African countries are missing the data needed to drive development

Curated on Posted on by Stefaan Verhulst

David Pilling at the Financial Times: “When statisticians decided to track how well African countries were doing in moving towards their 2030 UN sustainable development goals, they discovered a curious thing: no one had the faintest idea. More accurately, on average, African governments keep statistics covering only about a third of the relevant data. To be fair, the goals, which range from eradicating poverty and hunger to creating sustainable cities and communities, are overly complicated and sometimes unquantifiable.

The millennium development goals that they superseded had eight goals with 21 indicators. The SDGs have 17, with 232 indicators. Yet statisticians for the Mo Ibrahim Foundation, which compiled the report, are on to something. African states don’t know enough about their people. 

In this age of mass surveillance, that might seem counterintuitive. Surely governments, not to mention private companies, have too much information on their citizenry? In fact, in many African nations with weak states, big informal economies and undocumented communities, the problem is the reverse. How many people are there in Nigeria? What is the unemployment rate in Zimbabwe? How many people in Kibera, a huge informal settlement in Nairobi, have access to healthcare? The answers to such basic questions are: we don’t really know.  Nigeria last conducted a census in 2006, when the population — a sensitive topic in which religion, regionalism and budget allocations are messily intertwined — came out at 140m. These days it could be 180m or 200m. Or perhaps more. Or less.

President Muhammadu Buhari recently complained that statistics quoted by international bodies, such as those alleging that Nigeria has more people living in absolute poverty than India, were “wild estimates” bearing “little relation to facts on the ground”. The riposte to that is simple. Work out what is happening and do something about it. Likewise, unemployment is hard to define, let alone quantify, in a broken economy such as Zimbabwe’s where cited jobless statistics range from 5 to 95 per cent. Is a struggling subsistence farmer or a street-side hawker jobless or gainfully employed?

For that matter what is the status of a government employee who receives her salary in a useless electronic currency?  According to Seth Berkley, chief executive of the Vaccine Alliance, keeping tabs on unregistered people in the sprawling “slums” of Africa’s increasingly massive megacities, is harder than working out what is going on in isolated villages. If governments do not know whether a person exists it is all too easy to ignore their rights — to healthcare, to education or to the vote. The Mo Ibrahim Foundation found that only eight countries in Africa register more than 90 per cent of births. Tens of millions of people are literally invisible. Mr Ibrahim, a Sudanese billionaire, calls data “the missing SDG”….(More)”

Data gaps threaten achievement of development goals in Africa

Curated on Posted on by Stefaan Verhulst

Sara Jerving at Devex: “Data gaps across the African continent threaten to hinder the achievement of the Sustainable Development Goals and the African Union’s Agenda 2063, according to the Mo Ibrahim Foundation’s first governance report released on Tuesday.

The report, “Agendas 2063 & 2030: Is Africa On Track?“ based on an analysis of the foundation’s Ibrahim index of African governance, found that since the adoption of both of these agendas, the availability of public data in Africa has declined. With data focused on social outcomes, there has been a notable decline in education, population and vital statistics, such as birth and death records, which allow citizens to access public services.

The index, on which the report is based, is the most comprehensive dataset on African governance, drawing on ten years of data of all 54 African nations. An updated index is released every two years….

The main challenge in the production of quality, timely data, according to the report, is a lack of funding and lack of independence of the national statistical offices.

Only one country, Mauritius, had a perfect score in terms of independence of its national statistics office – meaning that its office can collect the data it chooses, publish without approval from other arms of the government, and is sufficiently funded. Fifteen African nations scored zero in terms of the independence of their offices….(More)”.

How to ensure that your data science is inclusive

Curated on Posted on by Stefaan Verhulst

Blog by Samhir Vasdev: “As a new generation of data scientists emerges in Africa, they will encounter relatively little trusted, accurate, and accessible data upon which to apply their skills. It’s time to acknowledge the limitations of the data sources upon which data science relies, particularly in lower-income countries.

The potential of data science to support, measure, and amplify sustainable development is undeniable. As public, private, and civic institutions around the world recognize the role that data science can play in advancing their growth, an increasingly robust array of efforts has emerged to foster data science in lower-income countries.

This phenomenon is particularly salient in Sub-Saharan Africa. There, foundations are investing millions into building data literacy and data science skills across the continent. Multilaterals and national governments are pioneering new investments into data science, artificial intelligence, and smart cities. Private and public donors are building data science centers to build cohorts of local, indigenous data science talent. Local universities are launching graduate-level data science courses.

Despite this progress, among the hype surrounding data science rests an unpopular and inconvenient truth: As a new generation of data scientists emerges in Africa, they will encounter relatively little trusted, accurate, and accessible data that they can use for data science.

We hear promises of how data science can help teachers tailor curricula according to students’ performances, but many school systems don’t collect or track that performance data with enough accuracy and timeliness to perform those data science–enabled tweaks. We believe that data science can help us catch disease outbreaks early, but health care facilities often lack the specific data, like patient origin or digitized information, that is needed to discern those insights.

These fundamental data gaps invite the question: Precisely what data would we perform data science on to achieve sustainable development?…(More)”.

Why policy networks don’t work (the way we think they do)

Curated on Posted on by Stefaan Verhulst

Blog by James Georgalakis: “Is it who you know or what you know? The literature on evidence uptake and the role of communities of experts mobilised at times of crisis convinced me that a useful approach would be to map the social network that emerged around the UK-led mission to Sierra Leone so it could be quantitatively analysed. Despite the well-deserved plaudits for my colleagues at IDS and their partners in the London School of Hygiene and Tropical Medicine, the UK Department for International Development (DFID), the Wellcome Trust and elsewhere, I was curious to know why they had still met real resistance to some of their policy advice. This included the provision of home care kits for victims of the virus who could not access government or NGO run Ebola Treatment Units (ETUs).

It seemed unlikely these challenges were related to poor communications. The timely provision of accessible research knowledge by the Ebola Response Anthropology Platform has been one of the most celebrated aspects of the mobilisation of anthropological expertise. This approach is now being replicated in the current Ebola response in the Democratic Republic of Congo (DRC).  Perhaps the answer was in the network itself. This was certainly indicated by some of the accounts of the crisis by those directly involved.

Social network analysis

I started by identifying the most important looking policy interactions that took place between March 2014, prior to the UK assuming leadership of the Sierra Leone international response and mid-2016, when West Africa was finally declared Ebola free. They had to be central to the efforts to coordinate the UK response and harness the use of evidence. I then looked for documents related to these events, a mixture of committee minutes, reports and correspondence , that could confirm who was an active participant in each. This analysis of secondary sources related to eight separate policy processes and produced a list of 129 individuals. However, I later removed a large UK conference that took place in early 2016 at which learning from the crisis was shared.  It appeared that most delegates had no significant involvement in giving policy advice during the crisis. This reduced the network to 77….(More)”.

Urban Slums in a Datafying Milieu: Challenges for Data-Driven Research Practice

Curated on Posted on by Stefaan Verhulst

Paper by Bijal Brahmbhatt et al: “With the ongoing trend of urban datafication and growing use of data/evidence to shape developmental initiatives by state as well as non-state actors, this exploratory case study engages with the complex and often contested domains of data use. This study uses on-the-ground experience of working with informal settlements in Indian cities to examine how information value chains work in practice and the contours of their power to intervene in building an agenda of social justice into governance regimes. Using illustrative examples from ongoing action-oriented projects of Mahila Housing Trust in India such as the Energy Audit Project, Slum Mapping Exercise and women-led climate resilience building under the Global Resilience Partnership, it raises questions about challenges of making effective linkages between data, knowledge and action in and for slum communities in the global South by focussing on two issues.

First, it reveals dilemmas of achieving data accuracy when working with slum communities in developing cities where populations are dynamically changing, and where digitisation and use of ICT has limited operational currency. The second issue focuses on data ownership. It foregrounds the need for complementary inputs and the heavy requirement for support systems in informal settlements in order to translate data-driven knowledge into actionable forms. Absence of these will blunt the edge of data-driven community participation in local politics. Through these intersecting streams, the study attempts to address how entanglements between southern urbanism, datafication, governance and social justice diversify the discourse on data justice. It highlights existing hurdles and structural hierarchies within a data-heavy developmental register emergent across multiple cities in the global South where data-driven governmental regimes interact with convoluted urban forms and realities….(More)”.

Index: Secondary Uses of Personal Data

Curated on Posted on by Michelle Winowatan

By Alexandra Shaw, Andrew Zahuranec, Andrew Young, Stefaan Verhulst

The Living Library Index–inspired by the Harper’s Index–provides important statistics and highlights global trends in governance innovation. This installment focuses on public perceptions regarding secondary uses of personal data (or the re-use of data initially collected for a different purpose). It provides a summary of societal perspectives toward personal data usage, sharing, and control. It is not meant to be comprehensive–rather, it intends to illustrate conflicting, and often confusing, attitudes toward the re-use of personal data. 

Please share any additional, illustrative statistics on data, or other issues at the nexus of technology and governance, with us at info@thelivinglib.org

Data ownership and control 

  • Percentage of Americans who say it is “very important” they control information collected about them: 74% – 2016
  • Americans who think that today’s privacy laws are not good enough at protecting people’s privacy online: 68% – 2016
  • Americans who say they have “a lot” of control over how companies collect and use their information: 9% – 2015
  • In a survey of 507 online shoppers, the number of respondents who indicated they don’t want brands tracking their location: 62% – 2015
  • In a survey of 507 online shoppers, the amount who “prefer offers that are targeted to where they are and what they are doing:” 60% – 2015 
  • Number of surveyed American consumers willing to provide data to corporations under the following conditions: 
    • “Data about my social concerns to better connect me with non-profit organizations that advance those causes:” 19% – 2018
    • “Data about my DNA to help me uncover any hereditary illnesses:” 21% – 2018
    • “Data about my interests and hobbies to receive relevant information and offers from online sellers:” 32% – 2018
    • “Data about my location to help me find the fastest route to my destination:” 40% – 2018
    • “My email address to receive exclusive offers from my favorite brands:”  56% – 2018  

Consumer Attitudes 

  • Academic study participants willing to donate personal data to research if it could lead to public good: 60% – 2014
  • Academic study participants willing to share personal data for research purposes in the interest of public good: 25% – 2014
  • Percentage who expect companies to “treat [them] like an individual, not as a member of some segment like ‘millennials’ or ‘suburban mothers:’” 74% – 2018 
    • Percentage who believe that brands should understand a “consumer’s individual situation (e.g. marital status, age, location, etc.)” when they’re being marketed to: 70% – 2018 Number who are “more annoyed” by companies now compared to 5 years ago: 40% – 2018Percentage worried their data is shared across companies without their permission: 88% – 2018Amount worried about a brand’s ability to track their behavior while on the brand’s website, app, or neither: 75% – 2018 
  • Consumers globally who expect brands to anticipate needs before they arise: 33%  – 2018 
  • Surveyed residents of the United Kingdom who identify as:
    • “Data pragmatists” willing to share personal data “under the right circumstances:” 58% – 2017
    • “Fundamentalists,” who would not share personal data for better services: 24% – 2017
    • Respondents who think data sharing is part of participating in the modern economy: 62% – 2018
    • Respondents who believe that data sharing benefits enterprises more than consumers: 75% – 2018
    • People who want more control over their data that enterprises collect: 84% – 2018
    • Percentage “unconcerned” about personal data protection: 18% – 2018
  • Percentage of Americans who think that government should do more to regulate large technology companies: 55% – 2018
  • Registered American voters who trust broadband companies with personal data “a great deal” or “a fair amount”: 43% – 2017
  • Americans who report experiencing a major data breach: 64% – 2017
  • Number of Americans who believe that their personal data is less secure than it was 5 years ago: 49% – 2019
  • Amount of surveyed American citizens who consider trust in a company an important factor for sharing data: 54% – 2018

