The document discusses the new data economy and fair data sharing. It notes that trust is a barrier preventing some Europeans from using digital services due to lack of control over personal data. A business survey found that companies support fair data principles but there is a gap between attitudes and commitment to sharing data. Developing common rules and guidelines could help address this gap and enable innovation. The presentation discusses a "rulebook" approach where ecosystem members agree to common terms to facilitate data sharing in a transparent, consensual manner.

1. New Data Economy
Rainmakers: Where's My Money?

2. New Data Economy
Rainmakers: Where's My Money?

3. Uncovering Fair Data Economy
Jaana Sinipuro, Project Director, The Finnish Innovation Fund Sitra
@jsinipuro

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. 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. What is Europe’s role in digital platform economy?

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. We are
writing a
Digital
Constitution

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. 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. #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. Let’s make fair data economy
a competitive advantage for Europe
FOR
INDIVIDUALS
FOR
COMPANIES
FOR
PUBLIC
SECTOR

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. 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. 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. 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. JOIN THE DATA
REVOLUTION
IHAN® ENABLER OF A
PARADIGM SHIFT

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. 6.6.2019
19
What is your company position in your
future industrial data economy?

20. Why data?
Licensing as a Business Model and Contracts
6.6.2019
20
Source: Wired 21.4.2015

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. Why data?
Augmented Intelligence of Things and their operating
systems
6.6.2019
22

23. 6.6.2019
23
Data sharing is required when
developing next generation systems of
systems!
(e.g. Smart Traffic)

24. Data as a Resource?

25. 6.6.2019
25
Company revenues (outputs) can be split to
external (inputs) and internal (transformation)
resources.
Inputs OutputsTransformation

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. 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. 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. 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. 6.6.2019
30
Operational efficiencies perspective
Internal External
Strategic opportunities perspective
Internal External
Transformation:
From EDI-Economy to API-economy

31. What is your data sharing
strategy?

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. 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. 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. 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. 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. 6.6.2019
37
What is your company position in your
future industrial data economy?

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. Tiina Härkönen, Leading Specialist, The Finnish Innovation Fund Sitra
How do companies see the data
economy? A sneak peek on
European business survey

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. 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. 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. Attitude
Sharing data with other organisations is a good thing
3,4/5

44. Attitude
It is good that using personal data needs consent
3,8/5

45. Attitude
One needs to strive for consumer trust
3,9/5

46. Attitude
The respect for individuals’ privacy must come first –
even at the cost of customer experience
3,9/5

47. Attitude
There needs to be clear ethical rules for using and gathering data
3,9/5

48. Attitude
User terms and conditions need to be customer-friendly
3,9/5

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. There Is a Gap!

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. 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. 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. How to take the first steps? – “Renewary” and
rulebook for new data economy
Jyrki Suokas, Senior Lead, The Finnish Innovation Fund Sitra

55. Next Steps
Data Ecosystem Rulebook : Solution to the ”Contract challenge”
IHAN Uudistamo – renewing business models

56. RULEBOOK

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. 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. 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. 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. 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. 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. 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. 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. UUDISTAMO
How to support SME’s in their efforts to join
the data economy

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. 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. 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. 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. 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. sitra.fi | seuraavaerä.fi
@sitrafund