Data systems + nonprofits: A new space for innovation

Posted on May 14, 2019 by Scott Wolovich

Guest blog post from Bhavini Patel CEO/Co-founder beamdata

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.

Bhavini Patel
CEO/Co-founder beamdata
For more information:
@bhavinip_
@beamdata