9 - The International Organization for Migration as a Data Entrepreneur

Summary

Collecting and processing large volumes of both personal and non-personal data on migrants and displaced populations is an integral part of IOM’s work. Through the ongoing expansion of its primary data collection mechanism, the Displacement Tracking Matrix (DTM), the organization has come to be the most widely-used and authoritative source of data on internal displacement in particular. This chapter outlines how IOM has positioned itself in an increasingly competitive market of migration and displacement data and discusses the normative obligations that come with this. An analysis of the use of DTM in Haiti and along migratory routes in West and Central Africa illustrates that apart from its key role in the humanitarian planning cycle, one important political function of the DTM has been to showcase the success of donors’ policy interventions. This goes hand in hand with a number of risks and pathologies like the statistical “erasure” of populations with enduring needs and the crowding out of more development-oriented data collectors that raise questions with regard to IOM’s adherence to the principle of data responsibility. Against this background, the chapter calls for a change in institutional set-up and funding structure to ensure that IOM engages in responsible data collection only.

9.1 Introduction

Migration and displacement data are a growth sector. Apart from governments hoping to better understand, forecast and control human mobility, a number of global processes, including the Global Compact on Migration and the UN High Level Panel on Internal Displacement, have called for more and better data on human mobility. Throughout its history, the work of the International Organization for Migration (IOM) has entailed collecting and processing large volumes of both personal and non-personal data on migrants and displaced populations. In line with growing demand by donors, this engagement in data collection and analysis has over the course of the past two decades shifted from a by-product of the organization’s operational work to a key service offered to governments and humanitarian actors, and has been complemented by an increasingly active role in data dissemination, communication and visualization. While this at times includes the collection and processing of biometric data – alongside the stated objective to strengthen its use in programming¹ – the vast majority of data currently collected by IOM is statistical information about the number and key characteristics of people moving, and the routes used.

The processing of data on migrants and displaced persons is often presented as a purely technical exercise aimed at improving the evidence base for subsequent projects and policy decisions. This chapter questions this largely apolitical view of (migration and displacement) data and instead considers the collection, analysis, application and communication of data an inherently normative process with far-reaching political implications. Through an in-depth analysis of the Displacement Tracking Matrix (DTM), IOM’s primary data collection mechanism, it recounts how the organization came to be the most widely used and authoritative source of data on internal displacement. Drawing on the concept of data responsibility, defined by OCHA as ‘the safe, ethical and effective management of personal and non-personal data for operational response’,² the chapter sets out the obligations arising from this role, and questions whether in its current set-up, the organization is fit to meet them.³

Data responsibility subsumes, but goes beyond the protection of, personal data. For example, data responsibility in humanitarian contexts may be understood to require (1) data protection, (2) legality and legitimacy (data processing in accordance with applicable laws, as well as with core values of the respective organization), (3) doing no harm, (4) respect for the rights of data subjects (including access to, rectification and erasure of data), (5) purpose specification, (6) data minimization (collection on the basis of necessity and proportionality) and (7) data quality (accuracy, validity, reliability and being up to date).⁴ The adequate protection of personal data constitutes a continuous challenge for any organization engaged in humanitarian work. IOM acknowledges the existing international obligations in this field and is engaged in ongoing efforts to meet them.⁵ Arguably more pressing, therefore, are the not fully acknowledged ethical obligations arising from the potentially sensitive nature of the vast amounts of non-personal but demographically identifiable data collected through the DTM, that is, data on issues such as displacement rates, returns and the number of people resident in particular camps that cannot be traced back to any individuals but are characterized by group-specific markers, for example, ethnic identity.⁶ Beyond this, there are concerns about the quality of DTM data, about potential negative implications of the DTM’s expansion beyond the field of humanitarian needs assessments and about its responsiveness to donor demands for data that serve as a post hoc legitimization of policy interventions.

Adopting a political economy lens that foregrounds IOM’s role as a participant in a dynamic and highly competitive market of data providers – that can be described as the humanitarian data economy – helps to make sense of these developments. While the DTM serves an important function in humanitarian needs assessments, it has also been shaped by the entrepreneurial spirit of IOM as a whole.⁷ That is, IOM ‘sells’ the DTM to its donors, adapting it where necessary, and uses the data generated through the DTM to justify other projects that IOM then pitches to donors for funding. The demand-driven and service-oriented nature of the DTM may create tensions with data responsibility principles, including purpose specification, data minimization and ‘do no harm’. Any benefits offered by the DTM in terms of providing rapid overviews of IDP situations and providing the basis for advocacy therefore need to be viewed in conjunction with the tool’s limitations.

Based on these observations, the chapter argues that in order to ensure that IOM’s data activities are in line with the notion of data responsibility and produce the kind of data required for both evidence-based and rights-oriented decision making, a clarification and strengthening of related standards does not suffice. What is needed instead is a fundamental change in institutional set-up and funding structure that allows IOM to only engage in responsible data collection, free from a profit-driven market logic.

The chapter is structured as follows: It starts out with a brief review of the literature that speaks to IOM’s engagement in migration and displacement data, and the obligations arising from it. Next, it sets out the idea of a market for migration and displacement data within which IOM competes with other data providers for financial resources and reputational gains. It then offers a thick description of the DTM’s history, institutional set-up and output, including a discussion of the quality of data produced, before zooming in on two DTM country operations (Haiti and Niger) in order to illustrate the value of certain types of mobility data have for political actors. In conclusion, it juxtaposes the risks associated with IOM’s near-monopolization of internal displacement data, as well as with the expansion of DTM operations into the field of cross-border mobility with IOM’s data protection standards, to highlight the organization’s shortcomings with regard to the responsible conduct of its data work. The focus on the DTM and the data produced through it means that the chapter’s analysis is necessarily selective, and does not cover all of IOM’s data-related activities. This focus is justified by the fact that questions of political and ethical accountability that the contributions to this edited volume speak to are particularly pertinent in situations of forced displacement that the DTM focuses on.

The argument developed in this chapter draws on publicly available information on IOM’s data-related activities, as well as on a series of interviews conducted between September 2020 and March 2021 with 20 current and former IOM officials, representatives of other international organizations and non-governmental organizations and investigative journalists who regularly engage with DTM data in their work. These interviews were structured around three core themes: The history, structure and working mechanisms of the DTM, the function and value of the DTM within IOM overall and the relationships and interactions of different actors engaged in the collection and analysis of data on internal displacement and irregular cross-border movements. In order to allow for a frank discussion of sensitive institutional concerns, all interviews were conducted anonymously.

