How Data Informs Business

The Organisation for Economic Co-operation and Development (OECD), in its submission to a Group of Twenty conference, provides useful background about the opportunity of the data economy: "As the cost of data collection, storage and processing continues to decline dramatically, ever larger volumes of data will be generated from the IoT, smart devices, and autonomous machine-to-machine communications". This will require a new approach to thinking about infrastructure in the twenty-first century, with the definition expanded to encompass broadband networks, cloud computing, and data itself, which drives productivity growth.

In the United States, for instance, Brynjolfsson, Hitt, and Kim estimate that output and productivity in firms that adopt data-driven decision-making are 5 percent to 6 percent higher than what would be expected from their other investments in and use of information and communication technologies (ICTs). A study of 500 firms in the United Kingdom found that firms in the top quartile of online data use are 13 percent more productive than those in the bottom quartile. Overall, these firm-level studies suggest that firms' use of data and data analytics raises labor productivity faster, by 5 percent to 10 percent.

Other studies identify several characteristics of digital business that create "dynamic competition and high consumer surplus". Many of these characteristics depend on data as their fundamental lever.

Product and service design

In many industries, data has become the new product, rather than the physical goods that firms traditionally sold. When you buy a custom-fitted suit, you often become an unwitting participant in a data economy in which "clothing companies now see body measurements (data) as one of their most prized currencies". Stitch Fix, for example, which had nearly US$1 billion in sales last year, is really a data company in disguise (it gathers dozens of data points on each customer, including weight, jobs, and past pregnancies). Similarly, the moment you buy a car, you start making money for companies like Otonomo, which sells driving data to third parties. The company has raised US$40 million in investments designed to "move from the age of data mobilization, to the age of data monetization". And finally, when the world's top-ranked tennis player Simona Halep fell out with her clothing sponsor before the Australian Open "she took to the internet to find a design she liked, then ordered it from a seamstress in China. Twentyfour hours later, it was in her hands".

Take a simple example from daily life: smartphone speech recognition can help write text messages three times faster than human typing, a dramatic improvement over just a few years ago when speech recognition was considered an irritant (or an amusing novelty at best). The availability of more and better data (that feeds artificial intelligence) is the single most important reason for this enhancement, and firms that can successfully utilize the ever-increasing amounts of data at their disposal are beginning to separate themselves from their competitors by delivering new products and services that both depend on and generate vast amounts of data.

If data is to be the new oil, then firms must invest heavily in data refineries and new capabilities (see chapter 2). In 2016, Amazon, Alphabet, and Microsoft together spent nearly US$32 billion in capital expenditure and capital leases, up by 22 percent from the previous year. Firms are also investing significantly in developing analytical tools that can make sense of this data in real time and convert this data into artificial intelligence or "cognitive insights". Unfortunately, as the scale of investments required to run data-driven businesses has grown substantially, the marketplace has begun to tilt in favor of large-scale incumbents. Other reports have likewise concluded that the rise of big digital businesses may be squelching competition by using the power of their (data-driven) platforms.

This uneven growth between startups and incumbents is not limited to developed countries and there are still very few examples of scaled up data-driven firms in the developing world (see figure 5.3). Firms in the developing world face several additional data-specific challenges that create a tilted field in the marketplace:

• Low "datafication" of the economy (for instance, government records and archives may not be digitized)

• Limited data talent pool

• Restrictive data policies (localization, poorly developed privacy and consumer protection laws)

• Underdeveloped data ecosystem

• Generally, a higher unit price for data relative to affordability (see map 5.1)

Map 5.1 Average price of 1 gigabyte of mobile data per month, by country, 2016

Data-driven supply chains

Supply chains are a vital way for companies to create value and deliver products and services. Technology-based supply chain innovation initially gave firms such as Walmart, which invested heavily in radio frequency identification chip technology, tremendous competitive advantage. But firms like Amazon, which have mastered data and digital innovation, are now showing the way. Koçoƥlu et al. describe integration with customers, integration with suppliers, and interorganizational integration as the key value drivers in supply chain integration. Information or data sharing (with customers, suppliers, internal functions, and across organizations) is a core component across the entire supply chain and is being remade significantly as businesses digitalize ever more.

That said, McKinsey Global Institute found that as companies have begun to digitize products and services rapidly, supply chain digitization has lagged, (yet the same firms expect the digitization of supply chains to have the highest impact on revenue in the near future). Progress has been especially slow in the management of supply chain data, according to McKinsey Global Institute. Challenges include the development of data infrastructure to manage vast amounts of data (what Ernst and Young called the "out-of-control data growth trap"), the ability to link disparate sources of data, and the development and utilization of tools to analyze data productively. These challenges have been heightened by the growth of data-fueled disruptive technologies ­– such as the IoT, artificial intelligence, robotics, and blockchain ­– that are fast becoming essential elements of supply chain management technology but are frequently beyond the capabilities of SMEs and their customers. Digital and data technologies that have integrated millions of firms and their suppliers in common global value chains are also gradually beginning to separate them.

Amazon is an illustrative example of a firm that has used its mastery of supply chain data to distance itself from competitors but also begin to erode the space of its suppliers and sellers on the platform. With its granular visibility into the operations of both the buyers and sellers on the platform, Amazon has realized that it can manufacture and distribute many products on its platform cheaper than other suppliers can (via the Amazon Basics program). Streamlining of the manufacturing, distribution, and retail of these products, combined with its mammoth scale and superior data smarts, gives Amazon tremendous competitive advantage. Can its competitors without access to any equivalent market data, such as Jumia (see box 5.1), compete?

Marketing and customer relationship management

Customer acquisition, management, and retention are core functions of business and digital and data technologies are transforming this landscape. In many ways, data and digital are the ultimate equalizing force. Firms using digital tools and platforms theoretically have equal access to customers around the world (local laws permitting), can use communication tools and platforms to stay engaged online in real time, and take advantage of a variety of payment systems and platforms plus logistics services to deliver products and services worldwide. This is how Uber was able to reach riders around the world, for instance, and how Instagram became a global rage. If these firms could acquire millions (and even billions) of customers and scale globally quickly, then so can other firms if they can create appropriate products and services.

There is an element of truth to this theory, but data confers several advantages on incumbents (indeed, it maybe argued that several disruptors succeeded because the incumbents were not yet digitally or data savvy). Some of these examples include the following:

• Personalization. Incumbent firms can often deliver more personalized products and services to customers given the vast amount of data they have collected about them.

• Predictive analytics. Firms can use their vast data troves to predict the movies you like, the books you are likely to buy, and your likelihood of trying rival products and services. This gives them a significant advantage against competition.

• Prescriptive analytics. Data-smart firms are able to react to events in real time to resolve customer management issues (for instance, vouchers to compensate for a delayed flight, rather than a routine customer survey, for instance).

Data-poor firms are at an inherent disadvantage in these scenarios.