Data Ecosystems for Geospatial Data · 2020. 6. 17. · Data ecosystems for geospatial data Data...

Data Ecosystems

Geospatial Data

Data ecosystems for geospatial data - JRC/IPR/2019/MVP/27812Data ecosystems for geospatial data -

JRC/IPR/2019/MVP/2781

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Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781

“Data ecosystems for geospatial data - Evolution of Spatial Data Infrastructures”

• Ongoing study, started in January 2020

• Performed by the Luxembourg Institute of Science and Technology (LIST) in close collaboration with Joint Research Centre of European Commission.

• Identify and analyse a set of successful data ecosystems and to address recommendations in support of the implementation of data-driven innovation in line with the recently published European strategy for data.

Sharing the methodological approach undertaken;

Presenting identified data ecosystems

Call for:

• Ecosystem use cases

• Identifying & reaching ecosystems' experts

Rationale of the session

1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander

Kotsev, JRC

2. Study objectives and workplan - LIST

3. Data Ecosystems Identification and Selection - LIST

4. Ecosystem Thinking & Modular methodological framework - LIST

5. Illustration of data ecosystem analysis with experts

Rennes Urban Data Interface - Ghislain Delabie, OuiShare

Machine Learning for Geospatial Data - Sean Wiid, UP42

Data cycles, feedback from the field - Jean-Charles Simonin, ENEO

6. Interactive session

7. Conclusion & Next activities

AGENDA

1. Landscape of the study « Data Ecosystems for Geospatial Data”

- Alexander Kotsev, JRC

AGENDA

Data ecosystems for geospatial data - JRC/IPR/2019/MVP/2781

Alexander Kotsev, JRC

Landscape of the study

INSPIRE - STATE OF PLAY

• The INSPIRE Directive is 13 years old (now formally a teenager)

• Deadlines for full implementation are approaching

• What has changed in the past few years, and what are our

outstanding challenges?

1. Technological perspective

2. Organisational perspective

3. Political perspective

We have come a long way!

1. TECHNOLOGICAL PERSPECTIVE

Technology in 2007

API4INSPIRE

260+ million spatio-temporal, pan-European

measurements

• standard-based access through OGC

SensorThings API

• data provided in a public cloud through

virtualization

• harvested air quality data from multiple sources:

• national INSPIRE download services

• European Environment Agency

http://www.datacove.eu/ad-hoc-air-quality/

Technology in 2020

1. Heterogeneous data sources○ Citizen data / personal data

○ Remote sensing (e.g. Copernicus)

○ IoT

2. New technologies

○ Data handling at the edge/fog

○ Virtualisation and cloud computing

○ From data collection to data connection (APIs)

3. New standards

○ Embracing web best practices

○ Following an agile and inclusive approach

■ SensorThings API

■ OGC API - Features

1) Who does what in the European context?

○ Distributed system

■ 7000+ data providers (tip of an iceberg)

■ Federated governance

■ Excellence on subnational level

○ Emerging agile approaches at multiple levels

■ Hackathons, code sprints

■ wikifikation / Git repositories

2) Resources for SDI and sustainability of infrastructures

○ Many developments are based on projects

■ Projects do end

3) How to modernise/update existing infrastructures

■ Technologies changes are fast

■ Procurement and organisational changes are not

4) “Follow the user”

○ Sure, but how?

2. ORGANISATIONAL PERSPECTIVE

1) Europe Fit for the Digital age

○ European Strategy for Data

• Establish a pan-European single market for data

• Regulatory sandboxing

• Emphasis on the benefits of different actors

• Sector-specific data spaces

○ White paper on AI

• Extensive reuse of available data

○ Open Data Directive

• High-value datasets (exposed through APIs)

2) European Green Deal

o GreenData4all initiative

• Modernising INSPIRE

o Destination Earth

3. POLITICAL PERSPECTIVE

• The INSPIRE community is healthy (and growing)

• There are favourable conditions for data-driven

innovation in Europe!

Q: Is spatial still special?

A. Yes, everything that happens happens

somewhere. Location information is fundamental

for an increasing number of use cases.

B. No, SDI developments should be merged into

mainstream ICT.

● We should

○ Avoid that SDIs become a big silo

○ Focus on sustainability & scalability

○ Learn from existing ecosystems

The context for the evolution of SDIs

FROM SDIs TO DATA ECOSYSTEMS?

1. Landscape of the study « Data Ecosystems for Geospatial Data - Alexander Kotsev,

AGENDA

Luxembourg Institute of Science and Technology (LIST)

Prune GAUTIER

Sébastien MARTIN

Slim TURKI

Research and Technology Organization (RTO)

Develops innovative and competitive solutions in

response to the key needs of Luxembourgish and

European economies.

