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Future of value of data singapore.compressed

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This is an updated view on the future value of data. After events in Bangalore and Madrid we have added extra perspectives and these are all now being taken on to forthcoming workshops across Asia, Africa and South America in April and May.

Further events across Europe and North America in June and July will then complete this major global project

Veröffentlicht in: Daten & Analysen
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Future of value of data singapore.compressed

  1. 1. The Future Value of Data An Emerging View to be Challenged Singapore | April 2018 The world’s leading open foresight program
  2. 2. Context This is an point of view on the topic of the future value of data. It is a perspective to be shared, challenged, built upon and enhanced via a series global discussions that are taking place throughout the first half of 2018.
  3. 3. The Value of Data The economic incentive to generate and collect data from multiple sources is leading to a data “land grab” by many organisations.
  4. 4. Data is the New… Data can fulfil different roles in the economy, in society and for individuals. Simplistic views help us understand some roles, but also mislead and blind us.
  5. 5. Data is the New Oil? Vast hordes of data can make its owners very wealthy and powerful but unlike oil it is not a finite, exhaustible resource, nor are the costs of extraction high.
  6. 6. Data is the New Currency? Data can certainly serve as a medium for exchange and can also be used as a store of value, but describing it as currency just tells us it sometime has value.
  7. 7. Data is like Water? Data is like water - abundant and essential. It enables many other things of greater economic value to grow and develop. But it itself has little or no value.
  8. 8. Data Politics Data politics enters the mainstream as more people come to understand impact of its the collection and use of their personal data on their own lives.
  9. 9. Digital Taxation Governments increasingly seek to tax digitally-driven sectors and introduce a number of approaches to link this to locations of data generation and use.
  10. 10. Polarized Data Debate The debate becomes extreme. Protagonists adopt ‘all or nothing’ positions on issues such as privacy, encryption, security and economic freedom.
  11. 11. Open Data In many contexts, data is increasingly openly shared for free. The positive social benefit is seen to outweigh any economic loss.
  12. 12. Privatization of Data In sectors such as healthcare the privatization of public knowledge test the view that most information should be a ‘public commons’ for all.
  13. 13. Shared Language about Data A shared language around the definition and use of data emerges but it is not clear which voices will be seen as credible and authoritative.
  14. 14. Clear Data Value Organisations have to be clearer about why they value specific types of data and on what terms, or they risk losing public trust and their licence to operate
  15. 15. Talking at Cross-purposes Different stakeholders have very different perspectives on (and understandings of) what data is. This makes the quest for common ground increasingly difficult.
  16. 16. Data Liability Storing some kinds of data could come to be seen as a liability as it erodes user trust, and the costs of securing it outweighs the costs associated with losing it.
  17. 17. Increasing Public Confidence The public becomes more informed about the issues that really matter. There are new approaches around data education, transparency and choice.
  18. 18. Joined Up Regulation Policymakers and regulators undertake a more joined up approach to data regulation stimulating growth on one hand, and minimising risk on the other.
  19. 19. Sustainable Customer Relationships Organisations build long-term trust-based relationships with the people who use their services and so create a broader ecosystem based on social value.
  20. 20. Data Marketplaces Ecosystems for trading data are emerging and soon both personal and machine data are openly brought and sold in new data marketplaces.
  21. 21. Ethical Machines As we approach technology singularity, autonomous robots and smarter algorithms make ethical judgments that impact life or death.
  22. 22. Trust in Data Use Trust increasingly drives success. To gain buy-in from governments and consumers, trust in data usage becomes a core source of differentiation.
  23. 23. Personally Curated Data ‘Personally curated’ sources of data have higher value because they represent the wishes of individuals, rather than the presumed wishes from derived data.
  24. 24. Digital Shadows It is difficult to differentiate between the digital truth and the real truth. There is growing awareness of the importance of managing our digital shadow.
  25. 25. Rising Cyber Security Threats In some areas, greater interconnectivity and the IoT create new opportunities for the unscrupulous who seek to exploit weakness and destroy systems.
  26. 26. The Rise of Machines AI presents both a threat and an opportunity: Greater AI and automation free up time, but also threaten jobs - both low skilled and administrative roles.
  27. 27. Sharing Secrets In exchange for better service or an improved quality of life, we increasingly recognise exactly what personal information we are prepared to share.
  28. 28. Linkability of Open Data No data will be anonymous: Current practice wrongly assumes that technology cannot relink it to its source. So, we see different levels of re-identification.
  29. 29. Global vs. Local Data does not respect national boundaries. Nation states try to set rules. Growing tensions drive design for global standards but with localised use.
  30. 30. Democracy and Government Citizen data is increasingly used and shared by governments as an instrument of social change. The limitations around its use are challenged.
  31. 31. Living in Glass Houses We will allow our personal information to be widely accessible in return for the understanding that this enables an easier, more ‘streamlined’ life as a result.
  32. 32. Informed Consent Informed consent around data use is increasingly impractical and unworkable. Alternative models addressing transparency and data rights are developed
  33. 33. India Setting Global Standards India has an innovative data design solutions for large populations. Many are applied to higher income economies seeking efficiency benefits.
  34. 34. The Privacy Illusion There is a rising belief in the right to data privacy and security. But security is impossible without increased monitoring - and so true privacy is illusive.
  35. 35. Machine Learning Driving Accuracy As more people use apps as “AI” advisers, more data is collected and machine learning improves significantly. Devices therefore deliver more accurate advice.
  36. 36. Data Ownership Traditional legal models of ownership to digital data cause debate. The focus shifts from ownership to the question of who is benefitting from what data
  37. 37. Individual Custodians People make more informed personal decisions as they become custodians of their own health and financial data. They control who can access their data.
  38. 38. Block-chain for Trust Distrust drives the adoption of block-chain which offers a universal set of tools for data integrity, standardized auditing and formalised contracts.
  39. 39. Conservative Regulators Legislators desire certainty and are concerned about the consequences of change. They therefore slow adoption of new technologies and approaches.
  40. 40. A Public Good The broader use of data for public good drives system reliability, interoperability and consensus around when individual data can be used.
  41. 41. Too Much Information As more data is available, the fear of data overload exceeds the individuals’ capacity to see things in perspective leading platforms to filter what is shared.
  42. 42. Decentralized Secure Data We decentralize more of our data in an ambition to make it more secure. However as technology evolves, distributed data is more easily integrated.
  43. 43. Data Sovereignty Sensitivity over ownership of personal data constrains sharing across national borders. In particular, resistance to a US-based concentration of data builds.
  44. 44. Dataism More place their faith in the power of data to drive efficiencies and solve problems. This blinds many to the flaws in data-sets and applications.
  45. 45. Data Imperialism Dominant services, built by western engineers, reflecting western values are increasingly seen as imperialist interlopers, irrelevant in different regions.
  46. 46. Artificial Empathy The race for machines that emulate human emotions leads to unintended consequences as the unpredictability of driving behaviours takes hold.
  47. 47. Fake Data Poorly collected, deliberately contaminated or fabricated data drive weak decision-making, inaccurate and biased AI, bad governance or societal unrest.
  48. 48. Data from the Child’s Point of View Rather than designing for adults (whose psychology was formed pre-big data), we increasingly shape our systems and services for the users of the future.
  49. 49. A Commons Approach Europe is well-positioned to lead a very different kind of data revolution – one where companies pay for access to our data – that we mostly own in common
  50. 50. Future Agenda 84 Brook Street London W1K 5EH +44 203 0088 141 futureagenda.org The world’s leading open foresight program What do you think? Join In | Add your views into the mix www.futureagenda.org @futureagenda