10:00

Data Spaces
Symposium

Engaging SMEs andStart-ups in Data SpacesÓscar Lázaro, Arne Berre, Daniel Sáez-Domingo, Till Riedel,
Denia Kanellopoulou, Jakob Logar & Jeanette Nilsson

Engaging SMEs andStart-ups in Data Spaces
12/03/2024 10:00 – 11:30 (CET)Óscar Lázaro
Innovalia

Eventually, everything is
data:• to plan & monitor
goods and processes,
and• as a tradeable
product in itself, fordata-driven businessmodelsData Space“A DISTRIBUTED SYSTEM DEFINED BY A
GOVERNANCE FRAMEWORK THATENABLES SECURE AND TRUSTWORTHYDATA TRANSACTIONS BETWEENPARTICIPANTS WHILE SUPPORTING TRUSTAND DATA SOVEREIGNTY. A DATA SPACEIS IMPLEMENTED BY ONE OR MORE
INFRASTRUCTURES AND ENABLES ONE
OR MORE USE CASES”.

Challenges for SMEs & Start-Ups in Data
Spaces
LACK OF TRUST
57 %of organizations
reveal valuable data

59 %of organizations haveloose control over data55 %of processes & systems
are inconsistent32 %of organizations use
common tools & platforms

LACK OF COST-EFFICIENCY
55 %of organizations find Highcost in 3rd party data
licensing49 %of Contracting terms of (datalicenses) are very diverse

54 %of organizations have Low
confidence in data quality

50 %of organizations find Hard tocombine data

LACK OFINTEROPERABILITY
70-80%

Time & Efforts in organizations areinvested in managing
of organizations find Hard
to combine data
• Data acquisitions• Integration• Quality Management

Challenges for SMEs & Start-Ups in Data
Spaces
LACK OF CONNECTION

Most organizations don’t
share their data with their
suppliers to drive efficiency

LACK OF TOOLS

Most organizations don’t
have any tools to respondto the data and continuity
challenge

LACK OF OPERABILITY

Most organizationsof organizations find Hardstrugglewithdata
to combine
dataexchanges and keeping
safe, reliable functioning
operable processes andsystems

Data - Data Spaces vs Time to Value

Investments/
Costs
Value Driven
Fast/Short RoI

Data as a
Product
SMEs

Data - Data Spaces vs Time to Value

Tools, Ecosystems & Value

Tools, Ecosystems & ValueBusiness
Maturity

Ecosystem
Maturity

Tools, Ecosystems & Value

Data in Manufacturing Thematic
Working Grouppage

Tools, Ecosystems & Value

Thematic Working GroupThe Thematic Working Group (TWG) on Data in Manufacturingprovides an active community for EDIHs to agree on• common knowledge and approaches• areas of collaboration and effective mechanisms for cooperation• support to drive manufacturing SMEs to use data and data-related
technologies (AI, digital twin, industrial metaverse, …)

Find your EDIH

https://european-digital-innovation-hubs.ec.europa.eu/homeThank you!

Engaging SMEs andStart-ups in Data Spaces
12/03/2024 10:00 – 11:30 (CET)

Arne Berre
Sintef

The SINTEF GEMINI iSpacewith existing partners

Has merged with additional
partners in Nemonoor AI EDIHSINTEF
Nemonoor
Nemonoor is the national Norwegian
AI and Big Data focused European
Digital Innovation Hub (AI EDIH) - withan initial domain focus on AI for
Energy, Smart Cities and Communities,
Manufacturing and Health.

Your LOGO here

Arne.J.Berre@sintef.no

Main challenges of SMEs and Start-ups in joining Data Spaces
Experiences from the Norwegian SINTEF Nemonoor AI EDIH and BDVA iSpaceGold label

Understanding the value proposition of EDIHs in supporting SMEs and start-ups in data and AI innovation.
Exploring the main challenges faced by SMEs and start-ups in joining Data Spaces
and identifying strategies to overcome them.
Examining the practical implications and benefits of Data Space Labs for SMEs,
and discussing how they can facilitate experimentation, testing, and innovation within Data Spaces.