• Employees: ~600 | Budget: EUR 66 millions

• Activities:

• Fundamental and applied scientific research,

development of knowledge and competences;

• Experimental development, incubation and transfer of

new technologies, competences, products and services;

• Scientific support to the policies of the Luxembourgish

government, businesses and society in general;

• Doctoral and post-doctoral training, in partnership with

universities.

LUXEMBOURG INSTITUTE OF SCIENCE AND

TECHNOLOGY

Interdisciplinary portfolios

• Smart cities

• Spatial sector

• Industry 4.0

• FinTech and RegTech

Fields of activity

• Digital innovation

• Ecological innovation

• Materials innovation

Objectives of the study

Investigate how Spatial Data Infrastructures (SDIs) can evolve into data ecosystems to support the goals of digital government in Europe.

• Take into account factors such as relevant actors, their responsibilities and data value chains, emerging data sources (e.g. the Internet of Things) and technical/architectural approaches (e.g. digital platforms, mobile-by-default, Application Programming Interfaces).

Address the interoperability between data ecosystems for different sectors and/or different countries and cross-cutting requirements for geospatial data.

Provide an input into the discussion on the future evolution of INSPIRE after the conclusion of the current implementation programme in 2020.

Identify existing Data Ecosystems and case studies for interoperability between such Data Ecosystems

Analyse / compare characteristics / requirements of Data Ecosystems and their interoperability

Analyse in depth a subset of Data Ecosystems

Develop recommendations for setting up Data Ecosystems and to enable interoperability between them

Work plan

> Desk research> Academic literature

> Reports (official reports, projects reports, etc.)

> Companies' documentation

> Qualitative data (interviews)

Technology Policies Standards, and Human resources

Necessary too Acquireo Processo Storeo Distribute, ando Improve utilization of Geospatial data

• Linear process in which data is published and made discoverable and usable

• No feedback loop between users and providers - critical to the potential sustainability and evolution of data ecosystems.

SPATIAL DATA INFRASTRUCTURES (SDIs)

Definition

DATA ECOSYSTEMS

Evolve and adapt through a cycle of

data creation and sharing, data analytics,

and value creation in the form of new products,

services, or knowledge, which, when used,

produce new data feeding back into

the ecosystem.

Complex socio-technical system of People,

Organizations, Technology, Policies and Data In

specific Area/Domain, that Interact with one

another and their surrounding environment to

achieve a specific Purpose

Data ecosystem =

Ecosystem analysed with a strong focus on data issues

• How Ecosystem Thinking may contribute to identify and

trigger the uptake of SDIs?

• What is the Position and Role of SDIs in Self-sustainable Data

Ecosystems?

• Addressing interoperability between Data Ecosystems

Rennes Urban Data Interface - Ghislain Delabie, Simon Saint Georges

AGENDA

• Looking for cases studies illustrating shifts from Data Infrastructures to

Self-sustainable Data Ecosystems

• With potential to enrich recommendations

• Diversity of the case studies/use cases is important.

• Regional/City, if possible with Public, Private sectors and Citizen

involvement.

• Thematic: Mobility, Agriculture, Insurance, etc.

• Generic, involving user data and feedback, such as recommendation in

tourism

• Community oriented, such open science networks

• Place / role of spatial data

• Where expert(s), documentation or standards are available and

accessible

DATA ECOSYSTEMS IDENTIFICATION

Evaluation

Rennes Urban

Data Interface

Tracking Technologies

for Supply Chain

Hotel reviews Tripadvisor, Yelp,

VOD Entertainment

(Netflix, Disney, etc.)

Vehicles and planes

fleets predictive

maintenance

Circular

economy

Pandemic

Weather

Forecast

Digital patient record

(e-health)

e-commerce

Marketplace

Agriculture

ML services on

space imagery

Pan-European Invasive

Alien Species Monitoring

and Reporting

Crowdsourced traffic

information (Waze)

Energy efficiency of

buildings

Location aware

dating services

National wide

data ecosystem

• Sought expertise

• General overview of the Data Ecosystem

• Specific (actual ecosystem participants speaking from their perspective)

• Kinds of experts

• Data owners (public and private);

• Data re-users (public and private)

• Platform actors

• Academic

• (End-users)

• Available for some online/onsite interviews

Looking for Experts

Q1: Do you know about Self-sustainable Data Ecosystems?

