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:

The hidden assumptions in public engagement: A case study of engaging on ethics in government data analysis

Curated on Posted on by Stefaan Verhulst

Paper by Emily S. Rempel, Julie Barnett and Hannah Durrant: “This study examines the hidden assumptions around running public-engagement exercises in government. We study an example of public engagement on the ethics of combining and analysing data in national government – often called data science ethics. We study hidden assumptions, drawing on hidden curriculum theories in education research, as it allows us to identify conscious and unconscious underlying processes related to conducting public engagement that may impact results. Through participation in the 2016 Public Dialogue for Data Science Ethics in the UK, four key themes were identified that exposed underlying public engagement norms. First, that organizers had constructed a strong imagined public as neither overly critical nor supportive, which they used to find and engage participants. Second, that official aims of the engagement, such as including publics in developing ethical data regulations, were overshadowed by underlying meta-objectives, such as counteracting public fears. Third, that advisory group members, organizers and publics understood the term ‘engagement’ in varying ways, from creating interest to public inclusion. And finally, that stakeholder interests, particularly government hopes for a positive report, influenced what was written in the final report. Reflection on these underlying mechanisms, such as the development of meta-objectives that seek to benefit government and technical stakeholders rather than publics, suggests that the practice of public engagement can, in fact, shut down opportunities for meaningful public dialogue….(More)”.

Sharing Private Data for Public Good

Curated on Posted on by Stefaan Verhulst

Stefaan G. Verhulst at Project Syndicate: “After Hurricane Katrina struck New Orleans in 2005, the direct-mail marketing company Valassis shared its database with emergency agencies and volunteers to help improve aid delivery. In Santiago, Chile, analysts from Universidad del Desarrollo, ISI Foundation, UNICEF, and the GovLab collaborated with Telefónica, the city’s largest mobile operator, to study gender-based mobility patterns in order to design a more equitable transportation policy. And as part of the Yale University Open Data Access project, health-care companies Johnson & Johnson, Medtronic, and SI-BONE give researchers access to previously walled-off data from 333 clinical trials, opening the door to possible new innovations in medicine.

These are just three examples of “data collaboratives,” an emerging form of partnership in which participants exchange data for the public good. Such tie-ups typically involve public bodies using data from corporations and other private-sector entities to benefit society. But data collaboratives can help companies, too – pharmaceutical firms share data on biomarkers to accelerate their own drug-research efforts, for example. Data-sharing initiatives also have huge potential to improve artificial intelligence (AI). But they must be designed responsibly and take data-privacy concerns into account.

Understanding the societal and business case for data collaboratives, as well as the forms they can take, is critical to gaining a deeper appreciation the potential and limitations of such ventures. The GovLab has identified over 150 data collaboratives spanning continents and sectors; they include companies such as Air France, Zillow, and Facebook. Our research suggests that such partnerships can create value in three main ways….(More)”.

Companies Collect a Lot of Data, But How Much Do They Actually Use?

Curated on Posted on by Stefaan Verhulst

Article by Priceonomics Data Studio: “For all the talk of how data is the new oil and the most valuable resource of any enterprise, there is a deep dark secret companies are reluctant to share — most of the data collected by businesses simply goes unused.

This unknown and unused data, known as dark data comprises more than half the data collected by companies. Given that some estimates indicate that 7.5 septillion (7,700,000,000,000,000,000,000) gigabytes of data are generated every single day, not using  most of it is a considerable issue.

In this article, we’ll look at this dark data. Just how much of it is created by companies, what are the reasons this data isn’t being analyzed, and what are the costs and implications of companies not using the majority of the data they collect.  

Before diving into the analysis, it’s worth spending a moment clarifying what we mean by the term “dark data.” Gartner defines dark data as:

“The information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships and direct monetizing). 

To learn more about this phenomenon, Splunk commissioned a global survey of 1,300+ business leaders to better understand how much data they collect, and how much is dark. Respondents were from IT and business roles, and were located in Australia, China, France, Germany, Japan, the United States, and the United Kingdom. across various industries. For the report, Splunk defines dark data as: “all the unknown and untapped data across an organization, generated by systems, devices and interactions.”

While the costs of storing data has decreased overtime, the cost of saving septillions of gigabytes of wasted data is still significant. What’s more, during this time the strategic importance of data has increased as companies have found more and more uses for it. Given the cost of storage and the value of data, why does so much of it go unused?

The following chart shows the reasons why dark data isn’t currently being harnessed:

By a large margin, the number one reason given for not using dark data is that companies lack a tool to capture or analyze the data. Companies accumulate data from server logs, GPS networks, security tools, call records, web traffic and more. Companies track everything from digital transactions to the temperature of their server rooms to the contents of retail shelves. Most of this data lies in separate systems, is unstructured, and cannot be connected or analyzed.

Second, the data captured just isn’t good enough. You might have important customer information about a transaction, but it’s missing location or other important metadata because that information sits somewhere else or was never captured in useable format.

Additionally, dark data exists because there is simply too much data out there and a lot of is unstructured. The larger the dataset (or the less structured it is), the more sophisticated the tool required for analysis. Additionally, these kinds of datasets often time require analysis by individuals with significant data science expertise who are often is short supply. 

The implications of the prevalence are vast. As a result of the data deluge, companies often don’t know where all the sensitive data is stored and can’t be confident they are complying with consumer data protection measures like GDPR. …(More)”.

How does Finland use health and social data for the public benefit?

Curated on Posted on by Stefaan Verhulst

Karolina Mackiewicz at ICT & Health: “…Better innovation opportunities, quicker access to comprehensive ready-combined data, smoother permit procedures needed for research – those are some of the benefits for society, academia or business announced by the Ministry of Social Affairs and Health of Finland when the Act on the Secondary Use of Health and Social Data was introduced.

