TECHNOLOGICAL FRAMEWORK

The success of a data-driven economy, which reuses data, is dependent upon technological factors and framework conditions. Ten key challenges and ten key enablers have been identified that are critical for the development of the data economy.

CHALLENGES

  • Different standards and languages

    Different types of ICT infrastructures and databases have been developed over time. Not-fixed standards regarding technologies and processes of big data resulted partially competing standards. This needs to be managed at EU level to ensure the free flow of data (the end of rooming fee was the first step). Additionally, making data available in unfavourable formats (e.g. PDF) risks that data cannot be combined and reused by others. The language diversity of Europe represents also a challenge.

  • Data verification and validation

    Data verification, which is performed on the user’s side, delivers methods for checking data for accuracy, inconsistencies and data loss. Verified data allows better results in data analytics and minimises the need for reconciliation of data (automatic correction). During data validation the original data is checked for correctness, meaningfulness, integrity, usefulness on the data holder side. A lack of these processes can lead to data corruption or to security threats such as Cross Site Scripting.

  • Stability of interfaces

    Publicly or privately available interfaces (APIs) allow t access to internal or external services/data. When APIs are updated in their methods, users of the service/data need to update their own software (clients) - which causes high costs  otherwise the  old API may be used only with less functionality or not at all. Backward compatibility and stability are critical issues, which might be achieved by guidelines and standards. It is recommended to always keep old interfaces available, despite costs.

  • Data lineage

    Typically, data gets transformed over time by different actors therefore there is a need for transparent information gathering and analysis of the transformation processes to avoid errors. If we do not have data lineage information, it cannot be retraced if the original datum is trustworthy and is corresponding with reality. Data lineage is part of the data curation process, which describes data management activities to assure long-term data quality over the whole lifecycle of data.

  • Technological gap between SMEs and big players

    The data economy is heterogenous. Big companies have large own data assets, financial, human and material capacities, which result strategic advantage to them while the slow business processes represent a disadvantage. SMEs on the contrary often lack in those resources and in the possibilities to cope with latest technology trends and can hardly access data of big players. Governmental financial subsidies and the wide spreading of cloud services might provide a solution.

  • Guarantee of the privacy and protection of data

    The lack of trust in big data technologies and data reuse is often linked to data protection and privacy related fears. Making all citizens aware of privacy and security rules would fill this gap but is not realistic. However, making companies that earn money with data-based services responsible for security and privacy might be a working solution. Despite national privacy and security laws and the high fines they foresee companies evaluate the risk of being defended low and take only few actions.

  • Data timeliness

    An important quality criteria of data is its timeliness (in the big data context: volatility). It describes the length of time until the data is available to users. If a data is outdated it can still be used for history analysis but not for decision making. Timeliness is affected by how fast the ICT updates the data after an event happened and in which interval it makes updates. Providing a “best before date” or a best before condition to a data set would help data users to assess the quality of the data.

  • Lack of acceptance

    Data is often gathered from consumers or companies that need to accept processes of data exploitation therefore transparency is a key factor for establishing a sustainable data economy. A high acceptance might lead to a massive volume of data with great potential for analysis. The acceptance can be seriously affected by incidents, such as data loss or security threats. Security, privacy as well as guidelines and a stable framework increase the level of acceptance.

  • Dependancy on third parties

    Using third-party services might lead to dependency. Dependency in terms of availability can be partly solved with SLAs, which describe a set of guarantees related to a service. SLAs in terms of data-driven services are not that common and need to be developed. Switching a vendor usually causes high switching costs for a customer, which might result vendor lock-ins. This might be critical in the data economy as very specific data might be offered only by few or even one holder.

KEY ENABLING FACTORS

  • Definition of standardized requirements

    A major objective of the European data economy should be to implement high quality services. This can be achieved by defining standardized requirements. For instance, creation of standardized security requirements could make the implementation easier for companies and would increase the level of trust in data reuse by the citizens.

  • Common standards

    Only if the same standards or unified datasets are utilized among several projects, it is possible that the data generated over years of research will be useful for new projects and could be reused. To avoid misinterpretation of good data, it is crucial to follow strict guidelines and best practices with respect to the collection and analysis of data as well as to follow ethical principles.

  • Quality certificates regarding data

    A certificate of quality of data issued by a recognised organisation might lead to user trust regarding the source, the platforms, the data suppliers, producst or services reusing data and the quality of the data itself. Many research organizations are audited on the basis of ISO standards relating to data management and security. It would be useful if the same pattern could be followed to certify a database.

  • Transparency

    When it comes to transparency, individuals and organizations need to know exactly what data is collected, how it is used and transformed, including the logic used in algorithms to determine assumptions and predictions as well as the purpose of research conducted. The technological description of the data processing is challenging but, if done properly, could lead to trust in the data economy.