9.2 From the ‘Datafication of Migration’ to the Need for Data Responsibility in Migration and Displacement

The increased importance of quantitative data and related evaluation systems like benchmarks and indicators has given rise to much debate across all sectors.⁸ Migration and displacement are no exceptions. In the context of this chapter, three different (albeit interlinked) strands of literature are of particular relevance. The first discusses the ‘marketization of migration statistics’⁹ and its consequences; the second delves into the risks associated with the increasingly data-driven nature of humanitarian work; and the third concerns developments regarding data protection by international organizations broadly, and humanitarian actors more narrowly. This scholarship does not engage with IOM in any detail, yet the concerns raised are relevant to the organization’s work, in particular in light of the expanding nature of DTM data collection.

First, a growing literature starts from the assumption that ‘the accumulation of data is a core component of political economy in the twenty-first century’, and that this frequently takes the form of data extraction, ‘with little regard for consent and compensation’.¹⁰ This speaks directly to the notion of a ‘migration knowledge hype’,¹¹ and to Taylor and Meissner’s observation of an increasing marketization of migration statistics. One of their central insights is the co-constitutive relationship between the perception of migration as a threat, and the demand for more data on human mobility: ‘an existing policy vision creates a market for technologies which then shape the world to fit that policy vision and make its enforcement possible. This dynamic is particular to the involvement of commercial actors: where policy interacts with the data analytics market, a field is created for firms to compete for contracts based on how closely they can adapt and develop analytical techniques to policy objectives.’¹² The market dynamics described here arguably do not only apply to corporations, but also to international organizations like IOM that due to its projectized structure is constantly competing for funding. While the specific incentives to engage in this competition differ between IOs and commercial enterprises (e.g. IO staff are not paid bonuses based on performance), professional advancement is typically linked to being able to ‘sell’ projects to donors, which is facilitated through readily available data.

Second, the increasing pervasiveness of quantitative data in humanitarian work has given rise to a body of literature that – in response to an ‘avalanche of ‘tech-optimistic’ scholarly work, premised on the belief that adding technology will change things [in the humanitarian sector] for the better’¹³ – focuses on the limitations and the risks associated with this development.¹⁴ More specifically, contributors call into question the hoped-for benefits of the ‘data revolution’ for the humanitarian field, given the mismatch between the vast amount of data collected and the limited capacities for analysing it;¹⁵ point out the risk of big data and artificial intelligence (AI)-driven humanitarian work reproducing existing power asymmetries and relationships of dependency;¹⁶ and highlight privacy and data protection concerns related to the collection of sensitive data.¹⁷ Again, IOM – not neatly fitting into the humanitarian category – is not specifically discussed, yet due to the primary humanitarian purpose of much of the data produced by the DTM, these insights are of direct relevance to the organization’s work.

Third, and most directly linked to the themes of this volume, there is a nascent literature on the legal and ethical obligations linked to the data-related activities of international organizations. Kuner discusses to what extent the EU’s 2018 General Data Protection Regulation (GDPR), which sets new standards of data privacy and security, applies to international organizations engaged in the processing of personal data.¹⁸ While Kuner comes to the conclusion that the legal situation is ‘murky’, and that ‘there is considerable uncertainty about the extent to which IOs should implement the GDPR’, he argues that European donors could prod IOs into compliance by making it a funding prerequisite. Against this background, he recommends that IOs use the GDPR as a ‘source of inspiration’ to put into place adequate internal data protection principles.¹⁹ It is worth noting, however, that the GDPR only covers personal data. Over the past five years, there has been an important conceptual shift in discussions about humanitarian actors’ data protection responsibilities, broadening it beyond the ‘personally identifiable information’ that has traditionally been of central concern to include ‘demographically identifiable information’.²⁰ The notion that data protection obligations are not limited to individuals but also refer to vulnerable groups leads to the conclusion that humanitarian data collection and utilization ‘needs to follow the principle of proportionality and consider benefits and harms beyond individual interests’.²¹ These developments are of direct relevance to IOM – indeed, some of the related warnings regarding the risks entailed by ‘organizations tracking time and place-specific movement/status data of large demographically delineated groups’²² sound as though they were formulated with the DTM in mind.

These concerns have prompted some limited reforms in the humanitarian sector, with the development of various guidance on data responsibility. One key document that has become a common point of reference is the 510 Data Responsibility Policy initiated by the Netherlands Red Cross Society. It sets out the key argument that data responsibility encompasses ethical principles that go beyond compliance with GDPR data protection requirements, especially in terms of ‘doing no harm and respecting each individual’s fundamental right to privacy and to control the use and processing of his or her own personal data, bearing in mind the consequences that the use of data could have on vulnerable people around the world and taking measures to avoid putting individuals or communities at risk’.²³ The debate about data responsibility in humanitarian settings is ongoing in various fora, some of which IOM is actively involved in. However, these conversations do not generally cover data-related work carried out outside the context of humanitarian emergencies.

9.3 IOM and the Market for Migration and Displacement Data

As noted above, there is immense international demand for data on migration and displacement that is linked to broader developments in the development and humanitarian sectors. The call in the 2013 UN High Level Panel on the Post-2015 Development Agenda for a ‘data revolution’, and a subsequent report dedicated to mobilizing this data revolution for sustainable development are indicative of the increasing prioritization of data in development programming.²⁴ The equivalent for the humanitarian sector came with the international community’s commitment to improve the evidence base of humanitarian response operations under the 2016 Grand Bargain.²⁵

These developments, and the hopes for greater efficiency and cost effectiveness motivating them, are reflected in a number of recent global processes that have increased the demand for data on human mobility more specifically. The global indicator framework developed to measure progress towards the Sustainable Development Goals (SDGs) is preceded by a passage calling for the disaggregation of SDG indicators, where relevant, by migratory status.²⁶ Remarkably, the first of the 23 core objectives of the Global Compact for Safe, Orderly and Regular Migration (GCM) contains the commitment to ‘strengthen the global evidence base on international migration by improving and investing in the collection, analysis and dissemination of accurate, reliable, comparable data’.²⁷ In the Global Compact for Refugees (GCR), data and evidence feature as one of three ‘key tools for effecting burden- and responsibility-sharing’.²⁸ Most recently, the UN High Level Panel on Internal Displacement highlighted the relevance of more and better data on internal displacement, recommending inter alia that international donors should increase their funding efforts in this field.²⁹

Given that the majority of large donor countries have supported the development of these frameworks and are committed to implementing them, the last few years have seen a significant increase in the volume of funds available for the collection and analysis of migration and displacement-related data. This has had significant effects on the institutional landscape, visible both in the expansion of existing data initiatives by international actors and in the emergence of new ones.

IOM is a case in point. In line with the breadth of its overall portfolio, the organization collects various types of migration-related data, for example, related to pre-departure health assessments, interlinkages between environmental change and human mobility, and migrant deaths and disappearances. The organization aims to be ‘a primary reference point for migration information, research, best practices, data collection, compatibility and sharing’.³⁰ Its Global Migration Data Analysis Centre (GMDAC) in Berlin was founded in 2015, replacing the organization’s former Geneva-based Migration Research Division.³¹ It has the threefold aim to ‘(1) Strengthen the role of data in global migration governance […], (2) Support IOM Member States’ capacities to collect, analyse and use migration data, [and] (3) Promote evidence-based policies by compiling, sharing and analysing IOM and other sources of data’.³² The centre’s ongoing expansion – from a modest start with less than four staff members in 2015 to 45 in May 2022 – is evidence of its success. While GMDAC – with its ambition to serve as a one-stop-shop for all available migration data through its Migration Data Portal – serves as an institutional focal point for IOM’s engagement in data analysis and communication, IOM’s primary dedicated data-collection mechanism, the Displacement Tracking Matrix (DTM), has so far been institutionally separate. While both GMDAC and the DTM are crucial to IOM’s efforts to secure a leadership role in the field of migration data,³³ it is arguably the DTM that creates considerable revenue, both in and of itself, and in terms of providing the evidence base for further interventions that IOM may propose to donors.³⁴

The overall increase of interest in migration and displacement data, however, goes hand in hand with increased competition, especially in the humanitarian field, and where the humanitarian and development sectors meet. Dedicated data collection initiatives whose work overlaps with that of the DTM include REACH, the Mixed Migration Centre (MMC) and the Joint IDP Profiling Service (JIPS). REACH, a humanitarian data collection initiative, collects data on crisis-affected populations (many of whom are internally displaced persons (IDPs), creating a significant overlap with the DTM) and plays a crucial role in informing UNHCR’s crisis response. Established in 2010, it has seen a massive expansion over the course of the last five to seven years. The Mixed Migration Centre (MMC), established by the Danish Refugee Council in 2018, is a data collection initiative aimed at improving the evidence base on cross-border movements by a diverse set of people including refugees, victims of trafficking and individuals primarily searching for opportunities not available to them in their places of origin. The MMC conducts thousands of in-depth interviews with people on the move along key migration routes in seven distinct regions, responding to increased donor interest in understanding migrants’ routes and aspirations, especially on their way towards Europe. The MMC has grown considerably over the course of the last three years, its data feeding into the work of various UN agencies like UNHCR, UNODC and UNFPA. Another relevant actor engaged in data collection on internal displacement, JIPS, is an inter-agency body founded in 2009 that is administered jointly by UNHCR and the Danish Refugee Council. It conducts targeted profiling exercises with IDPs and host communities in individual localities to inform the development of durable solutions. While the data collection activities undertaken by these three actors do not compare to the DTM in size and coverage, all three offer valued and distinct services to actors engaged in displacement scenarios.

Beyond these individual organizations, new collaborative initiatives have sprung up in response to donor demand for improved data interoperability and joint assessments. The World Bank-UNHCR Joint Data Center on Forced Displacement (JDC) and OCHA’s Centre for Humanitarian Data are the most prominent examples. The work of all of these actors overlaps, intersects and feeds into each other. The fierce sense of competition that runs through these interactions is conveyed by remarks by IOM staff members that some of their smaller NGO competitors are ‘claiming more and more space’, and that management at JDC ‘are loading their guns, hiring all the right people’.³⁵ In 2020, IOM published a migration data strategy and in 2021 an internal displacement data strategy. Both of these documents acknowledge the broad array of actors involved, while at the same time claiming a leadership role for IOM.³⁶

9.4 The Displacement Tracking Matrix

The DTM – variously described by IOM itself as a data-collection mechanism,³⁷ a monitoring tool,³⁸ an information management tool³⁹ and a system enabling the development and maintenance of baseline information on displaced populations⁴⁰ – is a highly decentralized system for tracking and monitoring internal displacement and (frequently irregular) cross-border mobility. The DTM toolbox consists of four key components – mobility tracking, flow monitoring, surveys and registration – that can be flexibly combined to fit a given country context. The respective role of these four components can be broadly characterized as follows: Mobility tracking operations follow a distinct group of persons, capturing basic demographic characteristics as well as vulnerabilities and priority needs, tracking their movements. Flow monitoring, in contrast, focuses on fixed geographical locations and aims to capture data on the various mobile populations crossing that point. Using direct observation by DTM staff and key informant interviews, both initially only collect non-personal data, but can be further substantiated through surveys that may contain personal data. Registration – which entails the collection of personal data – is a service largely distinct from the other three components and is only undertaken at the explicit request of governments.

In its public presentation of the DTM, IOM emphasizes the tool’s modular set-up, and the fact that it can be adapted to widely varying circumstances, including ‘conflict, natural disaster, and complex emergency settings, from small to large cases of displacement’.⁴¹ Its target population is broad, encompassing conflict- and disaster-induced IDPs, returnees and migrants. The DTM expansion over the past decade has been rapid: While in 2010 it was deployed in ten countries, this had grown to over 40 in 2016⁴² and to 88 in 2020.⁴³ By 2020, these operations enlisted the help of approximately 6,600 staff around the globe, most of these local data collectors.⁴⁴ The DTM written output increased at pace, with a steady year-by-year increase – from one report in 2010 to 2209 in 2020. The DTM website today is a vast repository of data from past operations, currently storing more than 9100 individual documents in various formats – among these, dashboards, situation reports and maps.⁴⁵

9.4.1 Origins and Evolution

How did this vast data collection exercise come about? Despite IOM’s long-standing interest in IDP profiling, its Iraq operation in the early 2000s is widely considered the starting point of a methodology for rapid assessments of the movements and needs of IDPs carried out by field-monitoring teams – the initial core business of the DTM that now features under the label ‘mobility tracking’.⁴⁶ With the establishment of the humanitarian cluster approach in 2005 that accorded IOM the co-lead of the Global Camp Coordination and Camp Management (CCCM) cluster, IOM became increasingly engaged in IDP registrations in camp settings – a second core module of the contemporary DTM.⁴⁷ Data collection on IDPs in camp settings constituted a key element of IOM’s activities in Haiti following the 2010 earthquake, and it was in this context that the umbrella term ‘Displacement Tracking Matrix’ for displacement-related data collection exercises in various country contexts was coined.⁴⁸

‘Flow monitoring’ was initially developed to capture distinct displacement situations, such as the movements triggered by the military coup d’état in Mali in 2012,⁴⁹ the 2014–2016 Ebola epidemic in West Africa,⁵⁰ and those from the Dominican Republic into Haiti following legislative changes that threatened Haitian immigrants and Dominicans of Haitian descent with deportation.⁵¹ However, its usage significantly changed in the context of the so-called European refugee crisis, which led to a sudden and urgent demand for data on population movements towards the European Union (EU): The number of DTM reports featuring a flow monitoring component jumped from 25 in 2015 (19 of which were dedicated to the situation in Haiti) to 182 in 2016, the vast majority of which reported on movements towards EU territory.⁵² In this sense, flow monitoring has over time become almost synonymous with the expansion of DTM operations from internal to cross-border movements, and from displacement scenarios to broader migratory dynamics – reflecting, in the words of an IOM staff member, an ‘immense thirst for flow data on the part of donors’.⁵³ Surveys were added as a fourth component in 2013, initially to gain a better understanding of return intentions among displaced communities in Mali and the Central African Republic.

