companies that keep the digital plumbing working.

We have seen this pattern before. In the early part of the nineteenth century, the markets of the time afforded optimistic valuations to the companies that built railroads. But as the century drew on, railroad investors went bankrupt or were nationalized because of their huge debts, even as the companies whose products they carried, such as mail-order companies, thrived in the early twentieth century.

Once again, we face an inflection point. For more than a hundred years, infrastructure companies made their money primarily from subscriptions and usage charges paid by users – who paid by the minute, by the mile, and lately by the megabyte. This is changing. The value of telecommunication networks is now not so much in data transport as in data storage. As chapter 2 shows, the companies with the highest market valuations are those that collect then monetize their customers' data through targeted advertising. Services from Facebook, Google, or Tencent are largely "free" at the point of use – yet their bandwidth requirements grow ever larger, as does their customer reach.

Beyond internet business or commercial applications, multiple opportunities also exist for harnessing the value of big data and artificial intelligence to help us achieve shared development objectives, as exemplified in chapter 3. However, global efforts to develop new frameworks for the responsible use of emerging technologies must address their implications for society and the consequences of both using data and algorithms, and of failing to use them.