  • Advanced security and privacy mechanisms

    The security sector has expanded quickly to the big data area. Technologies regarding security and techniques such as attribute-based encryption are improving and are mostly used to protect sensitive data and apply access controls. Tools that increase security while offering high usability would boost the European data economy in a technological way and would help increase the level of user acceptance.

  • Joint European ICT projects

    Much of the published data is only available in specific languages in the European Union, and since in Europe there are 24 officially recognized languages, it brings struggle with respect to the sharing and reusing of data. Translation tools could improve services and results based on data across Europe.

  • Full integration and resulting values of application, devices and services

    There are many benefits of the integration of data within an organization however it is a major concern of several companies as well as the public sector. Companies must adopt an open type of infrastructure to enable simple and rapid integration of new channels and technologies. For the European data economy, similar formats or technologies can result in better integration among different countries.

  • Decreasing the execution cost due to ICT progress

    With storage being more accessible (decreasing costs), the industry has moved progressively to business intelligence. Actors collected more and more data and with the advancement of high capacity servers and cloud computing, the analysis of data has become possible also for SMEs, start-ups and researchers. Incentives to companies to renew their infrastructure could result in a direct impact on the generation, analysis and usage of data.

  • Regulatory guarantee for high speed broadband in every EU country

    The regulation and straightforward guidelines are essential for raising the average speed and guaranteeing broadband connection across Europe.

  • Regulation of cloud computing and cloud storage services

    Some international standardization bodies are involved in the regulation of cloud computing/storing but the related processes are slow. E.g. The Cloud Security Alliance offers certification to cloud providers that meet specific criteria. In cloud computing one cloud service is offered to many customers at once. Certification, by independent auditors, against network and information security standards (like ISO 27001 certification), could be used by customers to fulfil their own compliance obligation.

DID YOU KNOW?

  • Agents are hindered in sharing data due to missing de-facto standards and technical diversity

    There are different types of ICT infrastructures, databases and interfaces that have been developed over time. Standards regarding technologies and processes of big data have not been fixed yet, which resulted partially competing standards. Concrete standards need to be clearer promoted at EU level to ensure the free flow of data (the end of rooming fee was the first step). In addition, making data available in unfavourable formats e.g. PDF risks that data can be combined and reused by others.

  • Agents tend to reduce or avoid own ICT infrastructures

    Cloud-storage and cloud computing services guarantee a certain service quality when service-level agreements are signed, which identify responsibilities, rights and obligations between the service provider and the user. In the last years, the service-based approaches like infrastructure as a service or software as a service have become more and more popular. This allows companies to consume external ICT infrastructure and reduce the need to create and maintain an own ICT infrastructure.

  • Agents tend to hesitate to fully rely on services provided by third parties

    Using third-party services might lead to dependency. Dependency in terms of availability can be partly solved with SLAs, which describe a set of guarantees related to a service. SLAs in terms of data-driven services are not that common and need to be developed. Switching a vendor usually causes high switching costs for a customer, which might result vendor lock-ins. This might be critical in the data economy as very specific data might be offered only by few or even one holder.

  • Agents tend to benefit from equivalency of transmission speed and Internet access structure all over Europe

    The high-speed broadband Internet network is a fundamental technical requirement of the data economy. The available average speed and guaranteed broadband connection in Europe depends on the national efforts which are influenced by regulations and straightforward guidelines of the EC. However, this issue seems to lose its importance the new fibre generation might make it relevant again.

  • Agents tend to benefit from a reduction in the technological gap between SMEs and large enterprises

    The data economy is heterogenous. Big companies distinguish themselves by having large financial, human and material capacities, which result strategic advantage to them. SMEs on the contrary often lack in those resources and in the possibilities to cope with latest technology trends. To compensate this beside the governmental financial subsidies the wide spreading of cloud services might provide a solution, which allow SMEs to use latest technologies without hiring expensive consultants or expert staff.

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CONTACT

Project coordinator

Mr Daniel Bachlechner

Fraunhofer ISI

E-mail: daniel.bachlechner@isi.fraunhofer.de

 

Communication leader and liaison manager

Ms Klara Süveges-Heilingbrunner

ICT Association of Hungary

E-mail: hklara@ivsz.hu

 

CONTACT

Project coordinator

Mr Daniel Bachlechner

Fraunhofer ISI

E-mail: daniel.bachlechner@isi.fraunhofer.de

 

Communication leader and liaison manager

Ms Klara Süveges-Heilingbrunner

ICT Association of Hungary

E-mail: hklara@ivsz.hu