Over time, various sub-categories were added to the four key components, with more recent additions including biometric registration, community perception surveys and village assessments as a type of mobility tracking. In general, surveys are aimed at complementing baseline assessments through data on the socio-economic profiles of migrants, their means of travel and their intentions and expectations. Despite its displacement-focused name, the DTM is now deployed in a vast variety of mobility settings, and in individual countries records all types of movements, including in the context of tourism, family visits and seasonal nomadic mobility.⁵⁴ The operation launched in The Gambia in 2021 is an example of the DTM being deployed with the broadly stated aim to improve migration governance.⁵⁵

This brief reconstruction of the DTM’s evolution over time indicates piecemeal and demand-driven growth. Current and former IOM staff members complain about constant ad hoc expansions (‘running after money and fashions’) at the expense of a consolidation and standardization of data collection methods, and about sudden shifts in priorities. A recent example of this is the mapping of Covid-19-related travel restrictions around the world that the DTM management initiated in March 2020.⁵⁶ The resource-intensive daily updates this required reportedly led to a postponement of a planned revision of the flow monitoring methodology.⁵⁷ Meanwhile, the steady stream of funding from donors attests to the business-savvy character of the DTM management.

9.4.2 Institutional Set-Up and Funding

Despite its primary identity as a ‘tool’, the DTM can also be considered an institutional entity in its own right. Its organizational home is IOM’s Department of Operations and Emergencies, where a core ‘global DTM support team’ comprised of – at the time of writing – 45 technical experts across eight locations (Geneva, London, Bangkok, Nairobi, Dakar, Cairo, Vienna and The Hague) is engaged in data management and operations support.⁵⁸ Its overall staff structure in 2019 included 438 technical staff and 6,170 data collectors.⁵⁹ However, much of the DTM’s institutional set-up remains opaque: There is no publicly available organizational chart, despite the fact that DTM vacancy notices regularly refer to up to twelve organizational subunits.⁶⁰ The large share of internships among the DTM positions advertised online support the account that the DTM core team is thinly staffed and relies heavily on support from interns for substantive input.⁶¹ Against this background – and in line with the overall decentralized structure of IOM – DTM country coordinators enjoy a large degree of autonomy. At the same time, DTM field positions are hard to fill with qualified statisticians and data experts. As a result, the quality of data differs vastly between DTM country operations.⁶² Emphasizing this point, former IOM staff noted that ‘DTM is unrecognizable from one country context to the next’ and can be considered ‘more of a brand than a cohesive methodology’.⁶³

While the DTM is considered a ‘money-making machine’⁶⁴ within IOM, it is difficult to gain an understanding of the volume of funding the DTM attracts. IOM’s annual financial reports typically contain a number of DTM-specific entries, yet due to variations in the terminology used to report on these activities, no clear picture emerges of the amounts and sources of money involved.⁶⁵ The vast majority of DTM country operations seem to be subsumed under more overarching categories like ‘strengthening service coordination’ or ‘supporting a coordinated response’. Looking into individual country appeals affords slightly more insights: The 2018 and 2019 IOM’s crisis funding appeals for Iraq, for instance, calculated a need of three million USD per year for the implementation of the DTM across the entire country. This amounted to a share of 11.2% of the entire appeal for the year 2018, and 7.2% for the year 2019.⁶⁶ The IOM flash appeal following the August 2021 earthquake in Haiti calculated that one million USD was required for the DTM operation, amounting to 6.7% of the entire funding needed. Even though these appeals are not always fully met, it is clear that the DTM is a major source of revenue for IOM, particularly as DTM data are used to propose and justify further IOM projects.

DTM funding sources differ depending on the type of operation. DTM operations that are part of larger humanitarian interventions like those in Sudan, South Sudan and Libya, tend to be financed through a broad range of mechanisms, including UN funding mechanisms like Central Emergency Response Fund (CERF) or the UN Peacebuildig Fund, as well as by large bilateral and multilateral donors, for example, through USAID, German Humanitarian Assistance or the EU’s humanitarian office ECHO.⁶⁷ DTM operations that are primarily aimed at collecting data on migratory movements, often towards the European Union, tend to attract funds from actors fearing the arrival of migrants on their own territory: DTM Libya is financed through the EU Trust Fund for Africa (EUTF),⁶⁸ DTM Niger has since its inception in early 2016 been funded by the foreign ministries of the UK, Germany and Denmark as well as through the EUTF,⁶⁹ and the donors of the DTM operation launched in March 2021 aimed at collecting data on ‘migrant presence outside temporary reception centres in Bosnia and Herzegovina’ are the EU, Italy and the Czech Republic.⁷⁰

9.4.3 Data Collection and Data Quality

The on-the-ground data collection undertaken in the context of mobility tracking and flow monitoring – which together make up the vast majority of DTM operations and produce large amounts of non-personal yet demographically identifiable data – has at its core one key method: key informant interviews.⁷¹ While IOM is increasingly making forays into the use of advanced data collection technologies like high-resolution satellite imagery,⁷² the ‘coca cola recipe’ of DTM operations is the rapid roll-out of a large network of key informants even in remote locations in the DRC and Northern Nigeria, and in acute crisis settings like Libya and Syria.⁷³ Key informants – 166,379 of whom were involved in DTM operations in 2019 alone⁷⁴ – are typically community leaders, religious leaders, local government officials, humanitarian aid workers or others who have a good insight into mobility patterns or displacement situations in a particular local setting. Local data collectors, so-called ‘enumerators’, are recruited and trained in data collection methodologies relevant to the specific context. Enumerators then conduct regular rounds of structured interviews – on location or via telephone – with key informants, electronically recording information, for example on the number, location, demographic make-up, humanitarian situation and needs of displaced persons in humanitarian settings.⁷⁵ The DTM methodology then foresees a stage of validation, for example through assessing the consistency between the information provided by different key informants.⁷⁶

