Establishment of Sustainable Data Ecosystems - Recommendations for the evolution of spatial data infrastructures into self-sustainable data ecosystems

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The following recommendations come from the report "Establishment of Sustainable Data Ecosystems". They provide insights into the approaches that can be undertaken in order to ensure the evolution of contemporary spatial data infrastructures into self-sustainable data ecosystems. In order to give a clear picture of the recommendations, this section is divided in the four main categories that highlight the gap between a data provision philosophy and an ecosystem thinking.

  • Ecosystem Governance (EG)
  • Technical issues (TI)
  • Stakeholders engagement (SE)
  • Economic sustainability (ES)

For each of the four categories, a brief introduction of the context and the main actors is followed by recommendations which are provided in a structured manner. For each recommendation, the challenge at stake is summarised alongside the main barriers which need to be overcome. The description continues with best practice(s) which indicate how the challenges can be overcome. Recommendations are not to be considered in isolation, so relevant recommendations are also suggested.

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Ecosystem Governance

Context

The ecosystem governance designates the body of rules, procedures and practices that relate to the way interactions between the actors in the data economy are framed.

An ecosystem approach that is fit for purpose arranges all interactions and exchanges issues within the data ecosystem.

One of the main ecosystem governance issues, as described below, is to define which actor can embrace the role of orchestrator of the ecosystem.

Actors

Different actors are involved in governance, depending on the stage of the data life cycle / value chain.

Considering geospatial data for instance, public actors still have a strong role often through a legislatively defined mandate, however the private sector is increasingly contributing to the data creation processes for example through technical capabilities or even data providers by themselves.

EG01: Building a collaborative governance of the ecosystem.

Challenge

Governance is the key of the ecosystem sustainability. It is framing both the technical arrangement and the interactions between participants. Therefore, the key stakeholders need to agree on their roles and responsibilities beforehand.

Barriers

The governance model is difficult to adapt over time.

Difficulty to get a general agreement on specific issues, e.g. when competing entities have similar objectives.

Best practices

In Smart Agriculture, the API-Agro has chosen to be established as a company (Simplified Joint Stock Company). It is the outcome of a project and an industry-wide reflection on a large agricultural data portal specifically targeting the constitution of collaborative governance. They are 30 shareholders, and the governance is distributed between many public and private actors. Moreover, this use case illustrates the importance of predefining the exchanges settings on the components to distribute and their modalities. It shows also that a common agreement is mandatory to ensure the later acceptability of the platform and therefore of the ecosystem.

In the Disaster Management ecosystem, a collaborative governance is a prerequisite to enable the data sharing between stakeholders not always eager to share their assets. The public sector, backed up by related legislation plays a prominent role in liaising with the different stakeholders.

Related

SE01 - TI08 - EG02

EG02: Identifying the most relevant actor(s) to embrace the role of orchestrator depending on the nature and evolution of the ecosystem.

Challenge

The orchestration of an ecosystem is a key activity to insure its sustainability. The orchestrator is in good position to know the needs of the stakeholders. However, the role of Orchestrator is not obvious and independent, and for example we could wonder how a marketplace actor may be a platform leader. Often, this function is taken by another player of the ecosystem: a data provider, a public authority, or a data user. Moreover, the competences and skills needed to allow a stakeholder to act in the role of orchestrator can change during the life of the ecosystem.

Barriers

Difficulty to engage an actor skilled and incentivised enough to play this central role.

Difficulty to assess the Return on investment of activities related to ecosystem orchestration.

Best practices

In the data marketplace ecosystem, the central position of UP42, for instance, makes it possible to orient the ecosystem development based on feedbacks of their customers. More specifically, a central actor may identify new partners to bring on board, partly based on other partners suggestions.

In Spire, as there is no central orchestrator, orchestration functions are led by diverse actors in a distributed manner, based on local and specific business opportunities.

In Smart agriculture, the choice of a private orchestrator is based on the hypothesis that business related issues are crucial for the sustainability of the ecosystem.

Related

SE01 - EG07 – TI01

EG03: Clear consideration (roles, benefits, needs and means) for all stakeholders ensure the willingness to make the data ecosystem sustainable.

Challenge

Clear governance guidelines are required to ensure seamless interactions.

In particular, SMEs means and perspectives have to be considered.

