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1、Insight Report Data Collaboration for the Common Good Enabling Trust and Innovation Through Public-Private Partnerships April 2019 Produced in Collaboration with McKinsey compounding the problem, rules governing data collaborations are often absent or uncertain. Without appropriate guidance, some le

2、aders believe they have little recourse if their counterpart steps out of line. Although it may be tempting to say that data collaborations are too risky, the cost of inaction is immense. If companies and governments keep working in isolation, they will make slow progress in eradicating poverty and

3、other social ills. Thats not a situation society can afford to face. Instead, its time for leaders across the public and private sector to co-create a policy and data governance framework that strengthens trust and data practices in more pragmatic and sustainable ways, while encouraging a healthy do

4、se of innovation. By “going slow to go fast” we can collectively improve how organizations use data collaboration for both the common good and commercial gain and thats a win for us all. Chang-Gyu Hwang, Chairman and Chief Executive Officer, KT Corporation JoAnn Stonier, Chief Data Officer, Masterca

5、rd Nicolaus Henke, Global Leader of Digital and Analytics and Chairman of QuantumBlack, McKinsey and Company 7Data Collaboration for the Common Good: Enabling Trust and Innovation Through Public-Private Partnerships Executive summary As the digital technologies of the Fourth Industrial Revolution co

6、ntinue to drive change throughout all sectors of the global economy, a unique moment exists to create a more inclusive, innovative and resilient society. Central to this change is the use of data. It is abundantly available but if improperly used will be the source of dangerous and unwelcome results

7、. When data is shared, linked and combined across sectoral and institutional boundaries, a multiplier effect occurs. Connecting one bit with another unlocks new insights and understandings that often werent anticipated. Yet, due to commercial limits and liabilities, the full value of data is often u

8、nrealized. This is particularly true when it comes to using data for the common good. While public- private data collaborations represent an unprecedented opportunity to address some of the worlds most urgent and complex challenges, they have generally been small and limited in impact. An entangled

9、set of legal, technical, social, ethical and commercial risks have created an environment where the incentives for innovation have stalled. Additionally, the widening lack of trust among individuals and institutions creates even more uncertainty. After nearly a decade of anticipation on the promise

10、of public-private data collaboration with relatively few examples of success at global scale a pivotal moment has arrived to encourage progress and move forward. In response, the World Economic Forums Trustworthy Data Initiative has spearheaded an in-depth exploration of the contributing factors for

11、 catalysing progress in the domain of public-private data collaboration. Focusing on the multidimensional challenge of strengthening trust, a diverse community of commercial, government, academic and civil society leaders have participated in a series of global workshops and summits. The resulting o

12、utcome is a pragmatic framework for balancing two competing concerns: the imperative to innovate and the need to protect against emerging risks. The following report reflects the synthesis of an in-depth review of case studies, expert interviews and global workshops with prominent members of the pra

13、ctitioner community. They point to the need for a more holistic, iterative and outcome-based understanding of public- private data collaboration. The findings point to five areas for leaders to focus upon to strengthen trust. Leaders need to: 1) ensure that all relevant stakeholders are committed to

14、 shared outcomes; 2) operationalize the principles of responsible data governance; 3) deliver insights that are achievable, accurate, fair and explainable; 4) support both senior leader decision-makers and front-line users with the skills and resources to use data; and 5) establish sustainable econo

15、mics to ensure long-term impact. Figure 1: Critical enablers of public-private data collaboration Stakeholder alignment Responsible data governance Insight generation and validation Insight adoption Economic sustainability and scalability 8Data Collaboration for the Common Good: Enabling Trust and I

16、nnovation Through Public-Private Partnerships Section 1: Understanding the landscape As the technologies of the Fourth Industrial Revolution continue to evolve, the role of data has become indisputable. Described as the “lifeblood” of the 21st-century economy, rapid innovations across the data life