While the use of local enumerators conducting interviews with key informants is popular among many international aid organizations,⁷⁷ IOM has a competitive advantage in terms of rolling out large data collection exercises within a short period of time due to its continuous field presence, existing networks and physical equipment (e.g. adequate vehicles necessary to reach remote locations) in most regions of the world. However, the key informant methodology comes with clear limitations: data collected in this manner is by definition proximate, and there are many concerns about data accuracy. Typical problems include ill-defined units of observation that may lead to double-counting, such as when key informants are assigned to adjacent neighbourhoods and may record several times the people moving between them.⁷⁸ In addition, interview respondents reported a lack of verification mechanisms, especially in settings where information is gathered remotely and cannot be validated through direct observation by trained enumerators (e.g. during times when the security situation in Libya prohibited access),⁷⁹ and instances of long-standing migrant communities being counted as recent displacements due to a lack of historical awareness within data teams working in certain country contexts (e.g. Palestinians and Syrians in Lebanon and Rohingyas in Bangladesh).⁸⁰

Once the data is collected, it can be compromised by political imperatives – in many countries, DTM data has to be cleared at various levels of the respective host government’s hierarchy before it is publicly released, increasing the risk for distortion.⁸¹ Such an incident allegedly occurred in Nigeria, where the DTM team allegedly bowed to pressure from the Nigerian government that wanted to show progress in returns, and changed its methodology so as to record temporary returns between multiple displacements as returns ‘proper’.⁸² In other country contexts, DTM data largely amounts to a compilation of government figures with little or no verification.⁸³

Over and above any shortcomings in individual DTM country operations, there is widespread concern – both within and outside IOM – over a lack of technical expertise within the DTM management, and the persistent prioritization of quick results over investments in standardization that would be necessary for improved data quality.⁸⁴ This manifests in inconsistent methodologies for data collection, analysis and validation between countries.⁸⁵

Regardless of these various limitations, the data presented in DTM reports has a level of specificity that belies the fact that it is based on estimates: a figure like the 662,248 migrants recorded by a flow monitoring exercise in Libya in March 2018 gives the impression of being based on an exact head-count of individuals in particular contexts, despite the fact that the underlying data collection relies on key informant interviews.⁸⁶ This speaks to insights from the literature on the politics of expertise that organizations like IOM are first and foremost concerned with increasing their legitimacy through a performance of epistemic authority,⁸⁷ in order to increase their ‘claim to resources or jurisdiction over particular policy areas’.⁸⁸

9.4.4 The DTM’s Core Humanitarian Function

The various shortcomings of data produced through the DTM (some of which are acknowledged in the DTM methodological framework)⁸⁹ do not render the instrument useless or irrelevant. All migration and displacement data collection efforts face limitations, and the majority of interview respondents acknowledged the DTM’s value in terms of producing baseline data on displacements for humanitarian planning and programming.⁹⁰ By making visible the existence and needs of IDPs and other populations, the DTM fulfills crucial fundraising and advocacy functions, serving both IOM specifically and a broader range of actors involved in responding to human mobility. This plays out on two different levels.

On the one hand, when it is used in humanitarian settings, the DTM typically plays a crucial role during the early stages of a humanitarian response by providing a rapid operational overview as well as a displacement ‘planning figure’ that serves as the basis for subsequent funding appeals by other members of the humanitarian system.⁹¹ In 2020, DTM data on internal displacement was used in 80% of all humanitarian needs overviews and humanitarian response plans.⁹² However, despite its widespread use, the DTM’s relation to the wider humanitarian sector remains ill-defined. DTM operations in humanitarian settings are typically carried out under the umbrella of the IASC cluster system, yet there is no formal basis for this, especially in conflict settings. And while IOM has developed protection risk indicators (e.g. related to gender-based risks and unaccompanied minors) that can be integrated into DTM assessments,⁹³ other humanitarian actors remain concerned about IOM’s lack of a protection mandate. More specifically, interview respondents acknowledged attempts to improve DTM integration into wider coordination structures, but noted that this has so far been limited to ‘gentlemen’s agreements’, with insufficient impact on the protection response.⁹⁴ A related concern is that project-based DTM operations may create parallel structures on the ground that come to an end when the respective projectized funding ends rather than when the humanitarian community deems appropriate.

On the other hand, the DTM is one of the main sources of the global IDP statistics compiled by the Internal Displacement Monitoring Centre (IDMC)⁹⁵ that in turn feed into the UNHCR’s annual Global Trends Report. Both can be considered cornerstones for advocacy efforts on behalf of IDPs. Beyond these institutionalized distribution channels (that entail external checks on the quality of DTM data, e.g., in the form of triangulation with other sources used by IDMC),⁹⁶ both individual DTM country data sets and aggregate figures are widely used and reproduced by governments, human rights NGOs, think tanks and academics alike, and are in these contexts typically presented as authoritative, without further data quality checks. In sum, the DTM creates visibility for IDPs both in distinct humanitarian crises and international discourse on displacement.

Part of the DTM branding is the claim that by making IDPs visible and highlighting gaps in assistance, it contributes to accountability in humanitarian response.⁹⁷ The extent to which this accountability function is actively pursued in DTM field operations is difficult to assess. Either way, the significance of the DTM for IOM’s standing is immense: By positioning itself as the go-to authority for data on internal displacement, IOM has secured a place in the humanitarian system that is largely uncontested.

9.5 Showing Success through Numbers: The Political Functions of DTM Data

Beyond the core functions of humanitarian needs assessments and advocacy noted above, there are a number of additional functions that make DTM data politically valuable to donors. First, due to the prominent place that the recent global processes outlined above have accorded to data on migration and displacement, funding DTM operations can at times have a performative dimension: Through this, donors can showcase their efforts to meet the commitments agreed upon at the 2016 WHS, and to work towards more evidence-based policy-making – at a far lower political cost than, for example, increasing the number of resettlement places or opening up pathways for legal migration. Beyond this overall incentive to provide funding for data-related activities, a closer look at individual DTM operations reveals another purpose – a post hoc legitimization of policy interventions that, in the words of a former IOM staff member, can be characterized as ‘showing success through numbers’.⁹⁸

9.5.1 DTM ‘Mobility Tracking’ in Haiti, 2010–2014

The Haiti earthquake of January 2010 forced around 1.5 million individuals to leave their homes, leading to a massive internal displacement crisis in the country.⁹⁹ In the wake of this disaster, thousands of official and informal IDP camps sprung up around the country’s capital Port-au-Prince. The roll-out of the DTM across these various settlements followed swiftly, the operation being largely limited to the collection of data on the number and the location of the displaced, as well as a basic assessment of the availability of water, toilets and waste management.¹⁰⁰ At the same time, the overall humanitarian response to the Haiti earthquake was widely criticized for its inefficiency. Two years on, less than half of the funds pledged for reconstruction had been disbursed.¹⁰¹

In light of this apparent dysfunctionality, both the Haitian government and key donors like the US were eager to showcase positive developments. Bradley and Sherwood discuss how in this context, ‘the concept of internal displacement became synonymous with residency in camps, and the resolution of the displacement crisis with camp closures, rather than with the more complex challenge of supporting durable solutions.’¹⁰² Against this backdrop, the purportedly apolitical data collection efforts of the DTM soon became hugely politicized. DTM Haiti reports, setting out the results of different rounds of mobility tracking from 2010–2014 – that ultimately amounted to monitoring changes in IDP camp populations – share one common feature: they feature graphs visualizing the decreasing number of camp residents on the front page.¹⁰³ Former IOM staff involved in the Haiti response recount a fixation on numbers, on the part of both the Haitian and the US government, with IOM’s country office receiving daily visits from the US embassy to have the latest numbers of returns and camp closures reported.¹⁰⁴ Irrespective of the subsequent contextualization of DTM data (in these same reports, but also by external actors)¹⁰⁵ pointing to the reasons people chose to leave the camps (among these the fear of contracting cholera in crowded camp settings, forced evictions and other safety concerns), and to the lack of safe housing in the areas people returned to, the primary message conveyed visually was one of progress towards ending displacement.

