Existing Literature on Identi fi cation and Infrastructure-Making

In this chapter, we build on the conception of infrastructures that was proposed by Star (1999) that sees infrastructures as dynamic organizing mechanisms that connect people and artefacts through time and space. In doing so, they offer new ways of collaborating and generating possibilities for action across organiza-tional and disciplinary boundaries. Infrastructures, thus,‘sort out’things (Bowker and Star 1999) and arrange knowledge by defining what counts and how to count (Kornberger et al. 2019).

This conceptualization of infrastructure also underpins the literature on infor-mation infrastructures that enable the exchanging, collecting, storing and man-agement of data (Star and Ruhleder 1996; Hanseth and Braa 2000; Ciborra and Hanseth 1998; Monteiro and Hanseth 1996; Hanseth and Braa 2000; Hanseth 2002). A specific type of information infrastructures is identification infrastruc-tures (IDIs) with the specific aim of establishing a recognized identification of a particular actor, such as—in this case—a legal entity, by associating them with a set of specific characteristics. These typically incorporate a unique identifier that is matched to a reference dataset and in doing so helps to establish a link between an entity’s long-lived temporal attributes and the various occasions and contexts in which the entity is involved (e.g. financial transactions with other entities) (Beynon-Davies 2016; Eriksson and Agerfalk 2010; Otjacques et al. 2007;

Whitley et al. 2014).

Existing approaches to IDIs pay attention to the components necessary for the IDI to be established and describe successful identification once it is achieved, but they do not address the dynamic interplay of interests associated with how identification-relevant information is collected, compiled, associated with reference data, and linked effectively with entities on an on-going basis (Millo et al. 2019).

Identification infrastructures also differ from other information infrastructures because of the degree of dependency an infrastructure participant and other infrastructures may have on the identification infrastructure. As a corollary, such dependency makes it difficult to put in place or change, once in place, elements of the infrastructure. We consider this characteristic of identification infrastructures through two related conceptual dimensions, which we term pivo-talityandlinkability. The degree of dependency of other information infrastruc-tures on IDIs gives IDIs—if adopted—both power and transparency (or invisibilityas per Star (1999)), but also creates difficulties and costs relating to its adoption and embedding into existing practices (Star 1999). Pivotality is important in the case of identification infrastructures as such infrastructures tend to have a high degree of pivotality because they play a crucial role in establishing relations between different sets of data that describe an entity and

which may have significant operational implications for the entity. This, in turn, has important implications in terms of the acceptance among users necessary for infrastructure to be established (Star 1999).

IDIs are also expensive–in terms of cost/effort—to replace (Whitley et al. 2014;

Eriksson and Agerfalk 2010) because they are vital for the day-to-day operation of the organizations that participate in such an infrastructure and hence cannot be replaced or changed without an operational‘backup’in place (Whitley et al. 2014).

The degree of pivotality of informational objects (Power 2015) that are part of an identification infrastructure such as a business identifier (e.g. name, address, and owner), is also dependent on the ubiquity (use across many infrastructures or in other informational objects) of the identifier, but also on the diversity of other informational items attached to it (e.g. reference data). Pivotal information items demand many organizational systems and practices also to change when the format or content of such items changes (Millo et al. 2019). For example, in a change in a unit of measurement (e.g. switching from Imperial weight and distance measures to Metric ones, or moving from national currencies to the Euro), much other information and its processing are affected/impacted by this change (Millo et al. 2019). As a result, pivotality is important firstly, in the establishment, and subsequent control and management of an IDI through network effects and irreversibility/path-dependency; secondly, as a point of resist-ance in the attitude of adopters towards a particular infrastructure relating to the costs of adoption. These costs may relate to the adapting of existing systems and practices to the new infrastructure and future lock-ins and dependences. The strategic value of the pivotality of an IDI is both in the irreversibility it can create and the implications this has in terms of lock-ins for infrastructure participants (Arthur 1989; Cantarelli et al. 2010; David 1985; Edwards et al. 2007; Ribes and Finholt 2009).

Because of the high pivotality and linkability of IDIs, the quality of the data being associated with the IDI is crucial (Millo et al. 2019).‘Identification’(Clarke 1994), therefore, is a complex process that results from the assembling of techno-logical, political, legislative, organizational as well as purely data-related factors which will be presented in more detail in this chapter.

Discussion around data-quality challenges in the existing literature is related to big data and analytics literature and in connection to the veracity of the data which can vary in nature and take different shapes or forms (Fox et al. 1994; Nagle et al. 2017; Park et al. 2012; Redman, 1995, 2013, 2016; Rubin and Lukoianova 2013; Zeng et al. 2010). Data veracity is defined as‘the level of reliability associated with certain types of data’including‘truthfulness, accuracy or precision, correct-ness’(Rubin and Lukoianova 2013, p. 7). Data Veracity, in turn, is related to the credibility assigned to the data, which is a function of the trust the data source has with regards to a potential (or a concrete) user of such data (Rubin and Lukoianova 2013). In the context of identification data, credibility also relates to

the perceived quality of the systems responsible for producing and maintaining the data. For example, an information infrastructure can suffer from semantic inconsistencies, lack of structure, conflicting evidence, multiple entries, and inac-curacies (Zeng et al. 2010). Many of these issues can be exacerbated when the data held by the infrastructure are user-generated. In such a context, ensuring high veracity of data sources can be a‘major challenge’(Zeng et al. 2010). It is clear that the credibility and usefulness of the information infrastructure largely depend on the quality of the data (i.e. garbage in/garbage out) and it will affect the value it provides to the entire ecosystem (see Chapter 2 for a description of the tax ecosystem) within which the infrastructure will operate. As we will see below, this is particularly important when infrastructure is highly embedded and linked to other infrastructures and practices.

Consequently, maintaining high veracity can be seen as a cost that needs to be accounted for when evaluating the effectiveness and overall benefits (e.g. return-on-investment) of the infrastructure. When treating data as a primary asset, one can also assess the benefits from other critical characteristics of data such as volume and variety and measure them against the cost for veracity. Overall, the importance of data quality in information infrastructures is paramount and can be associated with the success of data-related regulatory initiatives such as the one associated with the establishment of the GLEIS.

Underpinning the framing of identification infrastructures, we attempt above is a view of infrastructure not only as a nexus of material devices and affordances, but also as an achievement of an on-going alignment of the dynamic interests, incentives, and preferences of all relevant stakeholders (Millo et al. 2019).

Considering these points, we propose, as a guide for our examination of LEI, a definition of IDIs as‘a nexus of practices and on-going efforts made to establish these practices, which are aimed at enabling and framing the attribution of entities’identification through an association with other data’(Millo et al. 2019).

The emphasis in this definition is on the dynamics that surround the practices rather than only on the outcomes—the emergence and establishment of infra-structure. The infrastructure-building we examine in more detail below is an example of how the LEI identifier and associated identification protocols which incorporate a core of open-access data and design practices through which differential access to legitimacy, are performed.

There is onefinal dimension in information and identification infrastructures that has been neglected by extant studies and that we seek to incorporate into our analysis and which is how something intangible such as data gains value as part of an infrastructure and the significance of the definition and assessment of data quality in creating this value for the infrastructure.

Following the arguments above, we suggest that there are two crucial conditions for arriving at a working identification infrastructure: (1) having an agreement on

the selection of a single identifier over all possible others to be associated with identified data items that may change over time and (2) decide on a process to maintain the quality of the reference data held by the infrastructure and identify what elements of data-quality issues are more critical to address. Thus, the establishment of an infrastructure for reference data, such as identification data, calls for intermediated exertion of influence between two or more actors through the development of domain-wide rules that govern how references are to be associated with identification items and how high veracity of data can be ensured.

To examine this phenomenon, we focused on the specific actors who have sought to promote, develop and control the LEI IDI and how they went about trying to realize a role for themselves as gatekeepers to the arenas where identification data is trusted to be valid. The actions of such actors, we propose, are aimed at establishing control over how legitimacy is given to identification data and, as the infrastructure of such control is established, how such actors aim to position themselves as obligatory points of passage (Callon 1984; Latour 1987) among users of the infrastructure. We suggest that this control is attempted through a number of strategies, that have in common the fact that they are based on generating and maintaining a dynamic tension between the quality of data achieved and expected higher quality in the future.

That is, the designers and controllers of the IDIs promote the use of a universally applicable protocol of identification encoding but at the same time also add to the design measurable and validity-related qualifiers (primar-ily, measures related to data quality). Achieving a dynamic balance between standardization and variance, we propose, plays a critical role in calculating and coding identification information over which the promoters and designers of IDIs do not have direct ownership and control, but based on which they can make a legitimacy claim. This claim, in turn, will contribute to generating reputational and knowledge capital for the designers, the development of additional services and, ultimately, will contribute to the establishment of a positive network effect around the value of the IDI as a whole.

From the above review of the relevant literature, three key research questions emerge:

1. How are data made usable, useful, and ultimately capitalized when estab-lishing an identification infrastructure?

2. What implications does this have concerning the reconciliation of the different rationalities/logics that need to be resolved when establishing an identification infrastructure?

3. How does data capitalization relate to the establishment of a ‘Thinking Infrastructure’for the data-driven regulation offinancial markets?

9.4 Empirical Account