Barriers

Governance rules need to take into account opposite perspectives

Best practices

The Mobility data ecosystem, as well as RUDI, highlight this aspect for public transport services. Indeed, the benefit for each stakeholder is easily understood. Users gain better services, bus companies more customers and cities public value.

Related

ES08 - TI03 – ES04 – ES05

EG04: The creation of a platform provides a strong incentive to structure the ecosystem.

Challenge

An ecosystem needs, as a prerequisite, to facilitate the interactions between stakeholders and the data exchanges,a platform that enables for example time saving(e.g. data discovery) and decreases negotiation costs. In addition, it enables the creation of easily scalable products.

Barriers

Willingness of the stakeholders to collaborate.

The need to standardise formats.

Competing platforms.

Risk of dominance of one actor (the operator) over all the rest.

Best practices

3/5rd of the analysed ecosystems are based around platforms.For Spire, they prefer for the moment to avoid any intermediaries in their business relationships.

For the disaster management ecosystem, the lack of data discoverability was one of the main factors leading to the creation of the ecosystem based on a platform enabling convenient data discoverability.

The data marketplace ecosystem is a particular illustration of the advantages of choosing a platform based on a marketplace model, which has implication for governance rules.

Related

TI08–SE02

EG05: Avoiding a fragmented landscape of stakeholders and a lack of centralised governance.

Challenge

In an ecosystem, stakeholders are numerous and various, and their links are not tight and obvious. Therefore, a significant risk of silos’ thinking exists.

Barriers

Absence of a central actor

Best practices

In the disaster management ecosystem, the orchestrator sets incentives to facilitate the development of a data sharing culture, through the organisation of collaborative activities showing the long-term benefits and the value of an ecosystem perspective.

Related

EG06 –EG01–TI08 –SE01 –TI03

EG06: Importance of considering and aligning stakeholders' different cultures.

Challenge

To reach its goal, an ecosystem needs to encompass actors from heterogeneous backgrounds for a common purpose. They also have different ways of working and difficulty to understand each other working approaches.

Barriers

Public and private actors may have contradictory objectives and different working cultures, preventing an efficient structuration of the ecosystem.

Best practices

In RUDI, to tackle this issue and to define a common way of working, the actors decided to organise frequent meetings to identify the issues, enable the solutions and ensure the commitment of all ecosystem’s stakeholders. In addition, a social sciences laboratory is working with the Metropole on this specific topic.

Related

TI01 -EG03 -EG08

EG07: Exploring the role of local authorities in local data ecosystems over time

Challenge

In local data ecosystems, the role of the orchestrator is often endorsed by local authorities, but their role in the long-term still need to be investigated. Another challenge is to shift from an active engagement to a distant public orchestration.

Barriers

Lack of empirical evidence.

Financial issue (citizen taxes).

Financial assessment of public value creation

Best practices

Within the RUDI ecosystem, local authorities may have a leading role during the emergence stage, building on previous Open Data initiatives.

Empirically, the orchestration is composed of networking initiatives, creation of a data exchange platform and its governance rules and a financial role stimulating the ecosystem through public procurement.

Related

ES01 –ES05 –TI01

EG08: Importance of networks

Challenge

How to leverage state-of-the-art research output remains a challenge. To support the sustainability of ecosystems, networks can be used to share good practices, to benchmark initiatives, but also to provide insights.At the same time, it is crucial to consider existing projects instead of re-buildings ecosystems.

Barriers

Low awareness.

Lack of incentives.

Lack of interest to participate in networks.

Best practices

Both RUDI and the Smart agriculture ecosystems claim to be leveraging on the scalable use of research outputs.

Rennes Métropole, as a member of Smart Cities Network (e.g.Eurocities KnowledgeSociety Forum), intends to tap into the network in order to validate and disseminate the outcomes of their project, as well as to improve it with feedback and approaches shared by other smart cities.

Related

EG07

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Stakeholder Engagement

Context

Stakeholder engagement is the process used within an ecosystem to engage relevant stakeholders for a clear purpose. It obliges to involve stakeholders in identifying, understanding and responding to sustainability issues and concerns.The challenge is the risk of not having enough contributors (not achieving critical mass) –leading to lower service creation.

