How Data Informs Business

Policies for Maximizing the Data Economy

In addition to policies for the management and governance of data itself, a number of complementary data-related policies exist that governments can pursue to support the development of the data ecosystem and ensure that access to opportunities is inclusive. Digital skills and data for innovation and entrepreneurship are discussed here.

Data skills

To take advantage of the data economy, more people need to have the requisite digital skills. Educational programs that employ rapid skill training are increasingly demanded to develop data skills and capabilities for the use of data tools for innovators, entrepreneurs, SMEs, other private sector entities, and government agencies. According to Cisco, a shortage of 1 million people to fill data security jobs will exist over the next five years, and demand for data scientists between 2011 and 2013 alone increased about 40 percent. Data skills and tools have become crucial among firms, governments, and, particularly, entrepreneurs.

Data literacy is increasingly considered a core skill, with some research suggesting that 90 percent of jobs within advanced economies already require a measure of digital or data skills, while less than one-third of the population possesses adequate skills. The gap in developing countries is even wider. This is a gap that governments must close quickly.

Governments have employed different models to promote digital literacy. Examples include the following:

• Inclusion of digital literacy as part of government-supported basic skills programs, such as the Skills Plus program in Norway.

• Support to advanced digital skills. In the United Kingdom, for instance, the Government Digital Services supports a range of programs, such as the Tech Partnership (a network of employers focused on developing digital skills) and Doteveryone (an independent think tank focused on the digital society).

• Programs aimed specifically at women and girls, who are often underrepresented in the ICT sector. Examples include the Intel-backed She Will Connect initiative in Nigeria, Kenya, and South Africa, and Mozilla Learning's partnership with UN Women to support a network of web literacy clubs in Kenya and South Africa specifically aimed at upskilling girls and women through face-to-face peer learning.

• Mentoring and peer learning based programs. Such programs include Reboot UK, the Swedish IT guide program (which pairs immigrants with elderly Swedes), and the "CompiSternli" program in Switzerland (which pairs children with the elderly).

• The incorporation of coding into school curricula. This is done in the e-school program in Estonia and similar programs in Denmark, the United Kingdom, and the United States.

Some lessons and policy recommendations for governments to consider from these various digital skills initiatives include ensuring data literacy programs are multistakeholder (including participants from the government, private sector, and civil society); building on existing programs, where possible, rather than starting from zero; blending traditional nondigital education with data and digital literacy; bridging formal and nonformal sources of education, such as using mobile phones as a learning tool in developing countries, especially for refugees; and developing societal teaching capacity and mentorship programs.

Data innovation

Companies with huge amounts of data at their disposal and the technical capacity and skilled employees to analyze the data will gain competitive advantage.

In the digital revolution, access to large and diverse data sets is a prerequisite for innovation. Policies geared toward unlocking the reuse potential of data can boost the data economy so that businesses and governments are not left behind, but put forward at the frontier of innovation.

Public and publicly funded data can be at the service of data-driven innovation. Access and reuse of public and publicly funded data can constitute a cornerstone for a data economy. Policies aiming at making more data available and making data more reusable include policies to lower market entry barriers, particularly for SMEs, by reducing charges for the reuse of public sector information.

The nature of data-driven innovation also raises new challenges, including how to safeguard competition and to avoid using data as a barrier to the next generation of entrants and innovators. Given the value of controlling large amounts of data, there can be winner-take-most dynamics of companies benefiting from network effects (that is, where the more people that use a platform or service, the better the experience of everyone else using it). Although it is beyond the scope of this report to discuss competition policies, the treatment of data-sharing policies and the handling of data within intellectual property rights protections will increasingly be central parts of them. Another way that governments can address the risk of excessive first-mover advantage is ensuring that its own data-sharing arrangements do not result in few re-users able to exploit the data in practice. Increased transparency of public data reuse can allow any company, regardless of size, to be aware of the data available and promote a broader spectrum of re-users exploiting the social and economic value of data.

The EU estimates that, in 2016, some 254,850 data companies existed across the union, and that the figure could grow to some 360,000 by 2020 under a high-growth scenario.

• Government innovation. Government laboratories such as fab labs, data labs, and urban labs have emerged across regions. In 2016, the government of Mexico launched its Datalab for data analysis to improve Mexico's public policy formulation and management. Among cities, Barcelona's CityLab and Mexico City's Laboratorio para la Ciudad are examples of municipal level interventions for urban innovation using data.

• Private sector innovation. Policies are needed to build awareness, capacity, and adoption; and to promote cross-cutting uptake for market analysis, financial inclusion, value chain integration, and know your customer across sectors. As discussed in chapter 5, to ensure these policies reach a majority requires attention to SMEs and to the underlying layer that can connect firms to customers, vendors, associations, governments, and so on.

• Citizen-driven innovation. Innovation policy traditionally supports the "supply" side by funding research and development in areas deemed to yield scientific market results. Demand-driven innovation policies, in which processes are driven by the end beneficiaries rather than researchers, aim to ensure instead greater relevance and uptake. This is the case of data policies that consider that social innovation can promote citizen engagement and creative thinking about alternative ways to provide services and address problems. An example of this approach in Tanzania, Data Zetu, is part of the Data Collaboratives for Local Impact program, and aims to empower communities in Tanzania to make better, more evidence-based decisions to improve lives. Data Zetu works with stakeholders to build skills and develop digital and offline tools that make information accessible to everyone. Civic tech, crowdsourced programming, and open innovation processes to tackle development challenges can bring together the skills and technology needed to make a difference in the lives of those who need them most.

• Development innovation. Data is also shaping the traditional paths of development. The UN-coined "data revolution" has triggered novel development approaches that help analyze the context, measure impact, and coordinate project efforts on the ground, among others. Data is a cross-cutting tool for achieving the Sustainable Development Goals. Development is about knowledge, and data amplifies the power of development assistance as the building block of knowledge.

• Data entrepreneurship. Governments should ensure that other sources of innovation investment, ICT industry stimulation, and start-up incubation are playing their part in supporting the growth of innovative uses of data and of the supporting ecosystem of ICT and other services. In 2015, as noted, the Open Data Incubator for Europe was launched to support the next generation of digital businesses and fast-track the development of their products. Within the six-month incubation program, companies receive up to €100,000 (US$116,000) in equity-free funding, mentoring, business and data training, high-quality media, visibility at international events, and introductions to investors.

Policies for data-driven development

In examining data policies for the digital economy, it is easy to focus on the dark side ­– combating cybercrime, threats to data security, loss of privacy, and similar matters. But the data economy is not only about policies to mitigate risks; it is also about policies to maximize value. The true value of data is largely in its use. A strong demand-side "pull" of data is important. It creates and maintains pressure on expanding ubiquity. And it ensures that the wider data ecosystem develops and that data is turned into economic or social value with positive impacts for citizens. As shown throughout this report, the "pull" can come from governments, civil society organizations, the private sector, academia, journalists, international organizations, and donors, as well as from individual citizens. Data-driven development involves all of us.