Distribution by Region

Distribution by Organisation Type, Document Type, Region and Year

Data occurs mostly in documents published in Europe, followed by North America, Asia, Africa and MENA with comparatively smaller contributions. Overall, the top five contributors in terms of occurrences are IGO, State, NGO, C/B and RC organisations.

Occurrences from IGO and C/B were mostly obtained from general documents published in Europe. NGO and RC documents provide the greatest number of occurrences, primarily from activity reports published in Europe.

State documents mostly generated occurrences in activity reports published in North America.

Data ...

• is a

  • currency

  • force for good

  • preoccupation

  • prerequisite

  • lifeblood of decision-making

• can vary in quality & sources

• is analysed to help decision-making

• has great importance & limitations

• is collected in many manners

  • written communication

• • oral communication

• • physical observation

• • mixed means

• the collection of which

  • requires training in methodologies, tools, variables, & other factors

  • is being fundamentally redesigned because of internet & smartphones

  • is conducted periodically in rounds & continuously in real-time

  • may use multiple methods to later triangulate results

  • may require partnerships at various levels

• is missing in areas with "data gaps"

• variables (gender, HIV status, mortality, minority group)

• cultures

• programming

• data sources

• organisations

• types of financing

• countries included/excluded from the HDI

• related phenomena (internal vs. external migration)

• can be collected more effectively via

  • surveys (individual/industry)

  • monitoring

  • data gap analysis

  • partnership

  • statistical correction (as a last resort)

  • standardisation

• the lack of which especially affects

• the poorest, most vulnerable

• the local level

• fragile states, economies

• nutrition progress

• accountability

• integration

• is experiencing a revolution

  • with enormous quantities becoming available

  • with new approaches

• should be

  • protected

  • managed better

  • used in a timely fashion

  • disaggregated

  • used transparently

  • comparable

  • shared more often

• the management of which has

  • methodological challenges

• • resource issues

• • related issues

It was recognized that "data is the currency of the private sector" and has intrinsic value.

Many, including the UN Secretary-General, advocate that data is a force for good, which will help "get people the support they need, more quickly and more efficiently" (Meneghetti, 2018).

Data itself has become a primary preoccupation in the field of disaster management and the humanitarian sector more broadly.

Data are a prerequisite for delivering the 2030 Agenda for Sustainable Development, ensuring that no one is left behind.

The United Nations Secretary-General''s report, "A world that counts", says " Data are the lifeblood of decision-making and the raw material for accountability.

Data is collected via

• written communication

  • SMS

  • questionnaires

  • case studies

• oral communication

  • hotlines

  • meetings

  • focus groups

  • interviews

  • household surveys

• physical observation

  • direct observation

  • physical & biological measurements

  • aerial footage

  • satellite imagery

• mixed means

  • tablets, smart phones

  • apps/software, online portals (developed internally or externally)

  • crowdsourcing

  • GPRS transmission

  • metadata, GPS tagging, bar-code distribution

Data collection

• requires training in methodologies, tools, variables, and other factors

• is being fundamentally redesigned because of internet and smartphones

• is conducted periodically in rounds and/or continuously in real-time

• may use multiple methods to later triangulate results

• may require partnerships at various levels

To quote again the late, former UN Secretary-General Kofi Annan, "Data gaps undermine our ability to target resources, develop policies and track accountability. Without good data, we're flying blind. If you can't see it, you can't solve it."

Data gaps exist within/across

• variables (gender, HIV status, mortality, minority group)

• cultures

• programming

• data sources

• organisations

• types of financing

• countries included and excluded from the HDI

• related phenomena (internal vs. external migration)

They are addressed via

• surveys (individual or industry)

• monitoring

• data gap analysis

• partnership

• statistical correction (as a last resort)

• standardisation

They cause worse outcomes for

• the poorest, most vulnerable

• the local level

• fragile states, economies

• nutrition progress

• accountability

• integration

Data gaps are mentioned with

• data deficits

• knowledge gaps

• quality issues

• data incompatibilities

Following on from this work, PARIS21 has prepared a road map for a country-led data revolution that identifies three main elements for success:

1. a major and sustained increase in the generation and use of data to help countries and the world as a whole to deal with the major challenges of eliminating extreme poverty, leaving no one behind and managing natural resources

2. promotion of real institutional change and much more effective use of technology to improve the performance of everyone involved in the production and use of data

3. making data accessible to everyone in ways that they are able to understand and use the data to hold

A key challenge is that the data revolution is not yet producing dividends for most developing countries. Having appropriate information and communications technology (ICT) infrastructure is a pre-condition for seizing the opportunities presented by the data revolution. ICT can also increase the speed, accuracy and impact of data collection and dissemination while reducing costs.5 Yet for this to happen, it is essential to bridge the significant digital divide that underlies the data divide.

The chart below represents the distribution of data between 2005 and 2019 in terms of the number of occurrences and relative frequency of occurrences. It also allows you to view the distribution across Regions, Organisations and Document types.

The key lesson is that a high-profile data analyst must be involved in the study from inception to incorporate any variations before the actual data collection.

Summary of Contexts

• methodological challenges

  • bias for reporting more extreme cases

  • quality/error control

  • data entry

  • data cleaning

  • diversity of methods internationally

  • quantity and representativeness

  • statistical validity

  • proper disaggregation (age, sex, etc.)

  • comparability across data sets

  • timeliness (i.e. delay between collection, analysis and response)

  • streamlining

  • harmonisation

  • verification

• resource issues

  • unequal access to resources and technology

  • data gaps

  • outdated tools, methods

  • availability, sharing across organisations

• related issues

  • physical and digital security

  • government authorisation

  • site access, safety

  • local cultures, practices

  • adapting and testing with local conditions

Main challenges raised by IASC organizations:

• Linking data analysis and collective decision-making. Decision-makers often make decisions without having the right data in hand.

• Protect what has worked so far in the area of information management and reduce duplication.

• Optimize technology at our disposal (mobile phones are underutilized).

• Capacity challenge faced by governments and partners that are excluded from digital revolution.

• Lack of Sex and Age disaggregated data.

• Need for a common approach.

• Inter-operability and data protection differ between organizations making it difficult to share data.

In the same survey, organisations raised concerns about data privacy and security issues, inadequate IT systems and human resources for effective publishing, and the need for further improvements to the IATI Standard to ensure that it meets the needs of the humanitarian community.41 Grand Bargain signatories will work together with the support of external partners in the coming months and years to overcome these challenges.

A veritable explosion in the volume, variety, veracity, source and speed of available data creates ever-increasing opportunities to understand the world and respond more effectively to development challenges ( Data Revolution Group, 2014). But there are major data gaps, including in civil registration and vital statistics systems (CRVS), poverty data and humanitarian assessments, and whole populations can be rendered invisible as a result. According to one estimate, as many as 350 million people are likely to be absent from the data used to measure development progress (Carr-Hill, 2013), many of whom are in countries affected by humanitarian crises (Development Initiatives, 2017b).

The American Red Cross is able to track and integrate social media comments from a disaster-affected area into response decision-making. The problem with data monitoring is that the sheer volume of data needs to be converted into timely and actionable information. During Hurricane Sandy, for example, more than half-a-million Instagram pictures and 20 million tweets were posted. In Japan, there were more than 177 million disaster-related tweets the day after the 2011 earthquake.Another challenge is the risk to data security and privacy, and of information misuse. These concerns are legitimate, but the actual risk may vary and it depends on the type of data being collected.

But transparency is neither neutral nor natural: instead, organizational approaches to improving transparency, such as engaging with a constituency on Facebook, are artificial and constructed. Moreover, in a world characterized by information overload, it is difficult to determine both what is relevant and what is missing. Finally, transparency also has an internal aspect: technical challenges to transparency have long been considered a problem. Data silos – systems that are not designed to facilitate data exchange – have proliferated within the humanitarian community and are believed to greatly complicate transparency.

Aside from discussing the challenges of data management, organisations have also clearly stated the progress being made, sometimes offering valuable and unique examples.

This report presents impressive examples where technologies already contribute to humanitarian action, often with the result of putting affected communities at the centre of humanitarian action as engaged participants and not merely as witnesses or recipients of aid. 10 Focus on technology and the future of humanitarian action In Syria, for example, digital data collection tools were adapted and are now used to serve as a commodity tracking system, monitoring the distribution of supplies as they are transported and delivered by local partner organizations in areas that remain inaccessible to international humanitarian agencies.

This is particularly the case for livelihoods in the informal sector, which in some cases can make up more than 50 per cent of the economic activity of a village or region. In response to the challenge of data quality, CRED has developed a ranking system to rationalize its choice of data sources. This improves transparency but cannot address the lack of standardized and systematic data collection. In 2003, the World Disasters Report (Chapter 7) argued that data quality could be improved by encouraging active rather than passive data collection.

This chapter discusses how thinking on development and development co-operation have been informed by the availability and use of data, and what now needs to change to efficiently exploit traditional data sources and take advantage of new ones. It argues that the data revolution is contributing to three shifts in focus: from gross domestic product to multi-dimensional well-being; from aggregate to micro data; and from administrative data to smart data.