In one instance, three large settlements – Canaan, Jerusalem and Onaville – were taken off the list of IDP sites at the request of the Haitian government, leading to a sudden drop in IDP numbers from 279,000 in June 2013 to 172,000 in September 2013. This decision was justified with the assessment that these settlements showed key characteristics of permanent settlements.¹⁰⁶ Amnesty International noted that ‘While this was true, the exclusion of these areas from the DTM had the consequence of leaving thousands of IDPs outside the scope of intervention by humanitarian organizations.’¹⁰⁷ In addition, there was evidence of forced evictions being carried out by state authorities in December 2013, indicating that contrary to the recommendations of the Special Rapporteur on the Human Rights of IDPs, conflicts over land tenure had not been resolved before the recategorization of these camps as regular neighbourhoods.¹⁰⁸ The Haitian example illustrates the DTM’s central role in constructing a narrative of progress that is disconnected from a meaningful understanding of durable solutions to internal displacement,¹⁰⁹ yet has real consequences for those affected in terms of access to support.

9.5.2 DTM ‘Flow Monitoring’ in West and Central Africa Since 2016

The so-called European refugee crisis of 2015 and 2016 not only led to the introduction of DTM flow monitoring along the so-called ‘Balkan route’ and at the EU’s external borders,¹¹⁰ but also to a significant expansion of DTM operations in West and Central Africa. In 2016, flow monitoring points were set up in Mali and Niger ‘to better understand migration movements to Algeria and Libya on the Central Mediterranean Route’, and by 2018 these data collection exercises had expanded to Burkina Faso, Chad, Guinea, Nigeria and Senegal.¹¹¹ This example shows how IOM has succeeded in transforming a tool developed largely for use in IDP situations to make it applicable to a wider range of contexts. While at the outset, DTM data collection exercises in West and Central Africa were limited to recording the mere number of border crossings (the main source of information often being local officials), this was later complemented by surveys aimed at gaining additional information about the routes and means of transportation used. Interview respondents noted that the explicit focus on movements towards Europe (‘the system was pretty much designed to show that people move North’),¹¹² and the ‘gold rush’ mentality that came with the readily available funding from the EU Emergency Trust Fund for Africa (EUTF), at times led to an indiscriminate counting of movements as crisis-driven, irrespective of centuries-old mobility patterns in the region.¹¹³

Similar to the Haitian example, DTM data in this context was highly politicized. IOM staff recount a ‘massive thirst for flow data’ that was not subsequently used in any meaningful way¹¹⁴ – echoing Read et al.’s observation that ‘the enthusiasm for […] data is vastly outstripped by the capacity to meaningfully analyse it’.¹¹⁵ In addition, IOM publications suggest that one of the primary purposes of these data collection exercises was to better target IOM information campaigns about the risks of migration, with the aim of informing protection and assistance interventions taking second place.¹¹⁶ At the same time, European governments regarded DTM data as a possible source of evidence of the effectiveness of EU deterrence strategies.¹¹⁷

The latter aspect was thrown into sharp relief during a minor data-related scandal in late 2016, when the non-profit news organization IRIN (renamed The New Humanitarian in 2019) uncovered how faulty DTM data on drastically reduced numbers of migrants transiting through Northern Niger was touted by the European Commission and various EU governments as evidence that their efforts to curb irregular movements on African territory were producing results.¹¹⁸ Apart from being a poignant reminder of the fact that data produced by international organizations is rarely questioned and generally accepted as authoritative, the fact that the faulty number was included in various EU documents even after the mistake had been pointed out illustrates the EU’s eagerness to showcase success through numbers.¹¹⁹ An EU spokesperson highlighted the fact that regardless of the one-off miscalculation that was quickly acknowledged and remedied by IOM, the overall trend of decreasing numbers through DTM flow monitoring points in Niger remained the same.¹²⁰ While this was true, it disregarded the fact that both external observers and DTM reports pointed to evidence of a divergence of travel routes rather than an actual decrease in transit mobility through Niger.¹²¹ This disregard of the DTM’s own caveats echoes dynamics from the Haiti operation, in that the visual elements of DTM reports – curves pointing downwards – are selectively picked up to legitimize certain policy choices, despite the fact that the written analysis accompanying them is more nuanced.

These examples illustrate the political value that DTM data can have for donors, as well as for the governments of states experiencing a displacement crisis. They also show that this is largely independent from the quality of the data: In line with the notion of the ‘value of good enough numbers’,¹²² the performative function of DTM data may be more important than its accuracy. However, this may have negative repercussions for those from whom the information was extracted, as well as broader problematic political implications. The following sections attempt to systemize the potential negative side of the DTM for its ‘data subjects’, which go hand in hand with the epistemic power the organization holds with regard to both internal displacement and irregular cross-border migratory movements.

9.6 Risks and Pathologies: Mapping Out Key Concerns

The DTM is premised on the idea that data on displaced populations is essential for protection and effective interventions. This focus on the positive potential of data, and on the advocacy and fundraising roles of the DTM, tends to obscure the risks associated with data collection concerning often highly vulnerable populations. Four areas of concern stand out.

9.6.1 Insufficient Protection of Data in Field Settings

The DTM methodology entails a number of risks with regard to the protection of personal data collected in the context of registration exercises or through individual or household surveys. These are mainly related to its reliance on vast networks of local enumerators. Their rapid mobilization in humanitarian crises suggests that there is limited little time for adequate training on data protection procedures. This certainly is not specific to the DTM (interview respondents from other organizations readily conceded the challenges of collecting data in crisis settings),¹²³ yet potentially exacerbated by the sheer size of the operation and its overall lack of standardization. Beyond this, IDPs, especially in conflict settings, are often at risk of continued persecution by state or non-state actors. Contrary to the wide-spread assumption in humanitarian work that being counted is automatically beneficial since it affords access to support, this risk may be heightened through the visibility that comes with data collection.¹²⁴ Risks emerge not only as regards personal data, but also non-personal yet demographically identifiable data. Notably, much DTM data falls into this category, such as when the ethnic or religious characteristics of a group are recorded alongside their movements.