• With potential to enrich recommendations

• Where expert(s), documentation or standards are available and

accessible

Q2: Would you kindly recommend experts of already identified data

ecosystems?

Please share your suggestions

• Slim TURKI, LIST, slim.turki@list.lu

• Alexander KOTSEV, JRC, alexander.kotsev@ec.europa.eu

• Using the Chat

IDENTIFICATION of DATA ECOSYSTEMS

Suggestions?

4. Ecosystem Thinking & Modular methodological framework -

AGENDA

• How the ecosystem thinking may contribute to identify and trigger the uptake of spatial data infrastructures?

• (Oliveira & Loscio, 2018) : “Lack of well-accepted definition of the term Data Ecosystem”

• Ecosystem is a paradigm, to analyse a network and to act on it.

• Ecosystem emergence (Thomas, 2015)

• Ecosystem health

• Increasing data-reuse beyond the original purpose

• New modes of data creation

• Case of non-geospatial data

ECOSYSTEM THINKING

(Self-) sustainability:

• Well-balanced ecosystem

• Able to function and development without

direct government support

Combination of supportive factors:

• Value creation and business model

• Value distribution

FOCUS ON BUSINESS MODELS &

VALUE CREATION

Value Creation

Business

Models

• Orchestrating an ecosystem means managing the network relations.

• Originating from innovation networks (Gawer & Cusumano, 2014).

• Keystone actor(s) / actor(s) leadership• Orchestration concepts

• Ecosystem membership (size, diversity)

• Ecosystem structure (density, autonomy)

• Ecosystem position (centrality, status)

• Appropriability regime

• Knowledge mobility

• Ecosystem stability

• Kinds of orchestration• Organizational orchestration

• Technical orchestration• Standard / industry standard adoption

• Internal / external interoperability

• But intertwined

FOCUS ON ORCHESTRATION

Orchestration

Modular

Analysis

Framework

A MODULAR ANALYSIS FRAMEWORK

> Desk research> Academic literature

> Reports (official repor

ts, projects reports, etc.)

> Companies'

documentation

> Qualitative data

(interviews)

Ecosystem Summary

Provides an overall representation of the components of the ecosystem.

Focuses on Data Ecosystems key aspects

• Goal / purposes

• Main actors, their exchanges and communication

• Legal context and governance

• Technology specific aspects

• Cost and revenues / benefits

• Faced Barriers and incentives

First level of assessment, inspired by the

Business Model Canvas from Alexander

OSTERWALDER

Ecosystem Dynamics

Represents the interactions between stakeholders.

The graphical representation of this second layer of the

Framework finds its inspiration in network modelling tools.

While illustrating the resources exchanged between the

stakeholders, and their value, we can:

- follow the value creation,

- highlight the orchestration

- and evaluate the sustainability of the ecosystem ( balanced

counterparts missing, actor missing, value lost).

Goal centred, it gives a representation of

the actors commitment level and strength of interactions

Ecosystem Data flows

Focuses on the associated data flows / Data life cycles

• Data flows as proxy of the maturity and health of an

ecosystem

• Data cycle: (Pollock, 2011) "infomediaries — intermediate

consumers of data such as builders of apps and data

wranglers — should also be publishers who share back

their cleaned / integrated / packaged data into the

ecosystem in a reusable way — these cleaned and

integrated datasets being, of course, often more valuable

than the original source.“

• Approach and graphical representation are derived from

product lifecycle model

Q: Do you agree on the dimensions?

Q: Do you see missing dimensions?

Ecosystem Summary

Provides an overall representation of the components of the ecosystem.

Ecosystem Dynamics

Represents the interactions between stakeholders.

Ecosystem Data flowsFocuses on the associated data flows.

Data cycles, feedback from the field - Jean-Charles Simonin,

AGENDA

RENNES URBAN DATA INTERFACE

Ghislain Delabie, OuiShare

MACHINE LEARNING FOR GEOSPATIAL DATA

Sean Wiid, UP42

DATA CYCLES, FEEDBACK FROM THE FIELD

Jean-Charles Simonin, ENEO

AGENDA

Questions?

Discussion - Interactive session

AGENDA

Conclusion & call for participation

Next steps:

• Experts interviews, desk analysis, documentation

review

• Selection and in analysis depth analysis of 5

ecosystems

• Recommendations for setting up self-sustainable data

ecosystems

• Stakeholders workshop to present and challenge the

findings (Fall 2020)

• Joint Research Centre:

• Alexander KOTSEV, alexander.kotsev@ec.europa.eu

• Luxembourg Institute of Science and Technology

• Slim TURKI, slim.turki@list.lu

Contacts

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