Convenience

Microsoft’s 2015 Consumer Data Value Exchange Report attempts to understand consumer attitudes on the exchange of personal data across the global markets of Australia, Brazil, Canada, Colombia, Egypt, Germany, Kenya, Mexico, Nigeria, Spain, South Africa, United Kingdom and the United States. From their survey of 16,500 users, they find:

  • The most popular incentives for sharing data are: 
    • Cash rewards: 64% – 2015
    • Significant discounts: 49% – 2015
    • Streamlined processes: 29% – 2015
    • New ideas: 28% – 2015
  • Respondents who would prefer to see more ads to get new services: 34% – 2015
  • Respondents willing to share search terms for a service that enabled fewer steps to get things done: 70% – 2015 
  • Respondents willing to share activity data for such an improvement: 82% – 2015
  • Respondents willing to share their gender for “a service that inspires something new based on others like them:” 79% – 2015

A 2015 Pew Research Center survey presented Americans with several data-sharing scenarios related to convenience. Participants could respond: “acceptable,” “it depends,” or “not acceptable” to the following scenarios: 

  • Share health information to get access to personal health records and arrange appointments more easily:
    • Acceptable: 52% – 2015
    • It depends: 20% – 2015
    • Not acceptable: 26% – 2015
  • Share data for discounted auto insurance rates: 
    • Acceptable: 37% – 2015
    • It depends: 16% – 2015
    • Not acceptable: 45% – 2015
  • Share data for free social media services: 
    • Acceptable: 33% – 2015
    • It depends: 15% – 2015
    • Not acceptable: 51% – 2015
  • Share data on smart thermostats for cheaper energy bills: 
    • Acceptable: 33% – 2015
    • It depends: 15% – 2015
    • Not acceptable: 51% – 2015

Other Studies

  • Surveyed banking and insurance customers who would exchange personal data for:
    • Targeted auto insurance premiums: 64% – 2019
    • Better life insurance premiums for healthy lifestyle choices: 52% – 2019 
  • Surveyed banking and insurance customers willing to share data specifically related to income, location and lifestyle habits to: 
    • Secure faster loan approvals: 81.3% – 2019
    • Lower the chances of injury or loss: 79.7% – 2019 
    • Receive discounts on non-insurance products or services: 74.6% – 2019
    • Receive text alerts related to banking account activity: 59.8% – 2019 
    • Get saving advice based on spending patterns: 56.6% – 2019
  • In a survey of over 7,000 members of the public around the globe, respondents indicated:
    • They thought “smartphone and tablet apps used for navigation, chat, and news that can access your contacts, photos, and browsing history” is “creepy;” 16% – 2016
    • Emailing a friend about a trip to Paris and receiving advertisements for hotels, restaurants and excursions in Paris is “creepy:” 32% – 2016
    • A free fitness-tracking device that monitors your well-being and sends a monthly report to you and your employer is “creepy:” 45% – 2016
    • A telematics device that allows emergency services to track your vehicle is “creepy:” 78% – 2016
  • The number of British residents who do not want to work with virtual agents of any kind: 48% – 2017
  • Americans who disagree that “if companies give me a discount, it is a fair exchange for them to collect information about me without my knowing”: 91% – 2015

Data Brokers, Intermediaries, and Third Parties 

  • Americans who consider it acceptable for a grocery store to offer a free loyalty card in exchange for selling their shopping data to third parties: 47% – 2016
  • Number of people who know that “searches, site visits and purchases” are reviewed without consent:  55% – 2015
  • The number of people in 1991 who wanted companies to ask them for permission first before collecting their personal information and selling that data to intermediaries: 93% – 1991
    • Number of Americans who “would be very concerned if the company at which their data were stored sold it to another party:” 90% – 2008
    • Percentage of Americans who think it’s unacceptable for their grocery store to share their shopping data with third parties in exchange for a free loyalty card: 32% – 2016
  • Percentage of Americans who think that government needs to do more to regulate advertisers: 64% – 2016
    • Number of Americans who “want to have control over what marketers can learn about” them online: 84% – 2015
    • Percentage of Americans who think they have no power over marketers to figure out what they’re learning about them: 58% – 2015
  • Registered American voters who are “somewhat uncomfortable” or “very uncomfortable” with companies like Internet service providers or websites using personal data to recommend stories, articles, or videos:  56% – 2017
  • Registered American voters who are “somewhat uncomfortable” or “very uncomfortable” with companies like Internet service providers or websites selling their personal information to third parties for advertising purposes: 64% – 2017

