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New Data Economy r´Rainmakers: Where's My Money?

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Sitra's event 6 June 2019

Tiina Härkönen/ Sitra
Jaana Sinipuro /Sitra
Timo Seppälä / Etla
Jyrki Suokas / Sitra

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New Data Economy r´Rainmakers: Where's My Money?

  1. 1. New Data Economy Rainmakers: Where's My Money?
  2. 2. New Data Economy Rainmakers: Where's My Money?
  3. 3. Uncovering Fair Data Economy Jaana Sinipuro, Project Director, The Finnish Innovation Fund Sitra @jsinipuro
  4. 4. Founded in1967 Investments by the Finnish State 1967: 16.8 M€ 1972: 16.8 M€ 1981: 16.8 M€ 1992: 16.8 M€ 84.1 M€ Market value million euros in 31 Dec. 2017 840 of endowment capital Sitra by the figures Annual budget million euros 30-40 in 31 Dec. 2017 159 employees Average return 7.7%in 2017 89 % higher education 11 % other education 66 % women 34 % men Working for the future over 50years
  5. 5. S I T R A ’ S V I S I O N O N S U S T A I N A B L E G R O W T H Our well-being is challenged. We need a fast and just transition to a growth model that combines ecological, social and economic sustainable development. The climate and biodiversity crisis forces us to decouple economic growth from the overconsumption of natural resources. Data economy is key to productivity and growth but it has to be based on human-centric model. Fragmentation of societies in a globalised world reinforces the need to move onto more inclusive growth. EU has an opportunity to be an enabler of this transformation and take the lead to sustainable growth in a global scale.
  6. 6. What is Europe’s role in digital platform economy?
  7. 7. How to Own the World? Owning the IDENTITY [”Integrity is a luxury for those who can afford it”] Owning our TIME and PLACES where we talk [Middlemens, sousveillance] Andreas Ekström LIVE from #GartnerSYM: Seven Ways to Own the World https://youtu.be/qbCPFVfr8lo Being the LINK between the PEOPLE [”FB is becoming a phone book of the world”]
  8. 8. We are writing a Digital Constitution
  9. 9. of Europeans think that It should be possible to identify services that use data in a fair way 66%42% of Europeans say that lack of trusts towards service providers is preventing them from using some digital services Survey: Europeans attitudes towards the use of personal data https://www.sitra.fi/en/publications/use-digital-services/ Europeans attitudes towards the use of personal data
  10. 10. Maintaining trust – Europe’s biggest opportunity Europe’s biggest opportunity, however, may be political and regulatory rather than technical… Source: The Economist, Big Data, small politics − Can the EU become another AI superpower?
  11. 11. #GDPRGeneral Data Protection Regulation and especially Article 20 #PSD2Payment Services Directive #EIDASEU regulation on electronic identification and trust services for electronic transactions Great timing!
  12. 12. Let’s make fair data economy a competitive advantage for Europe FOR INDIVIDUALS FOR COMPANIES FOR PUBLIC SECTOR
  13. 13. IHAN® Our project aims to build the framework for a fair and functioning post-GDPR data economy. The main objectives are to test and create a common concept for data sharing and to set up European-level rules and guidelines for the human-driven use of data. AS AN ENABLER OF PARADIGM SHIFT INDIVIDUAL DATA PERMIT
  14. 14. IHAN® as a project • We define, not just the principles and guidelines, but also the needed components for the fair data economy • We pilot new concepts based on personal data in collaboration with pioneering businesses across corporate, industry and national borders • We develop an easy way for individuals to identify reliable services that use their data in a fair way
  15. 15. Enabling innovation and new services Example FINANCE Insurance tailored to your life situation and lifestyle. SIGN IN SIGN IN SIGN IN Example TRANSPORT A service that optimises your travel time, route and carbon footprint. Example MEDICAL A child’s diabetes monitoring service enables parents to exchange care info with people involved in the child’s care at home, at school and at care facilities.
  16. 16. System Integrators How are we helping companies? New Data Economy Rainmakers Create awareness at companies interested in finding new ways to compete Introduce fair data and open ecosystems as ways to sustainably create new value Improve organisations’ understanding and readiness for IHAN® Target audiences SMEs and major b2c companies, SMEs and major b2b companies, industry associations, analysts Advisers, Management Consultants and other Stakeholders IHAN® REFERENCE ARCHITECTURE Blueprint, Sandbox and Example implementations, User stories… IHAN® BUSINESS LIBRARY Guidelines, Fair Data Reporting Models, Governance Model Framework, Rulebook, Business Model simulation… FOR CITIZENS Digital profile tests and recommendations, My Terms Concept. Fair Data Label… DELIVERABLES (EXIT)
  17. 17. JOIN THE DATA REVOLUTION IHAN® ENABLER OF A PARADIGM SHIFT
  18. 18. How to unlock the value of your company’s data: the first steps Industry and Data Research Project Timo Seppälä and Henri Huttunen 06.06.2019
  19. 19. 6.6.2019 19 What is your company position in your future industrial data economy?
