Using Data Responsibly

Technological change and evolving business models

Personal data is generated through an individual's actions (such as making a payment using a credit card), through business processes that digitize analog data (such as medical histories), or through consequent machine response (such as call data records). Such data is now increasingly coming from use of the internet, wireless sensors, and the billions of mobile phones around the world. As the world gets more connected, more people are leaving a digital trail, wherever they go and whatever they do.

 This data, which has become more voluminous and granular over time, piqued the interest of various organizations that saw the financial value embedded in it. By the early 1990s, personal data such as telephone numbers and email addresses was widely used for marketing. Companies crunched data to predict how likely people would be to buy a product, and began using that knowledge to come up with targeted marketing messages. As more digital data was collected, organizations began to use increasingly powerful computing tools to manipulate and apply that data. Marketing companies built richer consumer profiles to predict future purchases and manufacturing and services companies to design and model new products.

Companies are now using such data to develop services powered by artificial intelligence (AI), and the bigger the data set, the better the AI. These and other innovations have greatly increased the value of data and its potential for being monetized, or bought and sold as a product in its own right. Data continues to gain value as its potential uses increase. Organizations – including businesses, governments, and others – can derive value from data by applying the insights arising from data's analysis to internal cost and revenue optimization, marketing and advertising, intelligence and surveillance, and automation.

The application of personal data for online advertising has skyrocketed, with the internet now surpassing television as the leading advertising channel. At a forecast US$237 billion in 2018, digital ads are expected to grow from 44 percent of global advertising revenues in 2018 to more than 50 percent by 2020. Facebook and Google accounted for 84 percent of digital advertising revenue in 2017 (excluding China). In 2016, Facebook's advertising revenues were US$27 billion (up more than 1,300 percent since 2010), accounting for more than 97 percent of its total revenues. Google's advertising revenues – US$79 billion (growing 180 percent since 2010) – accounted for 88 percent of its total revenue. Combined, the advertising revenues of these two online platforms were on par with the gross domestic product (GDP) of Morocco.

Data market actors

Table 4.1, complemented by figure 4.1, identifies the main types of actors operating in the markets built around personal data and the relationships among them. Using these categories of participants, it is possible to illustrate a simple model of the data market, as shown in figure 4.2. People produce personal data, the "raw material," which they "sell" (traditionally at zero price) to other market players who then use that data to derive various benefits. Individuals also provide "free labor" on many of these online platforms – by creating content such as posts and reviews and by uploading photos and videos – that data collectors can "scrape" – extracting data from online sources – to infer personal traits and preferences. This personal data, along with the data that individuals generate from their activities, and that might be inferred from their data (such as their political or culinary preferences), is the main source of data for these organizations.

People do not always directly derive value or benefits from this data (until recently, as discussed later). But people have been deriving indirect benefits from their sale of data in services or products that data-using organizations provide. These benefits are discussed in the next section. 

On the other side of the market, the "buyers" are the various organizations that collect and use the data. In some cases, these organizations depend on the data as a necessary input for their operating model, as do online social media, search engine websites, and various information and news sites. Using advertising as a source of revenue, they typically compensate people – producers of the data – with free or highly subsidized access to their services. Hence, data has financial value to those organizations, either immediately when it is sold to other organizations (such as marketing companies) or through the services that an organization offers others (such as a search engine selling advertisements tied to search terms).

In other cases, data is an input into an operating model. Health systems or government services are one example. Their processes are traditionally standardized and have relied in the past on highly abstracted models of user preferences. Data is thus an input into these systems and does not have an immediate financial value but has informational value. This implies that such services are often performed for a fee, whether paid immediately or separately (such as through taxes). However, these interactions do generate significant amounts of data, and thought has increasingly gone into creating more specialized services and choices based on that data (such as in e-government services) or improving the quality of those abstract models to design improved services (such as better medicines or treatments). Businesses can unlock financial value by generating more effective insights from data to launch a new product, reduce waste or costs, enable better decisions, and boost innovations.

One may say that the value of data depends on how and for what the data is used and how well it is prepared (cleaned and organized). In either case, however, data can find its way to other parties. The regulation of those data flows is the responsibility of data protectors, which can include rules pertaining to privacy and sharing of specific types of data (such as health or financial data), as well as rules about electronic transactions. Data could also be held as an "asset" by those who collect it directly or via others, and new rule systems have emerged around concepts such as the "right to be forgotten" by such entities. Hence, data regulators can also protect people by defining and enforcing rules around the use of their data.

In this construction of the generic data market, organizations have an opportunity to capture the value from the data people produce, and they can determine how much of this value returns to those people. As noted, this data has significant financial and political value since it contains information on behavior and preferences. Where people do provide such data voluntarily, it is because they expect to gain some of those benefits – whether it is access to online services or better medical care, or merely the chance to win a competition.

Questions then emerge from the perspective of data producers: are people aware of what data they are providing and under what conditions (or at what cost)? Do they understand the value (monetary and otherwise) of the benefits they receive? Are they able to assign value to the data they provide in a manner that explicitly differentiates between their perception of value and the actual value of the benefits that they already have or could receive? And what might ensure that maximum benefits are delivered to those who produce the data? The following section unpacks the benefits and costs that accrue to individuals as they participate in these data markets as data producers.

Table 4.1 Typology of actors in the personal data market

Figure 4.2 The personal data market