Data systems in the nonprofit sector are underdeveloped and fragmented, which has long-standing implications for community-based decision making.
Typically, nonprofit practitioners collect and use data for various purposes with many organizational leaders claiming there is too much data and too little actionable information. It is true that many practitioners are overwhelmed by large amounts of data, which can be frustrating to analyse. As a result, data often results in little problem-solving value or strategic insight. This perpetuates a culture that enables short-term performance and inhibits long-term strategizing or growth. The economic development and social good delivered by nonprofits necessitates more sophisticated analysis of meaningful data trends.
Nonprofits represent 15 percent of the Pennsylvania workforce and generate $132 billion in annual revenues. More specifically, Allegheny County alone is home to over 2,000 nonprofits generating $4.5 billion in annual revenues and sustaining over 100,000 jobs. Through direct social services and assistance to people, nonprofits are a critical pillar of building sustainable communities and catalyzing economic revitalization. Despite the large density and socioeconomic impact of nonprofits in Allegheny County, there is little use of data that guides decision making. Data-driven decisions are necessary to ensure an impact driven mission reaches the people and communities it intends to serve. More importantly, data is essential to identifying organizational strengths that catalyze equitable development, while also pinpointing flaws.
For example, long-term measures of socioeconomic indicators can lend predictive power to issues such as gentrification. Uneven trends of urban development can fuel uneven patterns of economic opportunity. Often the result is low-income families are relegated to neighborhoods impacted by limited access to affordable housing, fair-paying jobs, and quality schools. Community-level data can catalyze innovations such as forecasting tools that predict socioeconomic trends thereby allowing interventions to be made early-on. Local data trends can add value by helping organizations better manage affordable housing stocks, allocate limited resources, and plan equitable development.
A closer look at nonprofit data systems reveals a challenging reality. More often nonprofit practitioners admit to the importance of data but do not understand how to collect good data or make it useful for their impact-driven cause. Large amounts of data exist, but transforming data into data with problem-solving value is complex and resource intensive. This causes frustration and leads many practitioners to rely on assumption-based programming rather than data-driven decisions. In order to break down barriers to higher standards of data practice, we need to assess three core
organizational-level data concepts.
Understand the purpose of data
Knowing what you want to do with data and understanding the purpose it serves is a critical data collection requirement. However, big data is often not collected with mission or people in mind, which presents immense challenges in understanding their problems and extracting problem-solving value. Current models of data collection prioritize gathering large amounts of unstructured data, but more data doesn’t necessarily mean better data. Big data can’t be explained or used without understanding the human experiences, environments and emotions in which it arises. When we
activate data collection with an impact mission, we can make sense of human problems and develop better strategies, services, and policies.
Adopt standard methods of data collection
Adopting standard data collection practices requires understanding what practices do and do not work for your organizational needs. Within a nonprofits data ecosystem there can be significant amounts of different data types scattered across spreadsheets, databases, survey tools, and internal systems. Some nonprofits still rely on collecting data using paper surveys and interviews. Lack of data standardization and centralization makes it difficult to understand the macro and micro level problems that should inform the design of data analysis tools. As a result, the tools created don’t capture answers to important questions or allow nonprofits to effectively harness the utility of data.
Collect representative and unbiased data
In order to nurture inclusive community-based decisions, it is critical to limit bias in data collection and consider the local realities of people such as language access and ethnic diversity. Data is often collected without regard for local communities and aggregated to describe complex human experiences. This means certain subpopulations may not be represented. Typically there are quantitative data describing majority populations, while data about minority groups are relegated to qualitative sources. Recognizing disparities in data representation helps us understand indicators associated with demographics, poverty, housing, language accessibility, and more. Prioritizing comprehensive data collection is critical for identifying patterns between populations and specific geographies, which can impact programming and outreach strategies.
The first step to building an ecosystem of data-driven decision making requires ensuring practitioners have the knowledge to adopt sophisticated standards of data collection and analysis. This means working towards an organizational culture that values data and examining why data is
important to your social impact growth. This also means including data collection and analysis in general operating budgets thereby encouraging philanthropy to fund the development of data systems. The gradual implementation of data practices can break down silos between organizations, allow for new standards of impact measurement, and optimize social impact delivery.
Originally posted as a guest blog post for New Sun Rising.
For more information: