---
id: "coronavirus-smarter-crowdsourcing:strategies/Review-data-collection-processes-and-ensure-all-da"
type: "GovLab policy strategy"
title: "Review data collection processes and ensure all data systems adequately collect disaggregated data…"
url: "https://coronavirus.smartercrowdsourcing.org/strategies/review-data-collection-processes-and-ensure-all-da"
summary: "Review data collection processes and ensure all data systems adequately collect disaggregated data differentiating vulnerable populations."
source: "The GovLab (coronavirus.smartercrowdsourcing.org)"
licence: "Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/)"
---

# Review data collection processes and ensure all data systems adequately collect disaggregated data…

Review data collection processes and ensure all data systems adequately collect disaggregated data differentiating vulnerable populations.

- Timeframe: 1-3 Months (immediate)
- Audience: Private Sector, Academia, Civil Society, Healthcare Providers, Organized Civil Society, Nonprofits
- Type: Policy/Legislative/Regulatory, Data, Technology/Innovation, Partnerships, Vulnerable Populations
- Recommendation: [Improve Data Management](https://coronavirus.smartercrowdsourcing.org/recommendations/improve-data-management)
- Part of: [Smarter Crowdsourcing in the Age of Coronavirus](https://coronavirus.smartercrowdsourcing.org/)

## Actions governments should take

1. List existing data collection and storage systems the government controls. A thorough audit of these systems will look both at national, subnational, and local data sources.
2. Identify opportunities to capitalize on existing data from across government ministries or health centers.
3. Review existing data collection and storage systems to ensure they capture disaggregated data by sex, age, gender, ethnicity, race, education level, socioeconomic status, and other important demographic and social factors.
4. Standardize data collection processes to ensure sex, age, race, and ethnicity are adequately captured across all systems.
5. Review each analysis to consider which populations are missing from current data collection processes and how they can be captured by existing processes.
6. Avoid missed uses of data.

## Examples from around the world

1. [The COVID-19 Sex-Disaggregated Data Tracker is a database that captures sex- and gender- disaggreagated data on COVID-19 that highlights differences in COVID-19 infection, illness and death among women and men from across the entire world. The intention of the project is to create the evidence base to identify and address gender-disparities in health outcomes and to inform gender-responsive approaches to COVID-19. and to address gender-disparities in health outcomes.](https://globalhealth5050.org/the-sex-gender-and-covid-19-project/#1587664245579-30fa0c77-7d6a)