17、cycle have created an unprecedented moment to turn data into meaningful insight. Given the wide range of ways that data collaboratives can take shape, the scale of their intended outcomes and the types of data involved, this report will focus primarily on collaborative efforts at the global level. I

18、ts general focus examines the entangled set of technical, operational and governance challenges in accessing large-scale, commercially controlled personal data for the common good. From a stakeholder perspective, this report will primarily focus on the commercial entities that function as the data h

19、olders from the supply side (particularly from the mobile network operator, financial services, healthcare and social media sectors) and from the demand side the needs of large-scale international organizations and the United Nations System. In this light, it is important to note that the reports fo

20、cus on accessing large-scale private-sector data is a promising yet relatively new area of discussion. As it relates to the global development and humanitarian sectors, an equally daunting set of challenges lies in bringing together small amounts of non-standardized “public-to- public” data from doz

21、ens of NGOs and organizations to create a common picture of needs and responses.4 A review of more than 200 use-cases conducted by the UN has identified three clusters of public-private data collaboration activity:5 Humanitarian action and crisis response. These collaborations support activities rel

22、ated to the prediction, preparation, prevention, response and recovery from natural and human-made disasters and crises. For instance, the combined analysis of social-media trends, telecommunications data and proprietary records from humanitarian groups can facilitate the generation of more accurate

23、, robust and actionable insights to facilitate more effective relief efforts in natural disasters or crises. Global development. These collaborations involve researching long-term social, economic and political issues. They provide insights into citizens behaviour that allow stakeholders to establis

24、h more effective programmes and make better decisions. Also, cross- analysing seemingly disparate datasets from multiple sectors and industries can help uncover hidden insights and patterns critical to solving highly complex challenges. Official statistics. These collaborations create indicators and

25、 measurements that can serve as a proxy for more traditional data-collection methods to support national statistical offices. This, in turn, can help macro policy- makers monitor and evaluate the impact of their policies, allow for more accurate and timely indicators and support the work of local, n

26、ational and international statistical agencies with a focus on achieving the SDGs. Data holds great promise as a transformative resource for social good. JoAnn Stonier, Mastercard “With the adoption of sound and ethical data practices, all actors governments, the corporate sector, university researc

27、hers and non-governmental organizations (NGOs) can voluntarily work together to remove barriers and increase incentives to unlock the full power of data through sharing and analysis,” notes JoAnn Stonier, Chief Data Officer, Mastercard. Yet, with the promise of public-private data collaboration also

28、 come deeply held concerns on the significant risks of misuse. Data is a dual-use technology. It can be used both as a way to address global challenges and as a means to heighten existing inequities. Progress will demand balance. Ensuring that risks are fully managed while supporting collaborative i

29、nnovation requires new frameworks that are holistic, agile and pragmatic. Organizations are now linking and connecting diverse datasets at an accelerating pace to create value and this is one of the primary factors shaping todays global economy. From 2017 to 2019, the number of companies forming dat

30、a-related partnerships rose from 21% to 40%. A growing number of business competitors are also deciding to connect their data rising from 7% to 17%.1 Overall, McKinsey estimates that connecting data across institutional and geographic boundaries could create roughly $3 trillion annually in economic

31、value by 2020.2 As it relates to applying commercial data-sharing practices in the context of humanitarian and development challenges (in areas such as poverty, public health, environment and sustainable agriculture), there is also growing momentum. Collaboratively leveraging private-sector data has

32、 been widely recognized as an essential element for achieving the 2030 SDGs.3 “Data collaboratives represent a new form of multilateral collaboration where participants from different sectors including private companies, research institutions and government agencies can exchange data to help solve p

33、ublic problems,” says Stefaan Verhulst, co-founder and Chief Research and Development Officer of the Governance Laboratory. “They will be essential vehicles for harnessing the vast stores of privately held data towards the public good.” 9Data Collaboration for the Common Good: Enabling Trust and Inn

