ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018

DATA AS ECONOMIC GOODS: DEFINITIONS, PROPERTIES, CHALLENGES, ENABLING TECHNOLOGIES FOR FUTURE MARKETS

Yuri Demchenko, Wouter Los, Cees de Laat System and Networking Lab, University of Amsterdam

Abstract – The notion that data has value is commonly recognized. However, data value is different from that associated with consumable goods. There is a number of initiatives to create data markets and data exchange services. Existing business models of paid or commercial data (sets) services such as data archives are based on service subscription fees. However, emerging data-driven technologies and economies facilitate interest in making data a new economic value (data commoditization) and consequently identification of the new properties of data as economic goods. The following properties are leveraging FAIR data properties and defined as STREAM properties for industrial and commoditized data: sovereign, trusted, reusable, exchangeable, actionable, and measurable. Other properties to be considered and necessary for defining workable business and operational models are non-rival nature of data, data ownership, data quality, value, privacy, integrity, and provenance. The paper refers to other discussions and projects on defining data as consumable goods and market mechanisms that can be applied to data exchange, such as data markets, data exchange, and industrial data space.

Keywords – , big data infrastructure, data exchange, FAIR principles, data management, data markets, open data, STREAM data properties

revenue from big data technologies and services, 1. INTRODUCTION however, is small compared to the value that is expected to result in sectors such as trade, The emergence of a data-driven economy, powered manufacturing, finance and , public by big data and technologies [1, 2, administration, and health and social care that now 3], motivates research and exploitation of data have the tools at their disposal to make innovative market and data exchange models that can uses of data to drive high-value business and facilitate/enable effective data exchange as societal outcomes [4]. Unleashing the full potential economic goods and application-specific of data produced by the will integration while protecting personal data, data require both the creation of infrastructure to ownership and intellectual property rights (IPRs). facilitate data exchange and development of new market models and mechanisms. Companies, government bodies, academic institutions and citizens have access to, and are The fact that data has value is commonly recognized. increasingly using more, data today than could ever However, data value is different from that be imagined a decade ago. Traditional data sources associated with consumable goods. There is a such as company databases and applications are number of initiatives to create data markets and now complemented by a variety of open data and data exchange services. Existing business models of social media data or sensors embedded in IoT paid or commercial data (sets) services such as data devices, including mobile devices, smart meters, archives are based rather on service subscription cars and industrial machines. Economy fees than measurable properties of data. The quality digitalization and a growing volume of data has of data sets is in many cases assessed by an created an entirely new market of big data independent certification body or based on peer technologies and services to help organizations review by experts. Such models are useful for capture and extract value from all the data. The specific use cases, but they do not provide

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ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018 consistent models to make data an economic good impact of 2.5% on the GDP in the baseline scenario, and enable data commoditization (i.e. transforming which is defined by a continuation of the positive data into an object of trade and exchange [5]). but moderate growth trend of the economy [7]. Interest to make best use of research data has resulted in the formulation of the FAIR (Findable – A data-driven economy uses processes and Accessible – Interoperable – Reusable) data products/services digitalization, it collects and principles [6] which are fully accepted by the processes large amounts of data that include both international research community and widely personal and non-personal data. For personal data, supported by industry and business. However, the recently enacted GDPR mandates multiple emerging data-driven technologies and economies measures to protect personally identifiable data, in facilitate interests to making data a new economic particular informed consent [8]; this should build value (data commoditization) and consequently further confidence and trust in digital technologies identification of the new properties of data as and build trusted environments for data exchange economic goods. This paper leverages FAIR via the future data market [9]. In fact, research has principles to define the proposed STREAM data shown that people tend to share more data when properties for industrial and commoditized data: they trust the companies processing the data. The sovereign, trusted, reusable, exchangeable, data economy tends to involve the maximum actionable, and measurable. Other data properties possible amount of data that may be produced by important to enable workable/actionable business multiple sources, so creating a trusted environment and operational models include data ownership, for data exchange and trading is a key enabling data quality, data value, privacy, integrity, and factor for data markets and data-driven auditability. technologies. Data quality and veracity are important properties of data for data-driven This paper refers to past and ongoing discussions to applications. define data as economic goods and market mechanisms that can be applied to data, such as 2.1 Data markets as enabling mechanism for data markets, data exchange, and industrial data data economy space. The establishment of the Open Data Market as a The remainder of the paper is organized as follows. necessary stage in making a digital single market Section 2 analyses the emerging data-driven (DSM) in Europe a reality and working to facilitate economy and identifies data markets as important the digital transformation of the European economy driving forces. Section 3 discusses gaps and to embrace new trends related to Industry 4.0 [10] challenges on the adoption of data markets. Section and data-driven technologies. Data markets are 4 introduces the proposed STREAM data properties recognized as a priority topic in the Horizon 2020 and explain their importance in the context of data Work Programme 2018-2020 [11]. Currently, data markets. Section 5 analyses data market concepts exchange is limited to open data (including social and defines the main functional components and media data, city data, governmental data etc.) while mechanisms required to enable open data markets customer activity data, personal data, business and data exchange. Finally, the conclusion section operational data and industrial data that has a provides a summary and invites for future strong potential of bringing global optimization to discussion. multiple human/social activities and resources use, still remain in proprietary/private custody by 2. DATA ECONOMY AND DATA MARKETS companies. This brings unfair benefits to big network(ed) and technology companies that all The European data economy is growing at a fast tend to create their own full set of data that they pace and will continue to do so in the upcoming collect from their business and operational network. years. The overall value of the data economy grew Data markets [11] and data exchange [12] have the from €247 billion in 2013 to almost reaching €300 potential to bring benefit for facilitating business billion in 2016. According to the estimates of the activity, social creativity and technology European Data Market study, the value of the data advancement, but current infrastructure, data economy in 2016 was worth nearly 2% of the protection, legal conditions and regulations are not European GDP. By 2020, the data economy is expected to increase to €430 billion with an overall

ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018 ready to offer the necessary environments. There dominance by using exclusiveness of information are multiple initiatives and projects that have and data collection in a data-driven economy. collected valuable experiences, however, most of Besides the regulation measures suggested in [16], them are of an ad-hoc nature and involved a limited necessary data market and data exchange number of stakeholders and they are not based on mechanisms need to be created to allow data sufficient conceptual research, which in its own exchange at all stages of the data value chain from turn would require a significantly wider experience data production to data analysis and business and observational basis. application.

2.2 Data-driven economy and technology Few European projects address general issues of integration data protection, policies, and exchange. However, there is no common vision and widely accepted As described in recent studies and reports there are approach to create open data markets that would clear trends of all technologies’ digitalization and an benefit big and small companies. European Union increased demand of advanced/smart data (EU) and Member States, anticipating the DSM handling to enable the full potential of data-driven paradigm, invested significant resources in building technologies. This is characterized by wide an advanced digital infrastructure and regulatory technology integration based on modern big data basis for a digital economy in Europe. A recent infrastructures and technologies which are development of digital and data services is predominantly cloud based. primarily based on individual companies’ genius that discover and explore unique business Data processing and data analytics require opportunities. However, despite bright examples continuous data storage and exchange between such as , Twitter, Instagram and other applications and processes, which need to be social network and social media platforms that supported by a scalable distributed data storage actually demonstrated the potential of data-driven infrastructure, such as using a Hadoop Distributed and digital technologies, the full potential of the File System (HDFS) [13], Data Lake Storage [14], data-driven economy has not been realized yet. and facilitated by a special functional component Recent technology development is characterized by such as data exchange, which can be a part of the conceptual research lagging behind occasionally data market [12] in a general business scenario. generated business opportunities.

Currently, there is no common approach and Further development of data-driven technologies architectural model to address potential must combine conceptual research and cross-industry, cross-application and cross- development and implementation of economy-wide platform integration and exchange of data and data- technological platforms for data collection, storage, driven applications. Most current developments exchange and integration with industry, business and use of data in data analytics and data-driven and social applications. applications require and remain inside vertical data processing stacks (typically belonging to one 3. CHALLENGES IN DATA MARKETS company). The best examples of effective data use ADOPTION for business purposes and offering data analytics platforms for business as services are provided by Based on the analysis provided above, we can define big data infrastructure and cloud providers such as the general gap that creates a number of challenges Web Services (AWS), Microsoft Azure, for future data economy development and data Google Cloud Platform, IBM, Facebook. All these market adoption: Data is becoming an economic companies combine three key components of the good but there is no facility to unleash its full modern hub/networked digital economy [15]: (1) market potential. (big data) computation and storage infrastructure together with business (data analytics) services, (2) We can list some of the identified challenges that direct business services and marketplace, (3) motivated current research by the authors while wide/extended customer base and/or access actively developing the modern data infrastructure created around products or services. In recent for data-driven research and industry: research [16], these types of companies are defined as “superstars” that achieve their market

ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018

• Data property as an economic good and Factors that facilitate the need for data trading as commodity is not researched and not defined. economic goods include but not limited to: Data is more than oil of the future economy. • IoT sensor networks and farms that • There is no common vision and model of how continuously produce data that potentially to trade data while retaining data ownership may be used by different organizations and and sovereignty. produce secondary data that may have added • The new data market model needs to be value; developed and adopted. • use of personal data for advanced market • GDPR provides common rules but there is no research and services development; clear technology alignment to implement and • earth exploration data collected over years enforce GDPR requirements. New ePrivacy (such as from mining or oil/gas companies) legislation will make data management rules that can be also offered on the market; even stricter. • existing data archives whose value may • There is no (or limited) coordination and increase if data is traded in a more flexible interaction between industry and academia and measurable way; to develop new market mechanisms. • secondary data created from open data.

Technological aspects bring additional complexity Emerging data-driven technologies and economies to the topic of building data markets. facilitate interest in making data a new economic value (data commoditization) and consequently the Use of modern cloud-computing and big data identification of new properties of data as economic technologies and infrastructure is inevitable but goods. there is no well-developed security and trust model for storing and processing sensitive/proprietary The following properties are leveraging FAIR data data on cloud. principles and defined as STREAM properties for industrial and commoditized data: 4. DATA PROPERTIES AS ECONOMIC GOODS [S] Sovereign [T] Trusted This section provides suggestions about defining [R] Reusable data properties as economic goods and explains [E] Exchangeable their importance for enabling data markets and [A] Actionable facilitating data exchange. The section refers to [M] Measurable related works that created background in defining the proposed data properties. Other data properties that are important for enabling data commoditization and allowing data 4.1 From FAIR principles to STREAM trading and exchange for goods include: quality, properties value, auditability/traceability, branding, authenticity, as well as the original FAI(R) Initially proposed by the research community, the properties findability, accessibility, interoperability. FAIR (Findable, Accessible, Interoperable, Reusable) Special features that must be managed in all data data principles [6,19] found also strong support transfer and transformation are data ownership among industry and is currently included as a and IPR. The data property originated from its priority topic in the Horizon 2020 EOSC (European digital form of existence defined as not-Rivalry, on Open Science Cloud) Programme [20]. The FAIR one hand, makes data exchange (copying, principles are complied with and extend data distribution) easy, but on the other hand, creates governance and data management models at problems when protecting proprietary, private or enterprises, as defined in industry standards: the sensitive data or IPR. DAMA Data Management Body of Knowledge (DMBOK) [21] and CMMI Data Management Below we explain why these properties are Maturity model [22]. important for effective data trading and exchange between data market participants along the whole data value creation flow/process.

ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018 a) Data sovereignty d) Data exchangeability

Data sovereignty allows companies, data owners, to Data exchangeability ensures that data can be keep control over their data. It is important for exchanged between a data producer and data business to enter the data market with their consumer in general and be used for target proprietary business data and be confident that applications or intended purposes. To much extent, their data is not compromised or used by third this implies data format and platform and APIs parties, without consent from the data owner (or compatibility, which is achieved by industry data controller). Sovereignty is a key principle of the standardization. Ideally, data exchange should be Industrial Data Space Architecture as defined by possible between compatible processes or International Data Space Association [18]. applications during the whole data processing flow or data lifecycle. In the context of economic data Often the data sovereignty principle is opposed to value, exchangeability of data can also mean the general data storage and processing on clouds possibility of exchanging data for other economic where data resides in the cloud provider’s data goods or money. However, data pricing models are centers and there is fear that companies may lose not addressed by the authors at this stage, although control over their data, or the cloud provider may some references to related works are provided have unauthorized access to data or their use for below. business purposes. However, modern cloud and e) Data actionability infrastructure virtualization technologies, provider business models and compliance provide a When data is purchased by companies they should sufficient number of controls to satisfy security and serve their business purposes and contain the trust requirements by companies to operate their necessary information to derive actionable businesses and host data on clouds (see CSA decisions about operation or process optimization, Complete Cloud Security Governance, Risk, and in particular customer experience improvement or Compliance (GRC) Stack, Cloud Security Alliance quality of services delivered. When data is used in [23]). industrial processes, the actionable data must be b) Trusted data extracted and included in the industrial processes control. With increased use of Using data in decision making or in the processes in industry, the spectrum and variety of data used in control requires that data is trusted and verifiable. the industrial production value chain is increasing Trust in data is achieved by the whole process of and may include process monitoring data, logistics data collection and by using verified models of the data, market data and user feedback data. processes that data represent, which must be in f) Data measurability general auditable. In most business cases, data trustworthiness is ensured by the reputation of the Data measurability can be discussed at least in two data provider but all aspects of data production and aspects: as an important property for data valuation origin must be verifiable and auditable. and exchange as economic goods, and as a part of c) Data reusability data handling on the data infrastructure platforms. The former still requires additional research and Data reusability should allow multiple uses of data, effective data valuation models, yet to be created. even if it is not for the original purposes that the The latter is concerned with the resources that are data was created for. Normally, data represents required or consumed by the data storage or data events, entity or processes and are application processing facility or applications. This area is well agnostic. Data reusability can create multiple developed and supported by modern cloud based opportunities for data economy actors, including data infrastructure. Both public cloud platforms small and medium enterprises (SME) or individual (such as AWS or Microsoft Azure) and Open Source researchers. Data reusability should be supported platform (such as OpenStack or CloudStack) by well-defined metadata and well-documented provide a rich opportunity to monitor cloud data collection processes. Data reusability is part of resources usage by all data handling processes with the research data FAIR principles and is well details up to processor cycles, storage transactions supported by metadata management tools [19].

ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018 or network traffic volumes. Such information is • First Law: Information is (infinitely) essential for infrastructure resources planning and shareable costs estimation. • Second Law: The value of information increases with use 4.2 Combining data and algorithms • Third Law: Information is perishable With the complexity of modern data collected and • Fourth Law: The value of information generated by human activity and IoT, there is a need increases with accuracy to store/archive data with the corresponding • Fifth Law: The value of information increases application programming interfaces (API) and when combined with other information containerized applications that can handle the stored data. Stored data must contain metadata and • Sixth Law: More is not necessarily better schema as well as the blueprint for use and • Seventh Law: Information is not depletable deployment (ready for integration with the target applications). API management [22, 24] is an Two blog articles of 2013 [28] and 2014 [29] important part of data management in modern attempted to re-define the Moody&Walsh laws to data-driven companies; this includes both accessing Data Science [28] or apply them to information data from external providers/sources and emerging from IoT and sensors [29]. providing own data to customers and application developers. “Algorithm economy” term was popular Although the presented paper does not discuss data in 2016-2017 [25] as one of the development pricing and cost models, we mention two other directions of the emerging data-driven economy but papers by Muschalle, et al (2012) [30] and Heckman, now API management is a necessary part of the et al [31] that discuss pricing approaches for data modern enterprise data infrastructure. markets. Their research can provide initial input for a developing a data pricing model and how it can be 4.3 Other related concepts and models used in the data market operation.

Defining data properties as economic goods is a new 5. DEFINING OPEN DATA MARKET research area and requires a complex approach involving different research and technology Data markets are an important component of the domains. There are not many published researches data economy that could unleash the full potential on this topic. The authors’ research is motivated by of data generated by the digital economy and the need to consistently define requirements, human activity in general. Future data markets architecture and services that must be provided by should incorporate an open data market (ODM) the big data infrastructure and such infrastructure model that should allow multiple market components as data exchange and data markets. participants to join by complying with established rules and using standard APIs for data and Information and data value are admitted in many information exchange. This section briefly discusses general publications and research related to the big the ODM model and its functional components as a data value chain [26], emerging data-driven hub part of the modern data architecture and future economy [15] and macroeconomic model of big data (driven) economy. technology firms (referred to in the paper as “superstar”) [16]. However, these publications do The proposed ODM approach leverages the not discuss data as economic goods that can be international data space (IDS) architecture and exchanged or traded for money. functional model [17, 18] that are strongly based on the concepts of data sovereignty and infrastructure Few papers and blog articles explored information federation to ensure architecture openness. as an asset. The seminal paper “Measuring the value of information: An asset valuation approach” by 5.1 Modern data architecture Daniel Moody and Peter Walsh (1999) [27] formulated 7 Laws of Information, which we quote Modern data architecture is enabled by cloud and here to facilitate further discussion and research big data technologies and has the following (refer for details to the original paper [27]): characteristics:

ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018

• capable of handling big data V-properties: 5.3 Data market and data exchange volume, velocity, variety, and addressing data functionality groups variability, veracity, value [1, 3]; • cloud based, elastic; Data markets and their associated data exchanges include technological, trust management, legal, • customer-centric; commercial and operational aspects, as described • automated, smart; below. • adaptable, powered by Agile and DevOps 5.3.1 Technical infrastructure continuous improvement/deployment model [32]; Infrastructure is a necessary component of any • combining data and algorithms (as part of service architecture. The following are essential containerized applications and data); components of the data market infrastructure: • collaborative; • architecture and conceptual model of the data • governed; market space, including technological, organizational, legal and commercial aspects; • secure, trusted. • shared/federated infrastructure to access 5.2 Characteristics of emerging data markets and operate the data market; • federated hybrid cloud based big data Data markets should be able to support all major infrastructure to support data storage, properties of big Data from/for all application processing and exchange in a secure and domains, allow data exchange and integration at trusted way; different stages while preserving data provenance • DataHubs support for generic services for and auditability. Below is non-exhaustive list of data data suppliers such as caching, streaming, market properties leveraging big data architecture containerized delivery; and powered by cloud technologies: • support for on-demand connectivity and • supporting heterogeneous data exchange at bandwidth provisioning between data different processing stages; handling services/hosts in the data lifecycle; • cloud powered/integrated; • gateway based and computational • customer-centric; enforcement of market policies and rules. • automated, smart; 5.3.2 Rules for trust • regional/sectoral specialized; • collaborative; Rules for trust provide the basis for trusted relations between data market participants and • governed; reducing contractual and operational risk between • secure, trusted; participants. This includes the following rules, • auditable; policies, services: • transparent; • policy framework and platform bound mechanisms to participate in and cooperate • enabling data commoditization and operating with parties in the data market; with monetized data values. • models for agreements between parties in the The listed above properties should provide a data market and end users, with engineering necessary framework for definition of the open data for scalable (software) contracts and markets architecture, at the same time they will supporting architecture; require a better definition of data as economic • compliance assessment tools of the big data tradeable goods. infrastructure to enable trusted interaction between market actors;

ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018

• infrastructure and transactions auditing for • supporting data pricing models (current and performance and disputes. future) 5.3.3 Matching of parties (brokerage) • data visualization (properties, usage, quality) • security Brokerage and third party services are typical • compliance. components of traditional market exchange services, however it is not trivial to enable such 5.3.5 Data exchange protocol services over data exchanges. The following are essential brokerage services to enable effective and Data exchange protocols are important components trusted data exchange: of future data markets and data exchange • directory and registry services to enable infrastructure. Data exchange protocols can be findability of data and services, including provided as a part of the data exchange (as a data data providers, target data applications and market functional component) and use a layered typical data uses, conditions, etc.; model benefiting from reliable and secure modern protocols, while deploying specific data • data filtering and quality assessment exchange protocols as upper layer protocols. services, data processing API, value added Modern network and cloud virtualization service/ product offerings; technologies allow the building of program able • automated/assisted contracts negotiation virtualized networks that can be provided as part of and data “value/property” virtual private clouds (VPC) and support data transfer/exchange, customer support. applications that produce and consume data and involve data exchange internally inside secure VPC 5.3.4 Data catalogues to keep everything and with external parties. together Recent developments in data descriptions and data A data catalogue (or Directory) is an essential management introduced a number of mechanisms component of the intended data market and data that can be used for effective and consistent data exchange infrastructure that can be distributed and exchange protocols and applications: persistent operated in centralized hierarchical and federated data identifiers (PID), data factories, metadata and modes. Catalogue service should enable both data types registry, data annotation and data hosting metadata information and discovery mechanisms, all these are part of the historical/provenance information about data sets Research Data Alliance (RDA) outputs and and data transformation. The following summarizes recommendations [33]. the data catalogue properties and services: • cataloguing data sets 6. CONCLUSION • cataloguing data operations The presented paper is intended to provide a basis • metadata catalogue for further wider discussion on defining data • searching properties as economic goods and building future • data curation open data market and data exchange infrastructure by involving concerned communities, in particular • data quality assessment and data those that the authors are actively involved with. categorization These are Research Data Alliance, Industrial Data • linking data properties and applications Space, European Open Science Cloud, and National • recommendations and relationships Institute of Standards and Technologies (NIST) Big Data Working Group. The proposed definition of the • data sets evaluation STREAM data properties is based on extensive • data access policies and API study of related works and authors’ experience in • usage metadata building modern big data infrastructure and cloud • lineage/provenance based services. • integration and interoperability

ITU Journal: ICT Discoveries, Special Issue No. 2, 23 Nov. 2018

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