Main challenges of SMEs and Start-ups in joining Data Spaces
Experiences from the Norwegian SINTEF Nemonoor AI EDIH and BDVA iSpaceGold labelnderstanding the value proposition of EDIHs in supporting SMEs and start-ups in data and AI innovation.

EDIHs provide a journey for SMEs and start up to understand their AI/Datav Digital Maturity – and help toselect appropriate services for AI Test and Experimentation and further development/funding opportunities.
Data Spaces are valuable as sources of data that can be included in the AI training and operational strategies
for the SME/start up value propositions and business models.

The Nemonoor AI EDIH Customer Journey: Stages in the digital transformation journeyNorwegian Nemonoor AI EDIH - Service organisation

DMA tool +Analysismeeting

TEFs
Norwegian national
Public data spacehttps://data.norge.no/

Emerging European
Common Data Spaces ?
Data Space Labs ?

Norwegian National Public Data Sets - https://data.norge.no/

Main challenges of SMEs and Start-ups in joining Data Spaces
Experiences from the Norwegian SINTEF Nemonoor AI EDIH and BDVA iSpaceGold label

xploring the main challenges faced by SMEs and start-ups in joining Data Spaces

nd identifying strategies to overcome them.

Initially, the Data Spaces have not been available – and the early approaches has mostly contained
historical data set examples - which have not met the needs from the SME/start-up value proposition.
The current development of Data Spaces with broader, up to date data sets, might become an
Important factor for new innovative value propositions.

Main challenges of SMEs and Start-ups in joining Data Spaces
Experiences from the Norwegian SINTEF Nemonoor AI EDIH and BDVA iSpaceGold label

xamining the practical implications and benefits of Data Space Labs for SMEs,

and discussing how they can facilitate experimentation, testing, and innovation within Data Spaces.

The Data Spaces and Data Space Labs can become important assets for the Test and Experimentation services
from European EDIHs and DIHs / iSpaces – and further also become a foundation through aavailable data for new
AI and Big Data Innovations from European SMEs and Start-ups.

Thank you!

Engaging SMEs andStart-ups in Data Spaces
12/03/2024 10:00 – 11:30 (CET)

Daniel Sáez-Domingo
ITIENGAGING SMES AND
START-UPS IN DATASPACESITI EXPERIENCEDaniel SáezDomingo(dsaez@iti.es)

Strategic Intelligence and Technology Transfer Director in ITICoordinator of ITI Innovation Space (i-Space Platinum BDVA)
Member of Board of Directors of BDVA and GAIA-X

ACCELERATING THE ADOPTION OF
INNOVATIVE DATA & AI
TECHNOLOGIES BY THE ECONOMY
The largest Technology
Centre in Europe
AND SOCIETY
focused on Data & AI
Key player in eDIH, AI Network of
excellence & Data Spaces
6+M€ investment yearly in Data & AI
technology stack
250+ companies formally linked
500+ companies in our ecosystem

Strategic Positioning in the Data
Ecosystem
Board of Directors
MemberBoard of Directors andSpanish president

Coordinator
Board of Directors

Coordinator

Coordinator

Data Spaces

Technology

Regulations & Standards

TRUSTEDECOSYSTEM

GovernanceBusinessPoor Quality of Data

Lack of computing power

Lack of knowledge in data ethics
Fair data value chains still need to be invented

Data owners (really big data holders) need
incentives to share

New AI and Data regulations bring challenges
for innovators

Lack of Skills

ITI proposal to Data Spaces

Technology

Regulations & Standards

TRUSTEDECOSYSTEM

GovernanceBusinessKnowledge foron-boarding in
the digitizationand extraction ofvalue from data

Model, tools andknowledge todeploy a data
exploitation
space

Own
infrastructure
and tools to hostexperiments andprofessional
data exploitation

Enriched node providing:

Data Quality



Data GovernanceData Ingestión



Data StorageData SharingData ProcessingAccess control andsecurityA Data Space in itself

