Digital technologies are driving major technological shifts in today's economy. While the impacts of these shifts were initially confined to areas such as e-commerce, media and entertainment, they are having cross-sectoral economic impacts through the digitalization of productive sectors such as manufacturing and agriculture.
These technological shifts are not only disrupting traditional production processes. In services, the rapid growth of digitally delivered services has led to the emergence of new service sectors, such as finance services and cloud computing. In manufacturing, new “Industry 4.0” innovations associated with robotics, artificial intelligence (AI) and machine learning (ML) are reshaping models of industrial organization.1
As the figure above shows, the implications of these technological shifts are not limited to advanced economies.2 While developing countries are seeing rising numbers of digital users, they don’t seem to be experiencing a similar expansion of digital leaders; these continue to be concentrated in a handful of advanced economies (see the map below). The digital economy has the potential to open up new avenues for technological and industrial development, but technological shifts also threaten to widen the global technological divide, exacerbating structural inequalities.
Data and the data value chain
Data has emerged as a key component of the digital economy. It is a cornerstone of business models, whether in the form of data-driven scheduling of transportation, the monitoring of production or the monetization of consumer data. Consequently, data policies are key for creating and capturing value in the digital economy.
While data might be seen as a resource just waiting to be exploited, targeted actions, infrastructure and capabilities are needed if data is to generate value. With the growing complexity of data, it is useful to break down the related processes into an illustrative “data value chain” (see figure below). Based on the data value chain, we can investigate the distinct stages through which data are produced/collected, stored, analysed and used to feed into decision-making processes.
Simplified data value chain
Aligning industrial and digital policies in developing countries
Many countries in the technology race are “digital latecomers” and lag behind the digital cutting edge. This group includes emerging and developing countries, but also parts of Europe.3 While policies to support the technological capability development of latecomer firms are well known, less is known about how industrial policies can be aligned with strategies for the digital economy.
The underlying reasons for developing a data policy include, among others, cybersecurity, consumer protection and privacy. Yet data policies are increasingly incorporating economic objectives as well, moving beyond a simple binary of “data blocking” versus “free flow of data” to include a broader range of policy tools along data value chains.4
Each stage within the data value chain entails different costs, activities and capabilities. In the data collection stage, revolving around technologies such as IoT and digitisation of machines, policies supporting data standards, data sharing and government data are important. For storage, instead, where distributed systems and databases are key, requirement will centre more closely on supporting digital infrastructure. The analytics stage necessitates a range of capabilities for data processing, data science and machine learning. The final stage relates to the applications, where businesses and service integration will be crucial. Hence, the distinct skills, technological requirements and data policies implemented in latecomer economies will depend on their stage in the data value chain.5
Considering data pathways
National data policies continue to evolve, but we are beginning to see some coherence around four major pathways where governments focus on specific stages within the data value chain.
The first is data sovereignty and localization rules as a foundation for data ecosystems. Several countries are nurturing local data economies by incorporating more targeted industrial policies around data localization and local control. Their efforts are often positioned as digital or data sovereignty. The second is strategic government initiatives to support data economies. Governments in several developing countries are implementing strategies to promote accessibility and use of strategic data as the foundation for building a data economy.
Several countries that are in the information processing and data analytics stage are exploiting opportunities in “low value” data processes. These include activities such as simple analytics, content curation and “clickwork” (routine online tasks). It is not clear, though, whether these low value data activities will serve as a stepping stone to higher tiers in the data value chain.
Finally, some economies are building sector-specific applications linked to data. Policies that drive the adoption of data-rich applications can ultimately lead to new opportunities and demands. The focus here is on data used in key industrial sectors and applications, supported by data capacity-building, industrial data infrastructure and national demonstrators.
These four pathways show that data policies are being integrated into broader sectoral and economic policies, and aligned with broader development goals. These pathways are still emerging, however, and it is not certain whether they will lead to technological catch-up. Yet they provide an important first step for exploring how data policy can be integrated into industrial development strategies going forward.
Disclaimer: The views expressed in this article are those of the authors based on their experience and on prior research and do not necessarily reflect the views of UNIDO (read more).
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