  20. 20. Why data? Licensing as a Business Model and Contracts 6.6.2019 20 Source: Wired 21.4.2015
  21. 21. Why data: Licensing, Contracts andAugmented Intelligence of Things 6.6.2019 21 Source: https://security.stackexchange.com/questions/78807/how-does-googles-no-captcha-recaptcha-work
  22. 22. Why data? Augmented Intelligence of Things and their operating systems 6.6.2019 22
  23. 23. 6.6.2019 23 Data sharing is required when developing next generation systems of systems! (e.g. Smart Traffic)
  24. 24. Data as a Resource?
  25. 25. 6.6.2019 25 Company revenues (outputs) can be split to external (inputs) and internal (transformation) resources. Inputs OutputsTransformation
  26. 26. 6.6.2019 26 Data as a Resource? The vast majority of all data stays within the companies’ internal systems and it is coded with a company specific “language”. Inputs OutputsTransformation
  27. 27. 6.6.2019 27 Data as a Resource? Industrial Data has been considered as a resource for the last 40years, but “only” from Operational Efficiency Perspective! Reference (Distribution and copying without citation prohibited): Huttunen, Lähteenmäki, Mattila & Seppälä, (forthcoming 2019), The role of data in firm’s performance; Inputs OutputsTransformation
  28. 28. Transformation: From EDI-Economy to API-economy • The number of connected parties (partners) has grown • Volume of Data • From Kilobytes to Terabytes • Variety of Data • From operational data to markets data • Velocity of Data • From static data to dynamic data sources • Adoption costs and other frictions have greatly reduced 6.6.2019 28
  29. 29. 6.6.2019 29 Data as a Resource? Industrial Data has NOT typically been considered as a resource from (external) Strategic Opportunity Perspective! Inputs OutputsTransformation
  30. 30. 6.6.2019 30 Operational efficiencies perspective Internal External Strategic opportunities perspective Internal External Transformation: From EDI-Economy to API-economy
  31. 31. What is your data sharing strategy?
  32. 32. Typology of Data Platforms 6.6.2019 32 • Propriatory data (Company) – Company internal use only data repository. Access to data maintaned by the company • Inner circle data (Platform) – Shared data repositories. Access to data maintained collectively with boundary resources. • Distributed data (Industry) – Controlled by a third-party actor. Shared practices and technology to access and share information. • Open data (Open) – Distributed, accessible by publicly auditable rules. Programmable interfaces as a key boundary resource. Source: Rajala, Hakanen, Mattila, Seppälä & Westerlund, 2018
  33. 33. What is the value added of the data processing, hosting and related activities; web portals in Finland? 6.6.2019 33 • Industry revenue: 1.868.600.000 € • Average Industry Value Added: 64,5% • Value added: 1.205.250.000 € • Gross Domestic Product: 223.900.000.000 € • Share of GDP: 0,5 % Source: Statistics Finland, ETLA calculations
  34. 34. What is the value added of the data processing, hosting and related activities; web portals in Finland? 6.6.2019 34 30.8% (Annual growth during the last three years) 5.5 B€ (Estimated industry revenue in 2021) Source: ETLA calculations
  35. 35. How are company resources being divided to internal and external resources? Case Nokia 6.6.2019 35 • Nokia revenue: 26.162.118.000 $ (2017) • Nokia Global Value Added: 40,7% • Nokia Value added: 10.645.071.000 € • Nokia revenue – Nokia Value Added (Nokia Internal Resources) = External Resources • Value added of External Resourcing (all tiers included): 15.517.047.000 $ (59,3%) Source: Eurostat, ETLA calculations
  36. 36. Data Sharing? Opportunity? 6.6.2019 36 • Propriatory data (Company) – Company internal use only data repository. Access to data maintaned by the company • Inner circle data (Platform) – Shared data repositories. Access to data maintained collectively with boundary resources. • Distributed data (Industry) – Controlled by a third-party actor. Shared practices and technology to access and share information. • Open data (Open) – Distributed, accessible by publicly auditable rules. Programmable interfaces as a key boundary resource. Source: Rajala, Hakanen, Mattila, Seppälä & Westerlund, 2018
  37. 37. 6.6.2019 37 What is your company position in your future industrial data economy?
  38. 38. Moderator: Tiina Härkönen, Leading Specialist, The Finnish Innovation Fund Sitra Turo Pekari, Senior Advisor, Teosto Teemu Malinen, CEO, Sofokus Discussion: What’s stopping me from making money out of data?