Able to connect to othernodes and enlarge the Data
Space opening the door to the
Universe of Data and AI

FOR EXPERIMENTATION

Wisephere deployment in ITI
for experimentation andbusiness model definition
before going to a Data Space

Experimentation Space• Access to datasets•Access to infrastructures and tools•Support to experiments•Feasibility analysis•Proof of concept•Prototyping•Data and Services Marketplace•Selection and testing of technologies

Data management consultancy• Advice on data sharing, governance, sovereignty, ...
•Advice on legal and ethical aspects•Advice on data transformation processes (curation,
anonymization, ...)
• Enablement in Big Data Analytics

Training• Technique• Infrastructure deployment• Data governance• BusinessGUIDE FOR SMES IN THE PROCESS TO
DATA SPACES

From ITI we willassign you an expert
who will prepare an
analysis of yourcompany's process,your data and its
potential value.

Free access to the ITI
Data Innovation
Space, so you candeposit/share datasafely and reliably.

We will help you
improve thequality of yourdata andanonymize/prepa
re it.

We will help
you setchallenges toobtain value
from your data

We will include
you in the Data
Space so thatyou can operatewith theecosystem

We will help
you findbusinessmodels basedon data.

We will help you withthe legal aspects related
to data protection, andits sharing in the case of
exploitation by third
parties.

GUIDE FOR SERVICE PROVIDERS IN THE
PROCESS TO DATA SPACES

From ITI we will help you understandthe characteristics of a data space andits architecture.

Free access to the ITI Data Innovation
Space, so you can experiencesolutions based on data and AI.

Access to the Data Space so that you canprovide the services

Non Data
No quality
DrivenNo sharing
SMEs

Quality

ITI Exploit
iSharingSpace

Training

Data
Space

Business

FEEDING DATA SPACES WITH SMES READY TO MAKE BUSINESSFOR DATA SPACES

Single Organization

Multiple Organizations

Non Data
No quality
DrivenNo sharing
SMEs

Quality

ITI Exploit
iSharingSpace

Training

Data
Space

Business

ACCELERATING THE BUSINESS IN DATA SPACES

Engaging SMEs andStart-ups in Data Spaces
12/03/2024 10:00 – 11:30 (CET)

Till Riedel
KITInnovation Lab

Smart Data InnovationLab
We are accelerating innovation on data

History of SDIL
2014 PPP started from national IT Summit
2015-2019 Buildup Phase2019-2023 Experimentation

Innovation Lab
Die Plattform fürSmart Data Experimente
Beschleunigen Sie Ihre Smart Data
ForschungIndustrielle Forschungsfragen

Schnellere
Innovationszyklen

2023- Smart Data Innovation Services Catalog

Spitzenforschung zu Big Data Analytics
In Kooperation
mit

Unterstützt durchExperience from more than 80
Innovation Lab
experiments with Industry and SMEs
Typically 2-4 weeks to start, 6-9 month run time
2022

2021
BERTI-4.0: A Pre-trained Language Model for
Industry 4.0 Conversational Agents
Solving Accounting Optimization Problems in
the CloudLatentAgeI4WaferFab- KI-gestützte
Vorhersage alterungsabhängiger EffekteHyperparameter Optimierung von Deep Learning Modellen unter
Nutzung innovativer Messdaten für die Bodenfeuchte
KI-basierte flächenhafte
Grundwasserstandsmodellierung
Verbesserung von KI-Modellen basierend auf
räumlichen Daten in der Agrarwirtschaft

EpiNet - NLP für partizipative Datenerfassung

Verbesserung von rechenintensiven Tiefen
Neuronalen Netzen beim datensparsamen Lernen

