Five Key Considerations for Leveraging Data for Better Migration Policies

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The effective and appropriate use as well as impact of data is a matter of growing cross-sectoral interest and concern. This emerging issues piece examines such issues in relation to the leverage of data within the humanitarian sector, specifically in the context of migration policies. It explores five key considerations, making a number of recommendations as to how identified issues might be progressed, namely that: data alone are not enough; good quality data can drive successful policies; data handling is a serious matter; conveying accurate information is vital; and number do matter.

Overview

Scientists and policy makers agree that we live in what has been named “the Information Society”. In this society, not only is there an overabundance of data easily accessible through digital devices, but also the information itself becomes a commodity to exchange, use (and misuse) and capitalise on.

For humanitarian actors, having the right data at the right moment means that aid can be allocated and delivered more quickly and effectively, with the result that potential harm to people and assets is reduced. For example, knowing that in a disaster-stricken area people are falling short in WASH (water, sanitation and health) supplies can help humanitarian actors to understand what kind of intervention is needed and, therefore, to reduce the potential for adverse consequences such as greater mortality or the spread of disease.

With such issues in mind, the UN Office for Coordination of Humanitarian Affairs (UNOCHA) recently launched the Humanitarian Data Centre in The Hague, The Netherlands, with the stated objective of increasing the use and impact of data in the humanitarian sector. This initiative aligns with other similar ones developed by international organisations to ensure the availability and easy exchange of data to reduce increasing and evolving risks derived from ‘natural’ and ‘non-natural’ hazards (e.g., security threats). The availability of data about such emerging risks makes it easier to address them by highlighting risk drivers and patterns (e.g., whether the risks are more concentrated in a geographical area). Examples of initiatives are the Risk Data Hub established by the Joint Research Centre (European Commission), and the Open Data for Resilience (OpenDri) Initiative developed by the Global Facility for Disaster Reduction and Recovery (GFDRR). At a more local level, law enforcement agencies can also offer valuable information to tackle risks derived from criminal activities. For example, the UK Metropolitan Police service offers an online tool that allows citizens to visualise criminal and anti-social behaviours at a neighbourhood level.

Data and Migration Policies

The need for the more effective management and analysis of data is illustrated here in the context of migration, a topical issue that is challenging governance systems across countries and continents. The underlying principles are, however, of wider significance and applicability. Mass migration requires governments to develop and enforce appropriate policies, and to engage with various actors to deal with the potential challenges that might emerge. In order to support the response strategies to the migrant crisis in Europe and to ensure that these are driven and supported by accurate data, the European Commission’s Knowledge Centre on Migration and Demography (KCMD) and the International Organisation for Migration (IOM)’s Global Migration Data Analysis Centre (GMDAC) recently created the ‘Big Data for Migration Alliance’ (BD4M). Its broad scope is to “harness the potential of big data sources for migration analysis and policy-making, while addressing issues of confidentiality, security and ethical use of data”. The launch of this initiative follows on from previous exploratory studies on the use of big dataartificial intelligence and social media analysis to understand and predict migration patterns.

Despite the many and diverse projects being implemented, there are still several shortcomings that need to be addressed to best use data to create effective migration policies. For example, policy-makers may find it hard to translate best practices of migration management into concrete campaigns and initiatives. Collaboration with all key stakeholders involved in a campaign – including end users (e.g., migrants and NGO officers), scientists and other potentially interested organisations (e.g., community-based organisations, local level government) – is key to ensuring that theoretical developments have a positive impact on the life of migrants and their host communities.

Five Key Considerations for Leveraging Data

Five key considerations, that policy-makers would be well advised to factor in when using data to devise policies for migration, are highlighted briefly here:

1: Data alone are not enough. We need information and then knowledge. The sheer volume of data available does not necessarily mean that all of it may be translated into useable data for the purposes of establishing and better informing effective policies (migration or otherwise). Data are often available in a raw, unstructured manner and need considerable interventions before they may be elaborated, combined and usefully interpreted. For example, it may be necessary to design templates for data storage and methodologies for data validation. This is illustrated by the DIKW pyramid (fig. 1) which reminds us of the many and various intervening steps/phases that exist between the availability of raw data and its ability to be used to inform policies.

Migration policies must be data-driven, knowledge-informed and supported by a deep and extended understanding of the context in which the policy embeds. To this end, it is recommended that policy-makers find innovative ways to team-up with scientists and tap into their expertise. Many policy-making organisations, like the European Union, already incorporate in-house research services consisting of leading field experts, as well as contracting out such services to independent think thanks and consultancy companies. These services can help policy-makers to better bridge the gap between data and policies through their resultant developed guidelines, technical reports and case studies.  A pertinent example is the Design For Impact Framework, recently developed by GFDRR, which incorporates wide-ranging advice and real life examples to incorporate open data and risk communication into decision-making processes.