  39. 39. Tiina Härkönen, Leading Specialist, The Finnish Innovation Fund Sitra How do companies see the data economy? A sneak peek on European business survey
  40. 40. A SURVEY REVEALS THAT PEOPLE’S LACK OF TRUST PRESENTS AN OBSTACLE TO THE GROWTH OF DIGITAL BUSINESS. THE PROGRESS ENABLED BY ARTIFICIAL INTELLIGENCE IS ALSO AT RISK IF ACCESS TO DATA IS COMPROMISED. Commissioned by Sitra, Kantar TNS Oy conducted the survey in November and December 2018 in Finland, the Netherlands, France and Germany. More than 8,000 respondents took part.
  41. 41. About the Business Survey - Objective is to understand – the level of comprehension, attitude and commitment to data economy and its business potential in European companies – whether an idea of a new data economy model based on “fairness” i.e. consumer consent, data sharing in ecosystems, as well as common rules and guidelines, resonates with business - Major corporations and SME companies in Finland, France, Germany and The Netherlands (n = 1667) - Launch of survey in full in September 2019 – Analysis, findings and recommendations – Business event coming up
  42. 42. Definition of fair data economy in the survey Different market actors exist in joint ecosystems to have access to diverse data through data sharing (and individuals consent). The parties in the ecosystem ensure usability and optimal utilisation of data, as well as create new applications and services based on them.
  43. 43. Attitude Sharing data with other organisations is a good thing 3,4/5
  44. 44. Attitude It is good that using personal data needs consent 3,8/5
  45. 45. Attitude One needs to strive for consumer trust 3,9/5
  46. 46. Attitude The respect for individuals’ privacy must come first – even at the cost of customer experience 3,9/5
  47. 47. Attitude There needs to be clear ethical rules for using and gathering data 3,9/5
  48. 48. Attitude User terms and conditions need to be customer-friendly 3,9/5
  49. 49. Commitment Sharing data with other organisations is a good thing It is good that using personal data needs consent One needs to strive for consumer trust The respect for individuals’ privacy must come first – even at the cost of customer experience There needs to be ethical rules for using and gathering data User terms and conditions need to be customer- friendly 3,21 3,56 3,69 3,61 3,74 3,61
  50. 50. There Is a Gap!
  51. 51. Proposition The Netherlands Finland Germany France Sharing data with other organisations is a good thing 3,49 / 3,32 -0,17 3,49 / 3,08 -0,41 3,29 / 3,11 -0,18 3,50 / 3,33 -0,17 It is good that using personal data needs consent 3,59 / 3,32 -0,27 3,86 / 3,71 -0,15 3,68 / 3,45 -0,23 4,02 / 3,80 -0,22 One needs to strive for consumer trust 3,62 / 3,43 -0,19 4,18 / 3,97 -0,21 3,74 / 3,59 -0,15 3,98 / 3,81 -0,17 The respect for individuals’ privacy must come first – even at the cost of customer experience 3,61 / 3,24 -0,37 3,92 / 3,75 -0,17 3,87 / 3,63 -0,24 4,20 / 3,84 -0,37 There needs to be ethical rules for using and gathering data 3,84 / 3,68 -0,16 4,10 / 3,86 -0,24 3,85 / 3,67 -0,19 3,95 / 3,76 -0,19 User terms and conditions need to be customer-friendly 3,82 / 3,61 -0,21 4,07 / 3,75 -0,32 3,84 / 3,66 -0,19 4,04 / 3,82 / -0,23
  52. 52. Main Outcomes - The principles of fair data economy is seen positively and gets backing – In all countries 3,8-3,9 out of 5,0 - “Sharing data with other organisations is a good thing” – possibly a bottle neck as only 15% of respondents strongly agree - The biggest gap is in respecting the consumers’ privacy at the cost of customer experience – may indicate that implementing fair data economy principles is not only beneficiary to the companies. However, the gap is moderate (0,29)
  53. 53. TRUST IS BUILT BY HAVING THE POWER TO INFLUENCE HOW YOUR DATA IS USED In Sitra’s citizen survey, as many as 42 per cent of respondents said a lack of trust in service providers prevents them from using digital services.
