Measuring Emerging ICT Trends: IoT, Big Data, Cloud Computing, and AI, Study Guides, Projects, Research of Economics

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Measuring Emerging ICT Trends

Johannes M. Bauer

Michigan State University

Plenary Session 6

15th World Telecommunication/ICT Indicators Symposium (WTIS-17)

Hammamet, Tunisia, 14-16 November 2017

Transforming emerging technologies into economic and societal opportunities Document the new ICT value system 1 Inform policy and governance 2 Develop next generation of ICT indicators 3

Digital transformations

• Four key technologies o Internet of Things (IoT) o Big data (analytics) o New computing architectures o Artificial Intelligence (AI)
• Tremendous opportunities to contribute to the 17 Sustainable Development Goals
• New challenges to establish supportive policy and governance frameworks

Internet of Things (IoT) Big Data Public Cloud Artificial Intelligence (AI)

Global Market for Emerging Technologies

(2015-2025*, US$ billion)

Source: MISR 2017; * … estimated

Technological and economic forces

• Exponential performance increases of ICTs (e.g. Moore’s Law, Cooper’s Law)
• Rapidly improving fixed and wireless connectivity (speeds, QoS)
• Ubiquitous, distributed computing power in smart devices and objects
• Massive growth of user- and machine- generated data (“Zettabyte Era”)
• Transition from “pipeline” to “platform” economy accelerates value generation 0 500 1, 1, 2, 2, 3, 3, 4, 2016 2017 2018 2019 2020 2021

Global IP Traffic in Exabytes per year 2016- 2021

Latin America Middle East & Africa Central & Eastern Europe Western Europe North America Asia Pacific Source: Cisco, VPI, 2017 CAGR 26% 20% 22% 22% 42% 21%

Non-linear, dynamic value generation Content, applications, service Networks Devices Development platforms

Fixed Wireless

Voice, data SS7, SONET, SDH Copper, fiber GSM, LTE, CDMA Voice, data Antenna towers, spectrum

A fast-paced digital innovation system Policy and governance

NGN

connectivity

Applications,

services

Internet of

Things

Cloud

computing

Big data

analytics

Artificial

Intelligence

• Innovation unfolds among many interdependent players
• Rapid experimentation, real-time feedback, market selection, replication of successful solutions
• Disruption of existing industries and new “Blue Ocean” opportunities
• Requires adaptive policy making and regulation (both too little and too much regulation is bad)

ICTs are neither good nor bad …

• Effects of unfolding digital transformations are not yet fully known
• Advanced ICTs promise enormous benefits for Sustainable Development Goals (SDGs) and human rights, including o Smart agriculture, smart cities, environmental stewardship o Individual empowerment, better government, improved education
• They also bring new challenges and potential risks o Replacement of human labor by robots and artificial intelligence o Next-generation digital divides, ambiguous effects on income inequality o Surveillance and control by supposed “technologies of freedom”
• Policy appropriate to national conditions is critical (there is no single “best model”) and dependent on reliable indicators

… they need the right policy conditions

• Network infrastructure o Availability of fixed and mobile broadband, smart devices o National and international bandwidth, data centers o Differentiated infrastructure quality of service (speed, latency, jitter)
• All-IP seamless connectivity o Fixed and mobile broadband, LPWANs, NB-IoT, LTE-M
• Complementary user skills o Digitally literate workforce, data scientists, computer scientists o Increasingly powerful software empowers users with appropriate skills and mindset
• Policy responses that enable digital entrepreneurship and innovation o Differentiated based on assessment of national strengths and deficits o Based on good statistical evidence and models (stimulation, foresight)

Knowledge for sustainable development

• The power of emerging technologies is best harnessed using a human-centered design approach
• Requires reliable and continuously updated information o Agreed conventions on data definitions and measurement o Improved accessibility of data to users and entrepreneurs
• Machine-generated data collection and processing o Harvesting of data directly form the digital infrastructure and services o Networks or sensors and devices could generate trusted database
• Roles for the public sector and intergovernmental organizations o Collector of critical, standardized information that is of broad importance o Facilitator of data collection (open algorithms) and availability (open data) o Curator and archiver of data and analytical models (open repositories)

Indicators and models

• Focus on objectives (SDGs, other economic and social goals)
• Development of an enhanced system of indicators o Direct indicators of emerging technologies - Hardware (e.g. # of devices, % of installed base with certain capabilities, revenues) - Basic services and software (e.g. M2M, big data analysis software) - Applications and services (e.g. % of businesses using cloud solutions, AI) o Indicators for enabling conditions - Network infrastructure (e.g. % coverage, quality) - Skills (e.g. % digital literacy, # of data scientists) - Policy arrangements (e.g. % unlicensed spectrum, open data policies) o Effects on outcomes (e.g. income, employment, equality)
• Descriptive, explanatory, predictive, and prescriptive uses/models

Recommendations

1. Short-term: use existing processes and data collection (e.g. EGTI, Partnership on ICT for Development) to develop an enhanced system of ICT indicators for IoT, big data analytics, cloud computing, and AI
2. Medium-term: develop a “System of Digital National Accounts” in which publicly collected and curated, machine-generated, crowdsourced, and case-specific big data complement each other in a coherent framework

Thank you!