People need technology to make sense of data and to present it in a digestible way. That’s where web and app analytics tools come in. The tools described in this resource gather and visualize data to help people make meaningful decisions.

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Well-managed data can support social impact organizations (SIOs) as they conduct lifesaving health research, reduce environmental threats, and produce societal value for individuals and communities. But these benefits are often overshadowed by harms, as current practices in data collection, storage, sharing, and use have led to high-profile misuses of personal data, data breaches, and sharing scandals. This report provides a practical framework and shares case studies to demonstrate how citizens can participate in shaping and improving ways data is collected and used. 

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Surveys are an important form of data collection to support program improvement and storytelling. We also know it can be challenging to create, disseminate, and analyze surveys amid all your other priorities. This resource shares eleven steps to collect and leverage high-quality survey data.

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Collecting data is essential to determine your organization’s impact and reach, but collecting data remotely can be extremely difficult during virtual programming sessions. With this in mind, it is important to have a plan of action to track and measure success. This resource shares key areas to remember when planning to collect data on virtual programs.

The post Collecting and Tracking Virtual Engagement Data appeared first on data.org.

In the last decade, incredible improvements have been made on the data collection side that helps the processing pipeline. However, various datasets are managed and maintained by several organizations in disconnected systems. This causes some information about the data to be lost during this transition, and people doing the cleaning have no control over the collection. The solutions to data cleaning challenges that arise demand varying degrees of personnel hours, which in some cases could be avoided through automation.

The post 3 Key Challenges to Data Cleaning in Digital Development Programs appeared first on data.org.

Data commons collocate data with cloud computing infrastructure and software services, applications, and tools to create powerful resources for the large-scale management, analysis, harmonization, and sharing of data.  Unlike data warehouses, data lakes, and other systems that support an organization’s business analytics, a data commons is focused on providing a resource for a community or collaboration, or in support of multiple communities. 

The term data commons derives from the more general concept of a “commons,” a term that was popularized by Elinor Ostrom, who defined it as a natural, cultural, or digital resource accessible to all members of a community, or, more broadly, of a society. An example is a pasture for animals to graze in a village. These resources are held in common, through a partnership, a not-for-profit, or another entity, but not owned privately for commercial gain [1].

An example of a data commons is the UK Biobank; a large-scale biomedical database with 500,000 participants who consent for their data to be used for the specific purpose of bonafide research on the diagnosis, prevention, and/or treatment of serious and life-threatening illnesses. Evidently, data commons can be powerful catalysts to innovation and transformation of the social impact sector especially when collaborative and synergistic action is required in the efforts to strengthen epidemic preparedness and response, financial inclusion, and enabling access to opportunity.

Data commons can be very challenging to set up and more so to maintain successfully. In this guide, we will share more examples of working data commons and three essential steps to maximize chances of success.

In this Guide

• Get inspired: explore examples of existing data commons
• Define your why and how: Understand the importance of governance in data commons
• Bootstrap: Investigate technology platforms for data commons
• Plan for the long term: Consider issues of data ingest and harmonization

Data Commons in Practice

Data Commons have been developed to address a diverse set of data sharing and research needs.  The following are some examples of data commons:

• NCI Genomic Data Commons – a data commons for cancer genomics data
• European Open Science Cloud – a European partnership supporting open science
• Australian BioCommons – a data commons supporting Australian bioscience
• datacommons.org – a data commons operated by Google

Gen3 – a general-purpose open-source platform supporting ad hoc analysis 

Terra – a platform for biomedical data

Figshare – a general data platform for storing, sharing, and discovering research data

Dataverse – open-source research data repository software

The FAIR data principles

The CARE Principles for Indigenous Data Governance

Research Data Alliance

Conclusion

Data commons support a community’s management, analysis, and sharing of data.  For this reason, data commons require a governance framework that supports the community’s values and goals.  Furthermore, these frameworks are key to ensuring resources are used for the specific purposes for which they were made available. This, in turn, enables more resources to be safely mobilized for the benefit of the community. Successful data commons tend to carefully curate and harmonize the data they contain, which reduces the time and effort required for users to analyze the data, especially when the data comes from multiple sources. This is especially helpful for emergency research and innovation. 

Please feel free to suggest any other guides you found helpful by contacting us and we may incorporate them.

[1] Elinor Ostrom. Governing the commons: The evolution of institutions for collective action. Cambridge university press, 1990.