9.6.2 ‘Erasure’ of Populations with Enduring Needs

The advocacy and fundraising function that the DTM fulfills with regard to IDP populations in particular comes with immense responsibilities: It often means that once a data collection operation is discontinued, humanitarian support also comes to an end, plunging the respective groups back into a state of invisibility as far as global humanitarian efforts are concerned. The problem here is not so much the fact that support structures are eventually dismantled, but that this is not done on the basis of a comprehensive needs assessment. While it is generally difficult to ensure sustained funding for data collection, this challenge is exacerbated by IOM’s projectized funding model: once individual funding streams have dried up, enduring needs are no longer captured in data. In addition, some of the examples set out above show pressure exerted by governments can lead to changes in DTM categorization (from temporary to permanent returns in the case of Nigeria, or from camps to permanent settlements in the case of Haiti) that may erase populations with enduring needs from the view of the international community. This raises the question of whether or to what extent DTM staff is trained to do advocacy in the sense of actively bringing forward needs and concerns emerging from the data collected.

9.6.3 Crowding Out Development-Oriented Data Collectors

As a first and foremost field-based operational agency, IOM has a competitive advantage over other data collectors to quickly ‘put boots on the ground’¹²⁵ and respond to new developments. Beyond this, part of the DTM’s appeal to donors lies in the fact that it is presented as a comprehensive package or one-stop-shop that can in theory cover all data needs, particularly in the context of internal displacement.¹²⁶ The 2017 IOM Framework on Addressing Internal Displacement, for instance, notes that the DTM increasingly provides the international community with information on IDP’s access to durable solutions.¹²⁷ In line with this, the DTM is moving beyond its traditional remit of baseline assessments of the needs and characteristics of internally displaced persons in crisis settings, and into the field of collecting data on the socio-economic profiles and aspirations of displaced populations through survey methods. However, just as interview respondents shared an appreciation of the contribution the DTM makes during the early stages of humanitarian crises, there are widespread concerns as to whether the DTM – whose management has a humanitarian background, and whose ‘quick and dirty’ mindset persists in non-crisis field settings even when prodded by other actors to strive for improvements in data quality¹²⁸ – is capable of producing the high quality disaggregated data on displaced populations that are essential for moving towards durable solutions.¹²⁹ One interview respondent described DTM data as a helpful ‘conversation starter’ on the needs of displaced persons, yet highlighted the fact that at a certain stage of a displacement situation when the need for more fine-grained data arises, the balance tips and the disadvantages of DTM data start to outweigh its benefits.¹³⁰ Yet a further monopolization of internal displacement data by DTM is likely: While donors are reportedly aware both of the limitations of DTM data, and are open to funding alternative data collectors, the bulk of the resources available tend to go towards the DTM.¹³¹ The further expansion of the DTM therefore comes with a real risk of crowding out actors and initiatives specializing in the collection of data required for development programming.

9.6.4 Feeding into Perceptions of Migration as a Threat

Fourth, the DTM’s continuous quest for growth has led to an expansion of its data collection activities far beyond its initial field of internal displacement. Instead, especially since the so-called ‘European refugee crisis’ and the related rise in demand for ‘flow data’, various DTM operations now cover irregular migration movements across borders. The funding sources of these operations indicate that they are primarily motivated by individual donors’ interest in containing migratory movements towards Europe on the African continent, rather than by overarching humanitarian or development rationales. As the account of ‘flow monitoring’ in West and Central Africa since 2016 demonstrates, IOM has responded to this demand by a sometimes indiscriminate collection of data on human mobility in the region. While subsequent DTM publications differentiate between different types of movements, clarifying that much migration on the African continent is intraregional,¹³² the aggregate numbers and corresponding visualizations may feed into a European discourse focusing on the threat of an impending African exodus towards Europe.¹³³

These different areas of concerns indicate that an understanding of data protection that focuses on individually identifiable data is too narrow a lens for grasping the responsibilities that arise in the context of DTM operations. The risks and pathologies outlined above are exacerbated by the fact that due to IOM’s decentralized and projectized structure, the DTM’s rapid growth continuously outstrips the organization’s capacity for oversight and control. The concluding section of this chapter provides an overview of IOM’s data protection standards, and asks whether these live up to the organization’s broader ethical obligations in terms of adequately addressing the risks and pathologies outlined above.

9.6.5 IOM’s Data Protection Standards: Fit for Purpose?

IOM prides itself in having been among the first international organization to develop its own internal guidance concerning data protection. The IOM Data Protection Principles were developed in 2009, and set standards concerning inter alia the specified and legitimate purpose of data collection, data quality, consent and data security as well as oversight, compliance and internal remedies. A corresponding Data Protection Manual was published in 2010.¹³⁴ Both documents focus on the protection of personal data, and are aimed at preventing ‘unnecessary and disproportionate interference into privacy’.¹³⁵ The introduction to the Data Protection Manual acknowledges the particular sensitivity of data related to vulnerable groups, and notes the increased challenges linked to data protection and human rights related to the use of ‘advanced technology in migration management’.¹³⁶ In terms of oversight, IOM’s Institutional Law and Programme Support Division of the Office of Legal Affairs serves as the organization’s ‘focal point […] for data protection issues and provides advice to ensure that personal data are processed in accordance with the IOM Data Protection Principles and Manual.’¹³⁷

Since this initial standard-setting exercise, IOM has been engaged in relevant inter-agency efforts to strengthen the protection of personal data at the international level, including through its membership in the UN Privacy Group that in 2018 issued the UN Principles on Personal Data Protection and Privacy with the threefold aim to ‘(i) harmonize standards for the protection of personal data across the United Nations System Organizations; (ii) facilitate the accountable processing of personal data for the purposes of implementing the mandates of the United Nations System Organizations; and (iii) ensure respect for the human rights and fundamental freedoms of individuals, in particular the right to privacy.’¹³⁸ Further related efforts include IOM co-hosting the 6th Workshop on Data Protection within International Organizations with the European Data Protection Supervisor (EDPS) in 2017, and participating in the advisory board of the 2020 ICRC Handbook on Data Protection in Humanitarian Action that contains an extensive section on standards for processing and sharing personal data in the context of new technologies.¹³⁹

Beyond these activities focused on the protection of personal data, IOM has recently started to engage in a number of processes concerned with the more encompassing notion of ethical and responsible data management. Most notably, these include its co-lead of the IASC’s Data Responsibility Working Group that developed ‘Operational Guidance on Data Responsibility in Humanitarian Action,¹⁴⁰ and its coordination role of the Humanitarian Data Science and Ethics Group (DSEG) that recently published its ‘Framework for the Ethical Use of Advanced Data Science Methods in the Humanitarian Sector’.¹⁴¹ Both documents highlight the need for standard-setting in the field of non-personal data, reflect the state of the art with regard to ethical and responsible data collection and processing (e.g. featuring significant overlaps with the 510 Data Responsibility Policy)¹⁴² and are in that sense of direct relevance to the bulk of DTM data collection in the form of non-personal data. In addition, IOM’s 2020 Migration Data Strategy and its 2021 Internal Displacement Data Strategy both contain explicit commitments to adhere to principles of data responsibility.¹⁴³ While this indicates a significant positive development, a number of question marks remain with regard to the practical applicability of these stated commitments to the current modus operandi of the DTM.