34、ovation Through Public-Private Partnerships Figure 2: The various types of data collaboratives Strengthening trust to achieve impact While growing evidence shows the value of public-private data collaboration, the challenges and risks remain daunting. As the Global Partnership on Sustainable Develop

35、ment Data notes, “access to data remains a great challenge due to real or perceived barriers”.6 Interconnected issues related to security, privacy, commercial risk, cross-border data flows, reputational concerns, due process and regulatory uncertainty all serve to create an environment that operates

36、 at a slow and deliberate pace. Underlying these concerns is a profound and widening lack of trust among individuals, institutions and governments. The 2019 Edelman Trust Barometer points to the plummet in trust in a variety of ways.7 The cost of damage caused by hackers, malware and data breaches i

37、s projected to double from $3 trillion to $6 trillion by 2021.8 The growing public outcry against the private sector on the misuse of personal data (as well as concerns on the limitations of industry self-regulation) have supported the call for more effective and meaningful privacy and data-protecti

38、on regulations globally.9 The rise of the “Data Justice” movement which seeks to ensure fairness in the way people are made visible, represented and treated in the production of digital data continues to gain momentum.10 Addressing the trust deficit has become a top priority at the most senior level

39、s. Prime Minister Shinz Abe of Japan has called for data governance to be a vital pillar of discussion at the 2019 G20 Summit. The UN Secretary-General has warned about the current “trust deficit disorder” and has highlighted through the Secretary-Generals High-Level Panel on Digital Cooperation the

40、 need for greater trust and the importance of shared vocabularies to address this challenge.11 Strengthening trust will require a number of coordinated actions related to the economics, operations and governance of public-private data collaborations. Without this cooperation, they will face difficul

41、ty balancing tensions between the need to protect data and the opportunities to innovate in its use. At the forefront of this governance challenge is the need for global frameworks that harmonize the requirement for local data-protection regulation with the need for data innovation at scale. Balanci

42、ng these competing concerns, while navigating an evolving global data-policy landscape, will require approaches that are agile, interoperable and iterative. Ensuring leaders have the appetite and patience to continually iterate and “fail fast” will also be vital for building sustainable and trustwor

43、thy data collaboratives. As the 2019 Edelman Trust Barometer demonstrates, people are shifting their trust to relationships within their control, most notably their employers.12 The 2019 global survey notes the increasing internal pressures for chief executive officers to actively engage and take ac

44、tions that both increase profits and improve socioeconomic conditions in the community in which they operate.13 Through its Data Cycle analysis, the GovLab has identified more than 150 examples of data collaboratives on a global basis. Listed below are the various forms they may take: Data cooperati

45、ves. Corporations and other important data holders group together to link and connect data resources. Prizes and challenges. Corporations make data available to qualified applicants who compete to develop new apps or discover innovative uses for the data. Research partnerships. Corporations share da

46、ta with universities and other academic organizations, giving researchers access to consumer datasets and other sources of data to analyse social trends. Intelligence products. Shared (often aggregated) corporate data is used to build a tool, dashboard, report, app or another technical device to sup

47、port a public or humanitarian objective. Application Programming Interfaces (APIs). APIs allow developers and others to access data for testing, product development and data analytics. Trusted intermediary. Corporations share data with a limited number of known partners. Companies generally share da

48、ta with these entities for data analysis and modelling, as well as other value chain activities. 10Data Collaboration for the Common Good: Enabling Trust and Innovation Through Public-Private Partnerships Figure 3: The changing data life cycle and its impact on policy Figure 4: Balancing the value a

49、nd risk dimensions of data collaboration Management Datasets are increasingly interconnected and shared to create new value “Anonymous data” is increasingly diffi cult to maintain as linked datasets can reveal unique attributes of individuals Processing Increasingly sophisticated machine- learning algorithms can process complex datasets more effectively Collection Increasing amount of granular and real- time data is collected by sensors and mobile devices Passive data collection by billions of sensors renders the concept of individual consen