Personal Health Data

The Robert Wood Johnson Foundation’s 2014 Health Data Exploration Project Report analyzes attitudes about personal health data (PHD). PHD is self-tracking data related to health that is traceable through wearable devices and sensors. The three major stakeholder groups involved in using PHD for public good are users, companies that track the users’ data, and researchers. 

  • Overall Respondents:
    • Percentage who believe anonymity is “very” or “extremely” important: 67% – 2014
    • Percentage who “probably would” or “definitely would” share their personal data with researchers: 78% – 2014
    • Percentage who believe that they own—or should own—all the data about them, even when it is indirectly collected: 54% – 2014
    • Percentage who think they share or ought to share ownership with the company: 30% – 2014
    • Percentage who think companies alone own or should own all the data about them: 4% – 2014
    • Percentage for whom data ownership “is not something I care about”: 13% – 2014
    • Percentage who indicated they wanted to own their data: 75% – 2014 
    • Percentage who would share data only if “privacy were assured:” 68% – 2014
    • People who would supply data regardless of privacy or compensation: 27% – 2014
      • Percentage of participants who mentioned privacy, anonymity, or confidentiality when asked under what conditions they would share their data:  63% – 2014
      • Percentage who would be “more” or “much more” likely to share data for compensation: 56% – 2014
      • Percentage who indicated compensation would make no difference: 38% – 2014
      • Amount opposed to commercial  or profit-making use of their data: 13% – 2014
    • Percentage of people who would only share personal health data with a guarantee of:
      • Privacy: 57% – 2014
      • Anonymization: 90% – 2014
  • Surveyed Researchers: 
    • Percentage who agree or strongly agree that self-tracking data would help provide more insights in their research: 89% – 2014
    • Percentage who say PHD could answer questions that other data sources could not: 95% – 2014
    • Percentage who have used public datasets: 57% – 2014
    • Percentage who have paid for data for research: 19% – 2014
    • Percentage who have used self-tracking data before for research purposes: 46% – 2014
    • Percentage who have worked with application, device, or social media companies: 23% – 2014
    • Percentage who “somewhat disagree” or “strongly disagree” there are barriers that cannot be overcome to using self-tracking data in their research: 82% – 2014 

SOURCES: 

“2019 Accenture Global Financial Services Consumer Study: Discover the Patterns in Personality”, Accenture, 2019. 

“Americans’ Views About Data Collection and Security”, Pew Research Center, 2015. 

“Data Donation: Sharing Personal Data for Public Good?”, ResearchGate, 2014.

“Data privacy: What the consumer really thinks,” Acxiom, 2018.

“Exclusive: Public wants Big Tech regulated”, Axios, 2018.

“Consumer data value exchange,” Microsoft, 2015.

“Crossing the Line: Staying on the right side of consumer privacy,” KPMG International Cooperative, 2016.

“How do you feel about the government sharing our personal data? – livechat”, The Guardian, 2017. 

“Personal data for public good: using health information in medical research”, The Academy of Medical Sciences, 2006. 

“Personal Data for the Public Good: New Opportunities to Enrich Understanding of Individual and Population Health”, Robert Wood Johnson Foundation, Health Data Exploration Project, Calit2, UC Irvine and UC San Diego, 2014. 

“Pew Internet and American Life Project: Cloud Computing Raises Privacy Concerns”, Pew Research Center, 2008. 