  54. 54. How to take the first steps? – “Renewary” and rulebook for new data economy Jyrki Suokas, Senior Lead, The Finnish Innovation Fund Sitra
  55. 55. Next Steps Data Ecosystem Rulebook : Solution to the ”Contract challenge” IHAN Uudistamo – renewing business models
  56. 56. RULEBOOK
  57. 57. Data Ecosystem Rulebook - Ecosystem Rulebook is the founding document that members of a data ecosystem sign to adhere to - Rulebook helps the ecosystem orchestrator to create the rulebook together with its ecosystem partners - Rulebook template contains a set of control questions that drive the results to fill the rulebook section by section: 1. Business – What is the vision and mission for the ecosystem. What are the business models for all participants in the ecosystem. Also terms on which new participants can be taken onboard 2. Technical – what technical means (data formats, consent management, logging etc.) are used 3. Legal – How different legislations enable or inhibit the activities in the ecosystem. 4. Data – different laws and regulations on different kind of data 5. Ethical – how data is sourced and how services utilize data. ow ecosystems thrive from sustainable and fair use of data. What kind of values ecosystems have 57 Multiple bilateral agreements Rulebook
  58. 58. Objective - To create a common rulebook model with a base structure for different data ecosystems – Making it easier and cost efficient to create an ecosystem rulebook – Making it possible for companies and organisations to join various data ecosystems more easily – Increasing know-how, trust and common market practises in the market – Ensuring fair, sustainable and ethically business within the data ecosystems - To build a tool that helps different data ecosystems to utilize a common rulebook structure and a process where by answering various modular control questions, to create make a initial version of the data ecosystem specific rulebook. The initial rulebook is then finalised by experts. 58
  59. 59. Current state - Rulebooks are hand written by expensive experts – lawyers, business developers and IT architects - who start from scratch each time a new rule book needs to be written - Very little or no reuse - Extra iterations are costly because these expensive experts are involved in both preparation and finalization phases PreparationFinalization 59
  60. 60. Near future state - Preparation phase is separated from Finalization phase by creating an initial list of the control questions. Business leaders go through the list and by answering the questions respective sections in the rulebook structure template are filled with answers. - This creates the initial rulebook which the experts then finalize - Iterations in the Finalization phase are reduced Preparation Finalization 60
  61. 61. End state - A tool which guides the business leader to go through the control questions. Tool automates the creation of the initial rulebook as much as possible - Control questions and rulebook structure are stored in updateable data repository. - Iterations in the Finalization phase are minimized Preparation Finalization 61
  62. 62. Rulebook interoperability - Rulebook interoperability validation process ensures that the resulting rulebooks conform to set quality and content standards - This also ensures interoperability between data ecosystems 62
  63. 63. Approach validation - Approach process is being tested against past, present and future rulebook work to ensure that the approach is valuable enough according to 80/20 rule Already completed rulebooks RETRO Rulebooks in progress REQUIREMENTS Future Rulebooks DIRECTION Past RB1 Past RB2 Current RB1 Current RB2 Future RB1 Future RB2 Future RB3 Future RB4 63
  64. 64. Rulebook Next Steps - Current working group will create initial version of control questions and rulebook structure by end of September - Additional members will be invited into the working group after this - Tool creation will commence after baseline has been stabilized 64
  65. 65. UUDISTAMO How to support SME’s in their efforts to join the data economy
  66. 66. Problem - Medium-sized (and smaller) Companies do not necessarily have at their disposal the needed competencies, resources and time to undertake a full-blown digital transformation initiative including – Rethinking their own and formulating new ecosystem Business model – Mapping the needed business and technology capabilities needed – Adjust their operational model to execute the business model – Enhance their technology stack to be able to play parts in data ecosystems – Create all needed legal documentation – Measure the impact of the change - Most importantly they do not always possess the energy, will, and know-how to execute the change - Additionally the business model of management consultant firms and system integrators does not allow them to serve other than large customers 66
  67. 67. Uudistamo – renewing the business model Objective Impact measurement To help SME’s to enter data economy by giving them tools and support to change their business so that the new data based products and services create sustainable business model for these companies. By equipping the companies with new competencies and tools that ease their way through the transformation journey To engage enough companies to have lasting and visible impact on national economy as a whole 1. creation of new data ecosystems that conform to the requirements of fair data economy 2. Number of new services making individuals’ life easier – also the revenue to the Service Provider companies creating the end-user services 3. How much revenue Data Sources make by making data accessible. 67
  68. 68. Solution: IHAN Accelerator program guiding companies through needed steps Businessmodel -Ownandecosystem Capabilities Operationalmodel Technology Execution 4 weeks 2 weeks 2 weeks 4 weeks 8 weeks EcosystemCompany 68
  69. 69. Timeline is challenging – some say it is utterly impossible. This forces us to find new ways to deliver the service Uudistamo 1 30 SMEs Spring 2020 Uudistamo 2 300 SMEs Fall 2020 Uudistamo 3 3000 SMEs Spring 2021 69
  70. 70. Potential solution: ”Digital Consulting Platform” • White labelled McPaaS (Management Consulting Platform as a Service)- tool that consulting firms can use to support the SME’s throughout the entire transformation process including Business and Operational modelling tools , Rulebook, and other needed collaboration and communication tools for all of the phases • Consultants can pick and choose tools provided by Tool providers on the platform • Customer experience is according to the brand of the consultant company 70
  71. 71. sitra.fi | seuraavaerä.fi @sitrafund

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