Grateful for the contribution of Robert Grossman, Ph.D., Frederick H. Rawson Distinguished Service Professor of Medicine and Computer Science, and the Jim and Karen Frank Director of the Center for Translational Data Science (CTDS) at the University of Chicago.

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This resource focuses specifically on the ethical aspects of humanitarian data management, ranging from standard exercises such as field-level data collection and processing to more advanced applications of data science, such as predictive analytics. It addresses common ethical concerns with data management, including validity, bias and fairness, and privacy and anonymity.

The post Humanitarian Data Ethics appeared first on data.org.

Archiving is a general term for the range of practices and decisions that support the long-term preservation, use, and accessibility of content with enduring value. It is not a one-time action but a process and an investment that connects directly to an organization’s goals for project outcomes and impact. This resource provides a guide on archiving and preserving data when the project is entering its closing stages.

The post Closing a Project: Archiving and Preservation of Content appeared first on data.org.

This resource provides an introduction to Data Impact Assessments (DIAs) in humanitarian action. DIAs can help maximize benefits and minimize risks in operational data management. DIAs can also serve as a compliance mechanism with applicable laws, regulations, and internal policies and provide other guidance to reduce risks in data management.

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Back in the early 90s, companies and organizations started investing in what was referred to as a webmaster — essentially the person that was in charge of all things having to do with the internet. As website needs became more complex and core to operations, we realized the internet wasn’t something that could be handled by one webmaster. Something similar has happened with data, where organizations invest in a “Data Person” to oversee all things data. But just like with the internet, collecting, storing, and analyzing data requires lots of different skills and tools that can’t be found in a single person.

The Data Stack is essentially what moves your data from individual ingredients stored in individual systems, to one cohesive data environment that is accessible and usable. It is often referred to as Data Engineering. This guide will walk you through the four major parts of a data stack and point to some of the things you need to know and consider as you build out your data infrastructure.

In this Guide

• Ways to collect and share data in your organization.
• Recommended data storage technologies and solutions.
• Practices for keeping your data secure.
• Ways to strategically use data in your organization.

Conclusion

These resources will help you level up your organization’s data stack. This represents a maturation from a tool-based approach (i.e., I have and analyze my data in a set of tools) to take a more infrastructure-focused approach where you use tools more specifically to collect/ingest data, bring it together, and then analyze it from one place. As you invest more in this kind of capability, you are also increasing the value that you are able to get from your data. We also suggest reaching out to peer organizations, whose challenges and learning may provide direct and current examples of ways forward. Please feel free to suggest any other guides you found helpful by contacting us and we may incorporate them.

The post How to Build Your Data Stack appeared first on data.org.

It seems like every week there are new data breaches, concerns about our privacy, and stories about data science gone wrong. We hear about banks giving credit to customers they know can’t afford it, casinos using data science and behavioral economics to keep customers coming back for more and driving them into bankruptcy, and on and on…

These kinds of behaviors aren’t limited to private sector actors in search of greater profits. Nonprofits can also be irresponsible with their data, or fall victim to unintended consequences. Blackbaud suffered a data breach and ended up paying ransom to hackers that were in their system for months. The United Nations shared data about Rohingya refugees without their consent, possibly increasing their vulnerability in returning to Myanmar.

In this Guide

• Simple, practical tools and principles to help your organization use data responsibly. 
• Checklist created by the DrivenData team for data scientists to assess the risk of a project at the outset.
• A tool developed by IDEO to support ethical design with data.
• How Salesforce developed the first office of Ethical & Humane Use.
• Help an organization develop a mindset for collecting and using data responsibly.

Ethical and Humane Use Leadership

Salesforce developed an Office of Ethical & Humane Use to develop principles and practices for ensuring that their technology is being used in ways that align with the company’s values. It has enabled employees to report unethical uses by customers, developed a toolkit to help teams build with intention, and pushed other technology companies to develop similar offices. 

Conclusion

The resources above will help you implement responsible data use for your organizations. We also suggest reaching out to peer organizations, whose challenges and learning may provide direct and current examples of ways forward. Please feel free to suggest any other guides you found helpful by contacting us and we may incorporate them.

The post Using your Data Responsibly and Ethically appeared first on data.org.

The DataKind Playbook is a globally-accessible, living knowledge base that enables users to design, implement, and manage a Data Science and AI for Good project following DataKind’s proven approach. Created by DataKind’s staff and volunteers, the Playbook provides direction on how to approach a Data Science and AI for Good project and to do it well. It also codifies the lessons learned from failures and successes for use on future projects.

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