“Poll: Little Trust That Tech Giants Will Keep Personal Data Private”, Morning Consult & Politico, 2017. 

“Privacy and Information Sharing”, Pew Research Center, 2016. 

“Privacy, Data and the Consumer: What US Thinks About Sharing Data”, MarTech Advisor, 2018. 

“Public Opinion on Privacy”, Electronic Privacy Information Center, 2019. 

“Selligent Marketing Cloud Study Finds Consumer Expectations and Marketer Challenges are Rising in Tandem”, Selligent Marketing Cloud, 2018. 

“The Data-Sharing Disconnect: The Impact of Context, Consumer Trust, and Relevance in Retail Marketing,” Boxever, 2015. 

“Microsoft Research reveals understanding gap in the brand-consumer data exchange,” Microsoft Research, 2015.

“Survey: 58% will share personal data under the right circumstances”, Marketing Land: Third Door Media, 2019. 

“The state of privacy in post-Snowden America”, Pew Research Center, 2016. 

The Tradeoff Fallacy: How Marketers Are Misrepresenting American Consumers And Opening Them Up to Exploitation”, University of Pennsylvania, 2015.

‘Digital colonialism’: why some countries want to take control of their people’s data from Big Tech

Curated on Posted on by Stefaan Verhulst

Jacqueline Hicks at the Conversation: “There is a global standoff going on about who stores your data. At the close of June’s G20 summit in Japan, a number of developing countries refused to sign an international declaration on data flows – the so-called Osaka Track. Part of the reason why countries such as India, Indonesia and South Africa boycotted the declaration was because they had no opportunity to put their own interests about data into the document.

With 50 other signatories, the declaration still stands as a statement of future intent to negotiate further, but the boycott represents an ongoing struggle by some countries to assert their claim over the data generated by their own citizens.

Back in the dark ages of 2016, data was touted as the new oil. Although the metaphor was quickly debunked it’s still a helpful way to understand the global digital economy. Now, as international negotiations over data flows intensify, the oil comparison helps explain the economics of what’s called “data localisation” – the bid to keep citizens’ data within their own country.

Just as oil-producing nations pushed for oil refineries to add value to crude oil, so governments today want the world’s Big Tech companies to build data centres on their own soil. The cloud that powers much of the world’s tech industry is grounded in vast data centres located mainly around northern Europe and the US coasts. Yet, at the same time, US Big Tech companies are increasingly turning to markets in the global south for expansion as enormous numbers of young tech savvy populations come online….(More)”.

Digital Media and Wireless Communication in Developing Nations: Agriculture, Education, and the Economic Sector

Curated on Posted on by Stefaan Verhulst

Book by Megh R. Goyal and Emmanuel Eilu: “… explores how digital media and wireless communication, especially mobile phones and social media platforms, offer concrete opportunities for developing countries to transform different sectors of their economies. The volume focuses on the agricultural, economic, and education sectors. The chapter authors, mostly from Africa and India, provide a wealth of information on recent innovations, the opportunities they provide, challenges faced, and the direction of future research in digital media and wireless communication to leverage transformation in developing countries….(More)”.

The Art of Values-Based Innovation for Humanitarian Action

Curated on Posted on by Stefaan Verhulst

Chris Earney & Aarathi Krishnan at SSIR: “Contrary to popular belief, innovation isn’t new to the humanitarian sector. Organizations like the Red Cross and Red Crescent have a long history of innovating in communities around the world. Humanitarians have worked both on a global scale—for example, to innovate financing and develop the Humanitarian Code of Conduct—and on a local level—to reduce urban fire risks in informal settlements in Kenya, for instance, and improve waste management to reduce flood risks in Indonesia.

Even in its more-bureaucratic image more than 50 years ago, the United Nations commissioned a report to better understand the role that innovation, science, and technology could play in advancing human rights and development. Titled the “Sussex Manifesto,” the report outlined how to reshape and reorganize the role of innovation and technology so that it was more relevant, equitable, and accessible to the humanitarian and development sectors. Although those who commissioned the manifesto ultimately deemed it too ambitious for its era, the effort nevertheless reflects the UN’s longstanding interest in understanding how far-reaching ideas can elicit fundamental and needed progress. It challenged the humanitarian system to be explicit about its values and understand how those values could lead to radical actions for the betterment of humanity.