Actors

Stakeholder engagement is one of the main functions of an orchestrator.

SE01: Defining and integrating the relevant stakeholders enabling the success of the ecosystem.

Challenge

In less structured ecosystems, there are difficulties for companies to identify the fruitful interdependencies and thus business opportunities. This function is an essential component of the orchestrator.

Barriers

Low degree of awareness in the ecosystem.

Best practices

For data marketplace ecosystems, this function is endorsed by the main orchestrator leading the platform. Data aggregators, especially in the absence of a central platform, play an important role in the initial structuration of the ecosystem and especially the data flow.The same stand for the IT Service Providers from the Services provision side.

Related

EG01 -EG02 –ES04

SE02: Distributing value between the stakeholders

Challenge

How to distribute value among stakeholders in a fair and balanced way in order to ensure the stakeholders' engagement.

Barriers

Difficulty to compare tangible and intangible assets.Monetization of non-financial values such as public value, common good, quality of life, well-being.

Best practices

The data marketplace ecosystem highlights the role of the central orchestrator. Each stakeholder of the ecosystem is considered as a particular partner, and the financial remunerations are negotiated on an individual basis, considering data and/or service provided, as well as their engagement’s value (revenue sharing model).

Related

ES04

SE03: Considering citizen as true stakeholders

Challenge

Citizens are more than just data objects. The goal is not to consider citizens only as data providers or end-users consuming products and services, but as central actors contributing the definition of the scope and the goals of the ecosystem, and being active participants aware of their data alongside their re-use.

Barriers

Trust (of citizen regarding public actors' concerns).

Lack of citizen’s interest considering data issue.

Reluctance to share personal data.

Perception of legal risk concerning personal data.

Best practices

In RUDI, inclusive data ecosystem governance based on co-construction of the ecosystem and its governance rules is led with participatory labs and collaborative workshops.

Moreover, trusted third parties are considered as a mitigation solution to trust issues concerning citizen data sharing.

Related

SE05 –TI01 –TI04 –SE08

SE04: Promoting data literacy among all stakeholders

Challenge

There are heterogeneous levels of knowledge and awareness regarding the potentials and the methods to benefit from the re-use data.

Barriers

Some data producers do not realise the usefulness of their data forthe creation ofdata-driven services providers(data re-users).

Best practices

In RUDI, the orchestrator uses workshops, trainings and panel sessions open to all stakeholders in order to raise the level of knowledge among the Quadruple Helix representatives (University, Industry, Government, and the Public).

UP42, as platform leader, is organising workshops and promotes the uptake of state-of-the-art knowledge and methods on its platform, to orchestrate the knowledge dissemination in its ecosystem.

Farmers are not traditionally used to utilise data in their day-to-day work, even if data-centred agriculture is their future. Therefore, API-Agro, through its role of orchestrator, has a wide mission to improve data literacy with services providers and associations. For logistic and tracking, there is a need to bring geospatial data literacy to a broader set of new actors of the ecosystem, including the insurance sector.

Related

EG02 –EG06 –TI01 –D10 –ES01

SE05: Organising events to increase awareness in the ecosystem and interactions frequency

Challenge

Reaching the critical mass of stakeholders is not sufficient for the ecosystem to be effective: the viability of the ecosystem depends on the interactions between stakeholders.

Barriers

The data provision paradigm.

Best practices

In RUDI, the local authority intends to take benefit of external events, such as InOut9, to organize parallel working sessions dedicated to the local data ecosystem.

Related

SE04 -SE03

SE06: Building a data social network at the scale of the ecosystem

Challenge

Combining a technical layer (e.g. data catalogue) with a networking layer where actors may meet, exchange and request data, and thus, frame the ecosystem and increase the self-awareness of the different actors on the functioning of the ecosystem. This way, the ecosystem may attract new participant and ensure the current ones.

Barriers

Best practices

For RUDI, Rennes intends to build a socio-technical component where stakeholders may fulfil their needs for data, processing and expand the network of business partners.

Related

EG08 –SE01 –SE03

SE07: Raising awareness on therole of regulators for the creation of incentives

Challenge

Integrate the decision made by Regulators on diverse topics within the broader context of data ecosystems.

Barriers

Regulation not enough connected to -aware of-business issues.