It came into force on 1st of May 2019. According to the Finnish Innovation Fund SITRA, which was involved in the development of the legislation and carried out the pilot projects, it’s a ‘groundbreaking’ piece of legislation. It’ not only effectively introduces a one-stop-shop for data but it’s also one of the first, if not the first, implementations of the GDPR (the EU’s General Data Protection Regulation) for the secondary use of data in Europe. 

The aim of the Act is “to facilitate the effective and safe processing and access to the personal social and health data for steering, supervision, research, statistics and development in the health and social sector”. A second objective is to guarantee an individual’s legitimate expectations as well as their rights and freedoms when processing personal data. In other words, the Ministry of Health promises that the Act will help eliminate the administrative burden in access to the data by the researchers and innovative businesses while respecting the privacy of individuals and providing conditions for the ethically sustainable way of using data….(More)”.

Bringing machine learning to the masses

Curated on Posted on by Stefaan Verhulst

Matthew Hutson at Science: “Artificial intelligence (AI) used to be the specialized domain of data scientists and computer programmers. But companies such as Wolfram Research, which makes Mathematica, are trying to democratize the field, so scientists without AI skills can harness the technology for recognizing patterns in big data. In some cases, they don’t need to code at all. Insights are just a drag-and-drop away. One of the latest systems is software called Ludwig, first made open-source by Uber in February and updated last week. Uber used Ludwig for projects such as predicting food delivery times before releasing it publicly. At least a dozen startups are using it, plus big companies such as Apple, IBM, and Nvidia. And scientists: Tobias Boothe, a biologist at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, uses it to visually distinguish thousands of species of flatworms, a difficult task even for experts. To train Ludwig, he just uploads images and labels….(More)”.

Trust in Contemporary Society

Curated on Posted on by Stefaan Verhulst

Book edited by Masamichi Sasaki: “… deals with conceptual, theoretical and social interaction analyses, historical data on societies, national surveys or cross-national comparative studies, and methodological issues related to trust. The authors are from a variety of disciplines: psychology, sociology, political science, organizational studies, history, and philosophy, and from Britain, the United States, the Czech Republic, the Netherlands, Australia, Germany, and Japan. They bring their vast knowledge from different historical and cultural backgrounds to illuminate contemporary issues of trust and distrust. The socio-cultural perspective of trust is important and increasingly acknowledged as central to trust research. Accordingly, future directions for comparative trust research are also discussed….(More)”.

How we can place a value on health care data

Curated on Posted on by Stefaan Verhulst

Report by E&Y: “Unlocking the power of health care data to fuel innovation in medical research and improve patient care is at the heart of today’s health care revolution. When curated or consolidated into a single longitudinal dataset, patient-level records will trace a complete story of a patient’s demographics, health, wellness, diagnosis, treatments, medical procedures and outcomes. Health care providers need to recognize patient data for what it is: a valuable intangible asset desired by multiple stakeholders, a treasure trove of information.

Among the universe of providers holding significant data assets, the United Kingdom’s National Health Service (NHS) is the single largest integrated health care provider in the world. Its patient records cover the entire UK population from birth to death.

We estimate that the 55 million patient records held by the NHS today may have an indicative market value of several billion pounds to a commercial organization. We estimate also that the value of the curated NHS dataset could be as much as £5bn per annum and deliver around £4.6bn of benefit to patients per annum, in potential operational savings for the NHS, enhanced patient outcomes and generation of wider economic benefits to the UK….(More)”.

Applying design science in public policy and administration research

Curated on Posted on by Stefaan Verhulst

Paper by Sjoerd Romme and Albert Meijer: “There is increasing debate about the role that public policy research can play in identifying solutions to complex policy challenges. Most studies focus on describing and explaining how governance systems operate. However, some scholars argue that because current institutions are often not up to the task, researchers need to rethink this ‘bystander’ approach and engage in experimentation and interventions that can help to change and improve governance systems.

This paper contributes to this discourse by developing a design science framework that integrates retrospective research (scientific validation) and prospective research (creative design). It illustrates the merits and challenges of doing this through two case studies in the Netherlands and concludes that a design science framework provides a way of integrating traditional validation-oriented research with intervention-oriented design approaches. We argue that working at the interface between them will create new opportunities for these complementary modes of public policy research to achieve impact….(More)”

Review into bias in algorithmic decision-making

Curated on Posted on by Stefaan Verhulst

Interim Report by the Centre for Data Ethics and Innovation (UK): The use of algorithms has the potential to improve the quality of decision- making by increasing the speed and accuracy with which decisions are made. If designed well, they can reduce human bias in decision-making processes. However, as the volume and variety of data used to inform decisions increases, and the algorithms used to interpret the data become more complex, concerns are growing that without proper oversight, algorithms risk entrenching and potentially worsening bias.

The way in which decisions are made, the potential biases which they are subject to and the impact these decisions have on individuals are highly context dependent. Our Review focuses on exploring bias in four key sectors: policing, financial services, recruitment and local government. These have been selected because they all involve significant decisions being made about individuals, there is evidence of the growing uptake of machine learning algorithms in the sectors and there is evidence of historic bias in decision-making within these sectors. This Review seeks to answer three sets of questions:

  1. Data: Do organisations and regulators have access to the data they require to adequately identify and mitigate bias?
  2. Tools and techniques: What statistical and technical solutions are available now or will be required in future to identify and mitigate bias and which represent best practice?
  3. Governance: Who should be responsible for governing, auditing and assuring these algorithmic decision-making systems?

Our work to date has led to some emerging insights that respond to these three sets of questions and will guide our subsequent work….(More)”.