Since then, 27 UN organizations have formed teams dedicated to supporting innovation. Today, the aspiration to innovate extends to NGOs and donor communities, and has led to myriad approaches to brainstorming, design thinking, co-creation, and other activities developed to support novelty.

However, in the face of a more-globalized, -connected, and -complex world, we need to, more than ever, position innovation as a bold and courageous way of doing things. It’s common for people to demote innovation as a process that tinkers around the edges of organizations, but we need to think about innovation as a tool for changing the way systems work and our practices so that they better serve communities. This matters, because humanitarian needs are only going to grow, and the resources available to us likely won’t match that need. When the values that underpin our attitudes and behaviors as humanitarians drive innovation, we can better focus our efforts and create more impact with less—and we’re going to have to…(More)”.

Index: The Data Universe 2019

Curated on Posted on by Michelle Winowatan

By Michelle Winowatan, Andrew J. Zahuranec, Andrew Young, Stefaan Verhulst, Max Jun Kim

The Living Library Index – inspired by the Harper’s Index – provides important statistics and highlights global trends in governance innovation. This installment focuses on the data universe.

Please share any additional, illustrative statistics on data, or other issues at the nexus of technology and governance, with us at info@thelivinglib.org

Internet Traffic:

  • Percentage of the world’s population that uses the internet: 51.2% (3.9 billion people) – 2018
  • Number of search processed worldwide by Google every year: at least 2 trillion – 2016
  • Website traffic worldwide generated through mobile phones: 52.2% – 2018
  • The total number of mobile subscriptions in the first quarter of 2019: 7.9 billion (addition of 44 million in quarter) – 2019
  • Amount of mobile data traffic worldwide: nearly 30 billion GB – 2018
  • Data category with highest traffic worldwide: video (60%) – 2018
  • Global average of data traffic per smartphone per month: 5.6 GB – 2018
    • North America: 7 GB – 2018
    • Latin America: 3.1 GB – 2018
    • Western Europe: 6.7 GB – 2018
    • Central and Eastern Europe: 4.5 GB – 2018
    • North East Asia: 7.1 GB – 2018
    • Southeast Asia and Oceania: 3.6 GB – 2018
    • India, Nepal, and Bhutan: 9.8 GB – 2018
    • Middle East and Africa: 3.0 GB – 2018
  • Time between the creation of each new bitcoin block: 9.27 minutes – 2019

Streaming Services:

  • Total hours of video streamed by Netflix users every minute: 97,222 – 2017
  • Hours of YouTube watched per day: over 1 billion – 2018
  • Number of tracks uploaded to Spotify every day: Over 20,000 – 2019
  • Number of Spotify’s monthly active users: 232 million – 2019
  • Spotify’s total subscribers: 108 million – 2019
  • Spotify’s hours of content listened: 17 billion – 2019
  • Total number of songs on Spotify’s catalog: over 30 million – 2019
  • Apple Music’s total subscribers: 60 million – 2019
  • Total number of songs on Apple Music’s catalog: 45 million – 2019

Social Media:

Calls and Messaging:

Retail/Financial Transaction:

  • Number of packages shipped by Amazon in a year: 5 billion – 2017
  • Total value of payments processed by Venmo in a year: USD 62 billion – 2019
  • Based on an independent analysis of public transactions on Venmo in 2017:
  • Based on a non-representative survey of 2,436 US consumers between the ages of 21 and 72 on P2P platforms:
    • The average volume of transactions handled by Venmo: USD 64.2 billion – 2019
    • The average volume of transactions handled by Zelle: USD 122.0 billion – 2019
    • The average volume of transactions handled by PayPal: USD 141.8 billion – 2019 
    • Platform with the highest percent adoption among all consumers: PayPal (48%) – 2019 

Internet of Things:

Sources: