# Capturing data Capturing data involves gathering diverse information from alternative sources to reveal patterns that traditional data collection methods might miss. We seek out real-time, local, and often overlooked data streams – from radio conversations and satellite imagery to social media analysis and community mapping – to develop a more nuanced understanding of complex issues. Capturing data democratizes insights by giving voice to communities often overlooked in traditional data collection, ensuring more inclusive decision-making. It enables timely responsiveness with real-time information about changing conditions that matter for sustainable development. By diversifying our data sources beyond official statistics, we can detect weak signals of change, identify emerging needs, and understand how communities are already adapting to challenges. For this practice, consider non-traditional data sources not just as tools for detecting signals and uncovering patterns, but also as enablers that make communities more adaptive, responsive and resilient. When communities access real-time information about their own situations, they can understand the present moment and act on emerging challenges as they unfold. By contrast, traditional data often arrives too late to inform decisions in fast-moving environments. This delay leaves us one step behind, playing catch up with a reality that has already moved on. With real-time data, the focus moves from data collection and analysis to community action and response. When collecting data, it's essential to take ethical considerations into account, including consent, privacy, and the potential for harm, particularly when working with vulnerable communities. Data collection should be designed as empowering rather than extractive, ensuring those who generate data can access, understand, and benefit from the resulting insights. We must also consider data quality, representativeness, and the technical capabilities needed to process diverse data types. ## What we do to make big steps forward ### Tapping into real-time data We tap into data streams already being generated through everyday activities: social media conversations, radio call-ins, satellite imagery, mobile phone patterns, and sensor networks.[\[1\]](#endnote-1) We also work with communities like farmers tracking weather patterns, citizens monitoring air quality, or youth documenting urban changes to gather on-the-ground observations. The value of these non-traditional sources lies not just in what they capture, but when they capture it: providing immediate feedback rather than historical snapshots. By working with existing data flows and mobilizing communities as data collectors, we capture information as it unfolds to reveal early warning signals and emerging patterns. This real-time approach turns everyday information into actionable insights, enabling faster responses to changing conditions. ### Diversifying data sources We actively seek out information sources that reveal hidden [patterns](/undp-accelerator-labs/references/glossary.md#pattern) and perspectives overlooked by conventional methods. By combining diverse data sources – including open data, non-traditional sources, as well as traditional statistics – we create a more comprehensive picture of complex situations. This multi-source approach helps us understand how people move and adapt, reveal concerns that official statistics miss, and identify community needs that don't appear in formal assessments. Each data source brings unique insights that, when woven together, help us better understand the multiple dimensions of development challenges and find entry points for system change. ### Building data partnerships We build partnerships with diverse actors across the data ecosystem, from government agencies and private sector entities to research institutions and local NGOs. The private sector in particular ( including telecom, finance, logistics, utilities, media and beyond) often holds critical data relevant to development challenges. These partnerships enable access to datasets like cadastral records, administrative registers, mobile usage patterns, transaction data, supply chain information, or satellite imagery that would otherwise remain siloed. Building these collaborations requires navigating data governance protocols, demonstrating public value, and establishing trust with both data holders and communities. {% hint style="info" %} #### Reflection questions These reflection questions help us identify data sources, establish data partnerships, navigate ethical considerations and ensure communities benefit from the insights their data reveals. * Which data is already being generated through everyday activities that could reveal hidden patterns? * Who holds valuable data in this ecosystem – government agencies, telecom operators, community groups, or others? What would motivate them to share it? * Which real-time information could help us detect early warning signals or respond to emerging challenges? * How do we ensure captured data represents diverse perspectives without reinforcing blind spots? * Who or what guarantees the accuracy and quality of the data we collect? * Which ethical considerations must we address – consent, privacy, or potential harm to vulnerable groups? * How can we ensure data collection empowers communities rather than extracts from them? * Can those represented in the data access, understand, and benefit from it? * How will data be collected, stored, and protected over time? {% endhint %} {% hint style="info" %} #### Methods and enabling technologies * [**Non-traditional data**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#non-traditional-data) tapping into social media, sensors, satellite imagery to unlock real-time information from everyday digital activities * [**Traditional/official data**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#traditional-official-data) using census, statistics, administrative records to establish baselines and provide historical context * [**Data collectives**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#data-collectives) to pool and connect datasets across organizations, generating insights that emerge from combined data * [**Citizen science**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#citizen-science) to enable communities to generate and analyze their own data about local conditions, revealing patterns when pooled across locations * [**Crowdsourcing**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#crowdsourcing) to tap into collective knowledge by gathering data and observations from large numbers of contributors * [**Microsurveys**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#microsurveys) to collect quick responses from communities through short, accessible questionnaires, commonly via mobile devices * [**Web and data scraping**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#web-and-data-scraping) to extract and consolidate publicly available data from websites, PDFs, and online databases for large-scale analysis * [**Internet of things & sensors**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#internet-of-things-and-sensors) to continuously monitor environmental conditions, resource usage, and community activities in real-time * [**Open data**](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#open-data) to make datasets freely available for anyone to access, use, and analyze, enabling communities to generate their own insights * [**Geospatial data platforms** ](/undp-accelerator-labs/doing-r-and-d/6.-r-and-d-methods-and-enabling-technologies.md#geospatial-data-platforms)to map community assets, resources, and patterns through location-based data {% endhint %} *** ## Notes 1. We make sure these data sources are anonymized. [↑](#endnote-ref-1) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://bas-leurss-personal-organization.gitbook.io/undp-accelerator-labs/doing-r-and-d/5.-r-and-d-practices/capturing-data.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.