First, unlike general data protection obligations (which are comprehensive in scope), the relevant UN standards and guidelines on data responsibility only apply to humanitarian contexts. Considering that the increasingly prominent DTM ‘flow monitoring’ component is typically used outside acute humanitarian emergencies and instead covers instances of cross-border migration (however mixed the motives may be), there remains an apparent regulatory gap in IOM’s data standards. Second, any mention of ethical obligations or responsibilities in the realm of non-personal data is conspicuously absent from IOM’s website on data protection that instead puts front and centre the idea that ‘Data protection is about the protection of personal data of individuals’, and features links to IOM’s 2010 data protection manual as well as to the 2020 ICRC Handbook (both of which focus on the protection of personal data), but none to the IASC data responsibility guidance or the DSEG framework.¹⁴⁴ Further, the DSEG framework itself contains an ‘Action Point’ on data responsibility that encourages organizations to comply with one of three ‘sector-leading’ documents on humanitarian data responsibility, these three being the 2020 ICRC Handbook, the 2010 IOM Data protection manual and the OCHA Data Responsibility Guidelines.¹⁴⁵ The fact that compliance with any one of these documents (two of which only refer to the protection of personal data) is deemed sufficient is likely to fall short of inducing real progress on the ethical and responsible processing of non-personal data.

Taken together, this indicates a piecemeal and inconsistent engagement in data-related standards beyond established principles on the protection of personal data. The rationale for this may be twofold. On the one hand, given the increase in so-called ‘data incidents’ that amount to instances of data theft or unauthorized use and disclosure of personal data in humanitarian settings,¹⁴⁶ putting in place technical safeguards to avoid such instances in the future could be considered a reasonable priority for an organization like IOM. At the same time, the curious absence of references to more encompassing data responsibility standards despite IOM being actively engaged in all the relevant fora and processes indicates a desire to showcase commitment to progress on ethics and accountability for reputational gains, while at the same time sidestepping the actual consequences of related frameworks. More specifically, taking seriously the principle of data minimization that requires the limitation of data collection to what is directly necessary for a clearly specified purpose would be in tension with the current modus operandi of the DTM, a modus characterized by expansionism. An explicit commitment to apply the principles of data minimization and defined purpose in the collection of non-personal data would call into question the apparently indiscriminate collection of data on population movements currently carried out under the DTM.

The empirical analysis presented in the previous sections indicates that so far, IOM does not appear to live up to data responsibility principles. While both IOM’s 2020 Migration Data Strategy and the 2021 Internal Displacement Data Strategy show a willingness to improve upon current shortcomings, the institutional set-up to date has not been conducive to any meaningful progress: The expansion of DTM activities, both in terms of the number of operations and in terms of the types of movements covered, epitomizes IOM’s longstanding history of entrepreneurial behaviour.¹⁴⁷ In this sense, the problems and pathologies outlined above should not be viewed as surprising, but as a consequence of IOM’s projectized and highly decentralized structure.¹⁴⁸ What makes the DTM so appealing to donors – its flexibility, quick deployment and adaptability – has a number of negative side-effects as regards data responsibility. Without changes to the incentive structure, it seems unlikely the organization will make real investments in data quality and accountability. However, shortly before this chapter went to press, relevant institutional reforms seemed to take shape: In early 2022, IOM established its new Global Data Institute as an institutional umbrella bringing together all of IOM’s data collection and analysis activities under one common roof.¹⁴⁹ At the same time, negotiations about a reform of IOM’s budget that would entail permanent funding for IOM’s ‘core structure’ have moved forward, and the new Global Data Institute is considered part of this core structure.¹⁵⁰ Against this background, the concluding section sets out a number of proposals for reform.

9.7 Recommendations for Reform

In order to live up to the core tenets of data responsibility – especially data protection provisions that take into account the potentially sensitive nature of non-personal yet demographically identifiable data and adherence to high standards of data quality – and to ensure that IOM headquarters-level efforts to improve data quality and strengthen accountability – with regard to the DTM in particular – filter through to and are respected in field-level data collection, the following aspects are key.

First, as a matter of principle, an organization’s data collection efforts should not exceed its related expertise. A realistic assessment of the limits of an organization’s expertise requires independent external evaluation. Rather than seeking continuous growth and expansion, IOM’s management should identify the added value the DTM brings to the international community’s humanitarian and development-oriented endeavours, and limit its engagement to these areas. With regard to data on internal displacement, this could mean focusing on the provision of rapid overviews and humanitarian needs assessments, while leaving space for other data actors better equipped to produce data for development programming.

Second, in order to improve the quality of data produced through the DTM, a greater degree of standardization with regard to both the collection and the validation of data is necessary – despite the fact that this entails trade-offs with the tool’s cherished flexibility and its adaptability to diverse contexts. This standard-setting should go hand in hand with obligatory trainings for DTM staff with regard to data protection, data responsibility and the systematic integration of protection concerns into data collection.

Third, awareness of the normative dimensions and the potential political instrumentalization of migration data should be strengthened among technical DTM staff at all levels of the hierarchy, but especially in field settings. This awareness-raising should go hand in hand with guidance on how to collect data in a way that makes an active contribution to the protection of IDPs and vulnerable migrants.¹⁵¹

IOM’s new Global Data Institute opens up new opportunities to achieve these changes and to strengthen IOM’s accountability with regard to its data work, for example, by enhancing control and oversight of the DTM in particular. However, at the time of writing, the post of director of this new institutional entity was not yet filled, and there was no publicly available information as to its future structure and mandate. In further specifying this, one key objective ought to be insulating the collection of data on displacement from both the market-based pressure of competition and political imperatives. The planned provision of permanent funding for IOM’s core data activities would be a prerequisite for this. If data really is ‘the lifeblood of decision-making’,¹⁵² the international community should consider high-quality data from politically independent sources to inform humanitarian and development interventions a common good, and establish the conditions for obtaining it.