Best practices

The logistic tracking ecosystem is based on AIS(Automatic identification system)data which collection and reuse are made mandatory by the International Maritime Organization (IMO). Nevertheless, this regulation was not directly intending to build a data ecosystem.

Related

EG02

SE08: Emphasizing the role of NGOs

Challenge

Non-Governmental Organisations(NGO)are valuable actors to engage as data providers but also to increase citizen engagement.

Barriers

None.

Best practices

In Smart Agriculture, NGOs are key actors from the data collection perspective, but also by encouraging data owners to share the data with other data ecosystems participants. They can rely on a long tradition of cooperative organisation. Moreover, in RUDI, NGOs are expected to complement the role of orchestration currently done by the local authority by becoming themselves “call for projects” leaders.

Related

SE01

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Technical Issues

Challenge

This represents the technical side of the ecosystem. It covers a plethora of different cross-cutting technical issues related to the access to data, architectures, standards, and technologies for both the provision and use of data.

Actors

Considering the stage of the ecosystem, the leading role can be embraced by different actors, but given the specificity and technological nature of data ecosystems, all actors are to be considered impacted.

TI01: Fostering ecosystem sustainability through problem solving approach, leading to new data cycles

Challenge

The use and reuse of data of different kinds, originating from various sources (public, private, personal data, etc.), through new services, is expected to create a virtuous circle leading to new data cycles. In particular, it is suitable to create new (public) data intensive services, customized for the needs of different purposes. How to extract the value of combined albeit heterogeneous data remains challenging.

Barriers

Data-sharing approaches and decisions steered only by data providers.

Data quality.

Data formats (proprietary –not proprietary, documentation, etc.).

Data standards (data models, formats, etc.).

Best practices

In RUDI, the orchestrator is organising “calls for projects” with the specific target to make the ecosystem more tangible, to define more accurate governance rules, to showcase the business opportunities. These “projects” are using already accessible data to produce data-driven services that in turn create new datasets, which are accessible and reusable, i.e. contribute to the uptake of private data for public good. In parallel, RUDI is organising dedicated hackathons.

In Smart agriculture, personalised services (such as precise irrigation) combine a large range of data.In the logistic and tracking ecosystem, the ecosystem approach allows to integrate new downstream actors such as insurance companies (as data re-users and new data providers). In the data marketplace ecosystem, LiveEO is developing a predictive infrastructure management service for Deutsche Bahn. Through this activity, it generated new data that are made available through the data marketplace.

Related

ES04 -ES08–D10

TI02: Strengthen the relationship between the data ecosystem development and the digital transformation of stakeholders

Challenge.

Data provision can be hindered by internal legacy IT systems.The digital transformation of public services may be enabled by data ecosystems through the provision of field-generated data instead of purely statistical data.

Barriers

Access to IoT data.

Data literacy.

Legacy infrastructures and systems.

Best practices

In RUDI, the local authority encourages the release of data through showcasing profitable business models and orienting call for projects leading at the same time to the update of these internal systems.

The Energy ecosystem has been stimulated by the Industry 4.0 development.

The Smart agriculture ecosystem showcases that the democratization of IoT devices and systems intensifies the exchanges of data and leads to completely new data cycles.Beyond the update of systems, it is also an argument to foster the participation in the ecosystem, as the disaster management and the Smart Agriculture ecosystems highlighted that organisations may capitalize on their information.

Related

EG03 –TI01

TI03: Grasp the opportunities for data sharing by private companies as result from the GDPR entry into force.

Challenge

The challenge for companies is to define processes allowing the release of personal data while complying with the provisions of the GDPR.

Barriers

Legal responsibility

Lack of well-established approaches for sharing personal data while preserving privacy

Best practices

In RUDI, the orchestrator intends to support the stakeholders in the release of personal data with a legal support.

Related

ES08 –SE02

TI04: Fostering data crowdsourcing

Challenge

Crowdsourced data are valuable but might be difficult to collect.In addition,the quality of the data that is collected needs to be ensured, as well as the sustainability of the data-collection approach.

Barriers

Personal data protection rules and privacy.

Lack of consideration and long-term interest.

Data literacy.

Data quality and validation.

Best practices

For crowdsourced data in the Weather ecosystem, the incentives could be micro-payments or goodwill to contribute to the public good, but also the role of entertainment to motivate participation. For instance, in the United States, the crowdsourcing of such data is shaped by the National Oceanic and Atmospheric Administration(NOAA). NGOs play an important role in the local ecosystem of Rennes for promoting and explaining the value of personal data. In the mobility ecosystem, citizen input is proven to be useful for the sustainability of the ecosystem through the collection of new data.

Related

SE04 –TI01 –ES08

TI05: Stimulate the datafication of a broader range of sectors

Challenge

Clearly, all domains are not at the same in terms of digital maturity. Therefore, they do not have the same opportunity to participate in the emergence of data ecosystems.

Barriers

Digital literacy.

Data Standards.

Awareness of the ecosystem.

Best practices

In logistic and tracking, the new business opportunities created by the ecosystem for insurance companies are a strong incentive for the datafication of the sector.

For the Smart agriculture ecosystem, the API of API-Agro Platform is a driver to encourage all relevant stakeholders to produce new or better data.

Related

SE04 –ES04

TI06: Integrating data ecosystems and data cooperatives & trusts

Challenge

Building on data cooperatives for the benefit of other data spaces, to enrich data ecosystems.Defining the technical links between data cooperatives and data platforms.

Barriers

Data collection rules (privacy, quality, roles and responsibilities).

Best practices

Within the healthcare ecosystem, platforms exist such as OpenHumans.org which organise personal data pipelines,handling especially the consent for the data reuse.

One important incentive is the awareness of contributing to public good through data creation or sharing.

Related

ES08 –EG07 –ES04

TI07: Put the APIs at the core of data sharing.

Challenge

APIs act as the ‘glue’ of data ecosystems as they link different already existing architectures in practical terms. APIs are therefore to be considered as a mandatory component for ensuring the economic viability and sustainability of data ecosystems.

Barriers

Large dependency on pre-existing systems.

Development costs.

Data ownership issues.

Best practices

In RUDI, the API has to link heterogeneous systems such as the local open data platform,or companies' data.

One of the achievements of UP42 is to provide such APIs for data sources but also processing APIs for running algorithms or financial clearing.

For the Smart agriculture ecosystem, the API managed by the central platform is considered as a means to orchestrate internal and external APIs of stakeholders.

Related

EG04 –TI08 –ES03

TI08: Choosing the platform architecture (tools and capacity) based on the specific features of the ecosystem.

Challenge

Different architectures are possible (e.g. federated, centralized, edge). No universal solution exists, and those different architectural approaches present various benefits and risks.

Another challenge relates to the necessity for handling data within a single architecture, but with different access-and users-rights.

Barriers

Lack of data discoverability.Absence of a single solution that can satisfy the needs of all actors.

Best practices

For UP42, the choice of a centralised architecture allows to offer on their platform data quality improvement blocks at the central node of the architecture, offered as block processing services.

RUDI selected a different approach, a federated, well adapted to big players of its local ecosystem. As they are often equipped with complex and legacy systems, it is easier to add a node in a federated system. Moreover, a federated architecture makes it possible to access and manipulate some data unreachable by other architecture models. Nevertheless, there is a doubt pertaining the suitability for SMEs.

Related

EG03 –EG04 –TI07 –TI10

TI09: Integrating in the platform not only data but also services and even computational infrastructure.

Challenge

Currently, platforms are mainly based on data provision. The challenge is to add complementary services to offer a single point of supply.

Barriers

Different expertise needed.

Best practices

UP42 has chosen this option by design. This brings costs sharing and economy of scale between the actors. RUDI is considering this option, but it implies to review a large range of component from technical ones to government rules.

Related

TI01 –EG04

TI10: Data standardisation is an enabling condition to the emergence of data ecosystems.

Challenge

Standardisation is heterogeneous depending on the application domain. Withinthe 5 in-depth analyses, geospatial data standardisation was never the main issue.However, downstream data that have to be combined are more often problematic.

Barriers

Standardisation of Data Models.

Commonly agreed standards.

Rapid change of technology.

De facto versus de jure standards.

Conflicting or competing standards.

Best practices

Within the logistic and tracking ecosystem, the standardisation of Automatic Identification System(AIS)messages is of paramount importance to create solid foundations for the ecosystem.

Related

TI12 -TI01 –EG01 –ES08

TI11: Identify and adopt the suitable data and metadata models, standardised where possible.

Challenge

One of the main challenges faced by ecosystems is the lack of common structures and semantic models (ontologies). This is also a standardisation issue.In addition, ensuring that data is provided at the right semantic granularity level thus ensuring the data ecosystem viability in essential.

Barriers

Lack of capacity.Immature standards.

Lack of support by mainstream software tools.

Approaches that do not follow standards.

Best practices

The disaster management ecosystem illustrates the bidirectional relationship between standardisation and ecosystem emergence,with standardisation being at the same time a prerequisite and a positive outcome of the ecosystem.

In RUDI, the definition of standardised data models is not a prerequisite fo rthe emergence of the ecosystem. The standardisation of data models is expected to evolve over time. Rennes intends to address this issue through the provision of the necessary details through the metadata of the data catalogue.For logistics and tracking purposes, the right granularity level is of paramount importance not only to comply with the data collection requirements from the regulation, but also to fulfil the needs of downstream use such as insurance.

The smart agriculture ecosystem confirms the possibility to proceed a progressive alignment of the data and solutions to standards and thus to interoperable and API based ecosystems.The Legal ecosystem showcases the importance of two concepts: data provenance, and data re-use traceability. Concerning data re-use traceability, it is an important dimension of the judiciary side of the data ecosystem as some legal systems are requiring to track who accessed which piece when a case is processed, and digital documents and data make this more complicated to ensure. Data provenance is of paramount importance to ensure not only the trust, but also the mere validity of the analysis.

Related

TI10 –SE04 –EG01

TI12: Aligning the data ecosystem with other components such as cloud and software ecosystems

Challenge

Ensuring the technical evolutions over time.Ensuring the links with cloud infrastructures and the software and hardware industries.

Barriers

Resistance to change.

Digital literacy.

Different objectives.

Best practices

For Logistics and Tracking, the ecosystem sustainability relies on the development of up-to-date algorithms, coming from external domains such as big data and artificial intelligence.

As a marketplace, UP42 also encompass the alignment of the platform with the other relevant components such as processing algorithms and infrastructures.

Related

TI13 –EG03 –SE02

TI13: Facilitate the access to real timedataand time series.

Challenge

One of the challenges is to link the data ecosystem to external cloud infrastructures, for example to handle over-sized datasets and to benefit from the rich ecosystem of algorithm developers.

Another challenge is to ensure the availability and utilisation of real-time data streams.

Barriers

Loading time.Single point of access.

Outdated legacy IT systems.

The IoT landscape is fragmented.

Multiple competing standards.vendor lock-in.

Networks latencies acting as a bottleneck.

Best practices

In the data marketplace ecosystem, some companies (e.g.Live-EO) are mature regarding data-analytics. These companies prefer to take benefit of existing commercial cloud infrastructures which at the same time have a big community of developers.

In the disaster management ecosystem, the challenge of emergency data collection is overcome by streaming data.

Related

TI01 -D12 –ES02

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Economic Sustainability

Challenge

Economic sustainability refers to practices that support long-term stability and growth of the ecosystem. The ecosystem resilience requires harmonious and evolving business models.

Actors

Economic sustainability is mainly a downstream task embraced by private companies even if also largely impacted by other topics such as governance.

ES01: Integration of Open Access, Open Source, Open Innovation and Open Data Paradigms

Challenge

Competition between public and private actors for data creation.

Reluctance of companies to open their data.

In addition, some companies consider that open data policies might lead to unfair competition.

Barriers

Divergent objectives.

Resistance to change.

Best practices

In RUDI, they intend to overcome the reluctance of private companies by showcasing the global added value of adopting open data approach.

For the Logistic and tracking ecosystem, this challenge is still to be overcome.

The Disaster Management data ecosystem witnesses the importance of combining open data and data ecosystem building as open data is recognised to facilitate a data sharing culture.

Related

ES04 –EG03 –EG07 –SE07

ES02: Emphasizing an adaptive and agile orchestration for the ecosystem evolution, especially for data collection

Challenge

The alignment of the ecosystem with scientific, technical and business trends is mandatory. For instance, feeding the ecosystem with accurate and new data rapidly is a success factor for the evolution of an ecosystem.

Barriers

Cost of data collection.

Difficulty to clearly identify data needs.

Best practices

For the logistics and tracking ecosystem, Spire can reconfigure existing sensors or to send new sensors and satellites in a fast and cost-effective manner. New satellites costs represent 1% of traditional satellites costs. In addition, with this new data collection practice, Spire can address Industry 4.0 challenges.

Related

TI01 -TI13 –EG05 –EG03

ES03: The creation of a platform is a strong enabler of business opportunities and implementation, as well as related data flows.

Challenge

Without an ecosystem, it is challenging and costly for companies to contractualise new businesses.

Barriers

Contractual issues.

Financial exchanges issues (security, etc.)

Best practices

In the Smart agriculture ecosystem, the central platform provides automatic transaction facilities.

The data marketplace ecosystem provides additional insights into the advantages of a platform through providing financial clearing operations. This service is suitable to attract big players that would not be interesting in addressing SMEs in the European market.

Related

TI08 –SE02 –EG03 –EG04

ES04: Synergies between individual stakeholders’ business models are the key condition for the overall ecosystem sustainability.

Challenge

To reach the sustainability of the ecosystem, both local and global financial profitability must be addressed. Especially for the platform structuring the ecosystem if there is one.Thus, there is a need to balance individual competition and global cooperation. The organisation of interdependencies between the stakeholders of the ecosystem is an orchestration issue of paramount importance.

Barriers

Low degree of self-awareness of the ecosystem due to a lack of contacts between the ecosystem stakeholders.

Duality between competition and cooperation enhanced by public / private cultures differences and mismatch in size.

Lack of trust.

Fragmentation.

Best practices

In RUDI, there is a particular commitment to help companies defining their business models, for example through “Call for projects”. The aim is to lead to sustainable data creation through business models.On the other hand, the local authority is planning to monitor the impacts leading to public value creation in order to justify their investment.In UP42, the platform leader is actively fostering “in-house” businesses and value exchanges.

Related

SE02 –TI01 -EG07

ES05: Data ecosystems rely on long term engagements.

Challenge

Return on Investment is not always achieved in the short term

Barriers

Trust.Absence of long-term vision.

Short-term business objectives.

Best practices

In the data marketplace ecosystem, the platform leader intends to create a data business model generating long-term and cumulative effects.

Related

EG03 –EG04 –SE02 -TI02

ES06: Legal issues are framing the ecosystem through the definition of users' interaction rules.

Challenge

Data ownership.

Rights on data and solution assets.

Licensing information,when available,is not based on a common framework.

Barriers

Concerns of various actors about legal risks.Licences on data, especially through data cycles.

Best practices

For the data marketplace ecosystem, the central actor has put a lot of control in the hands of data owners in order to gain the trust and confidence of suppliers and customers in the long term, especially through end user pricing and licensing.

UP42 has put a strong focus on defining clear licences, not only on the data but also on algorithms.

Related

EG01 –EG02 -TI08

ES07: Strong political and societal support facilitate the sustainability of the data ecosystem.

Challenge

Data ecosystems need strong political support in order to be sustainable. Moreover, data driven service creation and decision making through the ecosystem create public value, therefore provide solid arguments in support of the establishment of the ecosystem.

Barriers

Political agenda.

Data literacy.

Best practices

In the Disaster management ecosystem, political support is reported to be a prerequisite to break the data silos built around the various political and geographical responsibilities, ensuring aminimal data sharing between different agencies. Therefore, a public sponsor push for data sharing is important.On another side, inthe studiedlocal ecosystem, the digital strategy depends on the adopted regional/local development strategy.

Related

EG02 –EG07 –SE01 –ES05

ES08: Extracting the value of personal data.

Challenge

Personal data are difficult to gather but also valuable to create customized services. Technically, such data requires specific tools and processes based on personal consent to extract personal data stored by companies, while respecting the EU and national legislation.

Barriers

Legal framework, especially around personal data.

Technical feasibility.

Privacy.

Citizen reluctance for share their data.

Best practices

In RUDI, one of the objectives is to develop a personal consent module co-designed with citizens. It aims to empower the citizen through the management of their digital rights.

The same was done in the Smart Agriculture ecosystem, through the informed consent module.

Related

EG01 -TI04 -SE03 -ES04