WearNet: Deep Learning Toolkit für Wearable
Bewegungsanlyse

Airformer: Deep Learning Systemen für LuftQualität Vorhersagen und Analysen

Cognitive Matchmaking bei Communities vonLernplattformen

Knowledge Distillation for Relation Extraction
Unsupervised User Interface Representation Learning (UUIRL)
Rapid Urbanism KI Agent
Optimierung der Clusternutzung für das Reinforcement Learningvon Nesting Problemen
Automatische quantisierte neuronale Architekturen Optimierung
zur Zielerkennung und -Verfolgung
Deep Learning Ansätze für Intelligente Ergonomische Sitzmöbel
KI-Turbine: KI-basierte Betriebsüberwachung von Turbinen

Data Clean Room Concept
Goal since beginning : Accellerate Innovation on Data
State of the Art Infrastructurein Germany/ On-PremiseCommunity
Trust needs community

Legal & Governance
Scalability needs standards

Common Assets: Data, Tools, Algorithms
We started with idea of accumulating fair share: this did not scale!
How can we scale to serve a large number SMEs??

Innovation Lab

Smart Data Innovation
Services
Since 2023 new Catalog• Focus on reseach transfer offers• Target: innovative SMEs and collaborative research withindustry

Currently more than 20 Services• all services should be usable in a (pre-competitive)commercial setting• Can we sustain beyond funding retaining SME focus?

Legal challenges with „Commercialization“• Funding rules are complicated in Europe (de-minimis,
taxation, General Block Exemption Regulation)• Integration of offers from partners

www.sdil.de/services

Innovation Lab

Positioning in theGerman AI-Data-Service Ecosystem
Large funded offer inside Germany that often overwhelms SMEs:• Additionally to EDIHs, etc: Germany has very federal structure for research transfer• Prominently 4 AI-Service-Centers: giving access to AI infrastructure to SMEs

Despite funding: access to infrastructure for start-ups, scale-up and classical
SMEs beyond research stays challenging

Goal SDIL starting 2024: Integration of services usable undercommercial conditions beyond immediate ecosystem in Germany
Federation of iSpaces: Create also value accross Europe.
What makes sense to offer cross-border???

Innovation Lab

Smart Data Solution Center
BW

Innovation Lab

 KIT/SDIL’s regional initiative (also started 2014)
Association (in founding) with participation of SME,
industry and academia
Help adoption of AI and Data Space technology
Sustained via membership fees
USPs:
Networking, Workshops/Training, Access to ComputeKI-Allianz Baden
Württemberg
Data platform

Sharing AI assets for cross-sector AI applications
We design and implement an operational, customisable
platform for companies, preferably SMEs, public andscientific institutions according to a private-public
partnership model based on the following principles:
1. generation, management and sharing of data and AI models
2. compliance with ethical and legal compatibility
3. value enhancement through services, cross-sector linking andstandardised metadata
4. access to computing resources for the execution of AI models

Innovation Lab

Elephant in the room

Innovation Lab

Data spaces are currently driven by politics not business ?!

Why should SMEs then invest in data space technology andconsulting already today?

AI is the driving force behindthe data economy

Innovation Lab

 The main challenge of SMEs joining data spaces is motivation
1.
2.
3.
4.

AI is a driving factor for value generation on data
Data Spaces and Innovation Hubs need to evolve together
i-Spaces can be an important accelerator for data innovation
Data innovation in i-Spaces need to prepare compliance with data spaces

 SMEs should be supported based on their needs
 Data Spaces need to come “for free” facilitated by technology provided by hubs thatneed stable add-on funding to support this(mix of public and private funding needs to be improved)

Engaging SMEs andStart-ups in Data Spaces
12/03/2024 10:00 – 11:30 (CET)

Denia Kanellopoulou, PhD
NCSR “Demokritos”

A Pioneer in Scientific Excellence & Innovation● NCSR "Demokritos" is the biggest multidisciplinary public research center in Greece● The Institute of Informatics and Telecommunications (IIT) and more specifically its
Intelligent Information Systems (IIS) Division leads cutting-edge R & D activities focusedon Artificial Intelligence (AI)● “Demokritos” has founded the Technology Park “Lefkippos” hosting spin-offs andstart-ups that develop innovative and high added-value technology products● IIT’s digital innovation unit has established the “ahedd” digital innovation hub in orderto develop an open cross-thematic (AI, Big Data, IoT) innovation ecosystem

Challenges in Greek Data Value Landscape● Greek SMEs rank last in terms of digitalization in EU and only 2.6% of enterprises use AI (DESI 2023)● 20% of Greek enterprises want to invest in AI (SEV 2024)● Greek economy is recovering from a long economic recession● Lack of investments in research and innovation● Lack of skilled data and AI professionals

Leading AI-powered & Data-driven Innovation● Top enabler of AI Transformation in Greece● Orchestrator of a 5G- and IoT-enabled smart-city testbed for AI
applications at Demokritos campus● Major role in key EU & national AI initiatives● Wide network of business stakeholders● Coordinator of Smart Attica EDIH, the first EDIH for AI in GreeceSmart Attica: “AI-powering Greece”● Smart Attica aims to increase the digital maturity of Greek businesses and local authorities.
● The core driving force is Artificial Intelligence, complemented by the expertise in High-Performance
Computing, Cybersecurity and other emerging digital technologies like IoT, 5G and sensors.
● 17 acclaimed partners incl. Research & Academia, National Infrastructures, Tech Transfer & Accelerators,
Banking & VCs, Business Associations, Public sector & Society● More than 30 associate partners bringing sector-specific needs, willing to experiment or offer
complementary services.

Focus & Challenges

Energy & Environment

Supply chain & Mobility

Culture & Tourism

Priorities:
Natural resources use
Energy efficiency
Environmental impact

Priorities:
Logistics
Urban mobility
Transportation

Priorities:
Citizen participation
Visitor experience●

Administrative effort, Co-funding scheme (EU + Member state) delays●

Use cases & Commitment from SMEs●

Data availability and provision policies

Thank you!

DATA SPACESYMPOSIUM
2024
Experiences andchallenges with SMEs

DATA SPACESYMPOSIUM

M AG . JA KO B LO G A R

KNOW CENTERA E U R O P E A N I N N O VAT I O N A N D R E S E A RC H C E N T R E
F O R T R U S T W O RT H Y A I A N D D ATA S C I E N C E .

 20+ years of experience 100 employees 100+ industry projects per year
 50+ publications per year research & develop innovations in data
science and AI models
 focus on trustworthiness and explainability

MISSION&VISIONMISSION&VISION

Our mission is to make data and AI
models trustworthy, explainable
and accessible, also for SMEs.

MISSION&VISION

Data Spaces are an infrastructure thatenables fast, efficient and secure sharingof data.
It can generate benefits for the entire
European ecosystem and strengthen thecompetitiveness of European businesses.

Project“dataSChare 1.0”
Data Spaces in Supply Chains –Industry Requirements

WHAT ARE THE
USECASES?

WHAT ARE THE
USECASES?

USE CASES ranked by C-Levels:

Optimization of supply chain
end-to-end visibility

1

2

3

Source: “dataSChare – Data Spaces in Supply Chains – Industry Requireme

WHAT ARE THE
USECASES?

USE CASES ranked by C-Levels of
SMEs:
Integration of suppliers(with and without EDI)

1

2

3

Source: “dataSChare – Data Spaces in Supply Chains – Industry Requireme

WHAT ARE THE
USECASES?

USE CASES ranked by C-Levels of
SMEs:
Optimization of
Sustainability

1

2

3

Source: “dataSChare – Data Spaces in Supply Chains – Industry RequiremeMAIN BARRIERS FOR THE
USAGE OF DATA SPACES:USECASES&OBSTACLES

Uncertainty because it
is a new technology

1

2

3

4

5

6

Source: “dataSChare – Data Spaces in Supply Chains – Industry RequiremeMAIN BARRIERS FOR THE
USAGE OF DATA SPACES:USECASES&OBSTACLES

Lack of ressourcesin capital and personel

1

2

3

4

5

6

Source: “dataSChare – Data Spaces in Supply Chains – Industry RequiremeMAIN BARRIERS FOR THE
USAGE OF DATA SPACES:USECASES&OBSTACLES

The benefit of 1:n data
connections/sharing was not clear

1

2

3

4

5

6

Source: “dataSChare – Data Spaces in Supply Chains – Industry RequiremeMAIN BARRIERS FOR THE
USAGE OF DATA SPACES:USECASES&OBSTACLES

Technical readiness was seencritically –connection to ERP-Systems is crucial

1

2

3

4

5

6

Source: “dataSChare – Data Spaces in Supply Chains – Industry RequiremeMAIN BARRIERS FOR THE
USAGE OF DATA SPACES:USECASES&OBSTACLES

Doubts about data
security

1

2

3

4

5

6

Source: “dataSChare – Data Spaces in Supply Chains – Industry RequiremeMAIN BARRIERS FOR THE
USAGE OF DATA SPACES:USECASES&OBSTACLES

Low data
quality

1

2

3

4

5

6

Source: “dataSChare – Data Spaces in Supply Chains – Industry Requireme

HOW WE
APPROACH DATASPACE PROJECTSHOW WE
APPROACH DATASPACE PROJECTSCUSTOMER CENTRICITY:
Take a customer centric approach:Define the benefit companies andorganisations can reap from sharing
data.

1

2

3

4

HOW WE
APPROACH DATASPACE PROJECTSA STRONG USECASE IS THE KEY:
Find a usecase that is strong
enough (pain, gain, job) thatcan best be solved with a data
space.

1

2

3

4

HOW WE
APPROACH DATASPACE PROJECTSSIMPLICITY:
Simple & clear interfaces, lowintegration effort – not everybody
has an IT department or wants to
involve it in a complex data space
project.

1

2

3

4

HOW WE
APPROACH DATASPACE PROJECTSONBOARDING & ASSESSMENTS:
Maturity assessments and assisted
onboardings can help to lower entry
barriers for SMEs.

1

2

3

4

Thank you
for your attention!
We are happy to meet you!

Data Spaces Symposium
Experiences from EDIHs, iSpaces at local and Regional
level:

Jeanette Nilsson RISEBDVA labelled i-Spaces 2023

Infrastructure
Technology(12 KPIs)

Federatio
n (11
KPIs)

Services
(11
KPIs)

Data
EthicsSustainability (8KPIs)

Ecosystem
Impact(5
KPIs)

Projects
Sectors(13
KPIs)

RISE ICE I-space

Here you can add a pictureof your organisation or an
i-Space related picture

A full-scale research datacenter andtestbed with the objective to be an
enabler in the development of the AI &datacenter ecosystems by increasing
knowledge and competence.
More than 100 projects have been
performed with more than 100 project
partners since the start 2016. Partners
are Ericsson, ABB, Meta, LTU, Vertiv,
BP Castrol.
Focus areas:• Data centres• 5G and Edge
• AI and Data Spaces• Energy efficiency

Aktuellt Sverige• Första kunderna!
• Integrering RISE - RISE –AstaZero• Följer roadmap• 5G• Samarbete mellan TEF:ar och
EDIH

89

Roadmap for AgrifoodTEF Swe
Sat
2023-2024• Build Team• Internally Sverige• SEU-TEF• Build processes• Onboarding• State aid• International exchange(customers from other countries)

2025-2027

2028->• Value creation forcustomers• Operation (see 2025-2027)• Service build up• Web• Customer cooperation• Continue Service
development• Build Services• Continue Customer
Cooperation• Understand Customer needs• Infrastructure• Competens• Making the TEF well known• Create Policy Network• EDIH• Policy lab• Etc• Economically sustainable forall stakeholders• Companies are paying forservices• SME applies for funding (?)•• Work with policies• Make the Tef Independent• Long term Sustainability• Ways of working• Economy90 Thank you for the attention

 Jeanette.Nilsson@ri.se

AI/ HPC-Ecosystem expert
BDVA, Member of the Board of Directors of RIAG member
Sweden's representative in the EU's AI and Business Digitalization Working Group
with responsibility for the Partnership for AI, Data and Robotics (Adra)