2: Good quality data can drive successful policies. This second point follows on from the one just made. Once again, capturing data about migration does not mean that we can start to climb the DIKW pyramid straight to the top. First, we need to be sure that the data is relevant, complete and unbiasedThis requires that a number of preliminary questions be asked. Will the data collected help to respond to the challenges faced? Is there any missing data that should be accounted for? Is the interpretation of data potentially biased by the world views, socio-economic conditions or ideological perspectives of those who handle and interpret it? As was highlighted by a recent World Bank blog post, “poor data can be amplified into bad policy”. Bearing this in mind, data quality should also be taken seriously. On the other hand, policy-makers would be well advised to deal with and remain cognisant of the inherent uncertainty that accompanies incomplete data or data that are difficult to interpret. Indeed, the increased complexity of social phenomena can make their dynamics unclear until a later stage. In such cases, policy-makers could strive to pull together information in the more complete way possible while still leaving room for new incoming information.

3: Data handling is a serious matter. If data quality is a serious matter, data handling is of equivalent importance. The new European Union’s General Data Protection Regulation 2018 (GPDR) sets out strict rules to safeguard data privacy. Particularly when we are focussing on powerless and vulnerable people like migrants, it is of great importance that policy-makers pay attention to the ways in which sensitive data is collected, stored and used in order to avoid its misuse, such as for exploitative purposes. Collecting data about migrants can be a double-edge sword. Some projects have called for the collection of data to create a digital identity of migrants (with obvious positive consequences, including the possibility to access basic rights and services). Conversely, others have warned about the potential risks accompanying the collection of biometric information (e.g., fingerprints, iris scans, and photographs) of people who often are seeking to escape from persecution and conflict situations. Potential risks might include, for example, creating a database of people belonging to a particular ethnic group, or of political opponents, which may be misused to persecute rather than protect these vulnerable people. In the wrong hands, data about migrants may have disastrous consequences on their already broken lives.

4: Conveying accurate Information is vital. But sometimes it is not sufficient. International organisations now acknowledge that providing information is a form of aid as important as the provision of food, education and housing. With correct and timely information in their hands, people affected by disasters or conflicts may be empowered to find their own solutions for their recovery and to regain a sense of control over their lives and destinies. With this in mind, initiatives such as Refugee.info have been geared toward the provision of information to migrants and refugees about, for example, how to access health services or to assert other rights. Despite such efforts, reports – such as this one from Internews – reveal that the journey of migrants is often uninformed or misinformed. Migrants commonly face several challenges when trying to navigate through the myriad of data or information they are provided with by multiple official and non-official actors. This includes, but is not limited to, challenges regarding understanding the local languages, national and international laws and legal procedures, or filtering out rumours from accurate information. Also, the way in which data is received, elaborated and acted upon depends to a large extent upon the social, economic and cultural backgrounds of the recipients, as well as the cognitive processes that underlie the path between data reception and consequential behaviour.  Decades of research have demonstrated that the linear relationship between information provision and desired behaviour can be fundamentally false. For example, a recent study found that the provision of accurate information about migrants did not substantially change the policy preferences towards migration of those who had received it. This is an important issue for policy-makers to bear in mind and account for, namely the long timelines that may be required for their policies to become impactful in practice. Ideally, this process should include important steps, such as data processing and analysis (e.g., data validation, combination of multiple sources) as well as feedback mechanisms to ensure that data and information are received and elaborated correctly.

5: Numbers do matter. But context and people matter more. From the previous point, it follows that although quantitative data about the migration phenomena (e.g., number of migrants arriving in a week by country) is fundamental for defining appropriate responses, qualitative data (e.g., cultural and religious norms of incoming migrants) oftentimes can be even more important. For instance, in order to define effective policies, it may be relevant to collect information about who these migrants are, where they are moving to, as well as why and what the underlying drivers for migration are.  While aggregated data can certainly give a snapshot of migration trends, data disaggregation allows for the emergence of patterns that may remain otherwise concealed. For example, disaggregating data about migrants by gender may reveal that male migrants tends to move towards certain locations, whereas women with children tend to remain in the same location of arrival. Armed with such analysis, policies may be adjusted accordingly to maximise their impact on those to whom they are targeted. That said, it is fully recognised that this recommended path is inherently more arduous, since it involves looking at the data in ways that are more granular.  There is scope for pursuing it though: devising policies that recognise the complexity of today’s societies, whilst offering complex and multifaced answers that better suit the emergence of new threats and opportunities.

Progressing These Issues

In order to progress some of the recommendations made here, there are a number of concrete activities that policy-makers may wish to consider:

  • Establishing and/or reviewing existing mechanisms for the management and processing of big data related to migration, in particular how to translate such data into actionable information.
  • Ensuring that data management procedures comply with existing national and international regulations, such as GDPR, as well as other legal obligations (such as under international human rights law and international refugee law).
  • Ensuring that interdisciplinary processes are in place which permit the contextualisation of collected data, such as by accounting for relevant social, cultural, legal and/or economic factors of the phenomena under analysis, illustrated here by but not limited to migration issues.
  • Undertaking reviews of the entire data collection, processing, management and dissemination cycle, including for its accuracy, consistency, and effectiveness.

GSDM would be pleased to assist clients with these and other data related analysis and management issues.

Dr Serena Tagliacozzo is an Associate of GSDM. She recently finished a traineeship with the Joint Research Centre of the European Commission, Peace and Stability group. Her areas of expertise include open source intelligence and data mining for conflict risk prevention and disaster risk monitoring, digital innovation and ICT for disaster management.

 

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