Key trends in the social sector has been saved
Key trends in the social sector
Social, technological, behavioral, and political trends
Read about the key trends in the landscape of philanthropy—and the world around it—that are fundamentally transforming the context for decision-making within social sector organizations and for the broader practice of monitoring, evaluation, and learning in this section of the Re-imagining Measurement toolkit. Understanding these trends allows us to imagine how the trends might be harnessed to create the better future that we’d like to see.
Key trends affecting monitoring, evaluation, and learning
Active efforts to create a better future for monitoring, evaluation, and learning will become even more important in the coming years as dramatic societal shifts create both new opportunities and new challenges. These shifts in the landscape of philanthropy—and the world around it—are fundamentally transforming the context for decision-making within social sector organizations and for the broader practice of monitoring, evaluation, and learning. Key trends cut across a range of different dimensions:
- Social trends – Demographic shifts that influence the populations and organizations with which an organization works
- Technological trends – Digital developments that influence the information that can be collected, accessed, analyzed, and applied
- Behavioral trends – Changes in perceptions, expectations, and preferences that influence how individuals engage with organizations and with one another
- Political trends – Transformations in policy that influence the requirements and priorities for funding and accountability
Understanding these trends allows us to anticipate the future we can most likely expect for monitoring, evaluation, and learning (absent active intervention). And it can help the field imagine how the trends might be harnessed to create a better future that we’d like to see. On the following pages, we explore a number of the trends that are most likely to have significant implications for the future of monitoring, evaluation, and learning.
Increasing racial and ethnic diversity
The US is becoming a majority nonwhite country; by 2020 more than half of the children born in the US are expected to be part of a nonwhite race or ethnic group. Minorities have increasing economic and political power as an ever-larger share of college students, entrepreneurs, and voters. The present US electorate is the country’s most racially and ethnically diverse ever; almost one-third of eligible voters are Hispanic, African-American, Asian-American or another racial or ethnic minority. These demographic shifts are changing the composition of communities and workplaces, creating the need for organizations to inclusively adapt to the priorities and perspectives of a broader range of constituents, staff, and other stakeholders.1
Next generation leadership and organizations
New people are entering the workforce and new types of organizations are driving change. More than one in three US workers today are millennials, and in 2015 they surpassed Generation X to become the largest share of the American workforce. Millennials bring with them greater familiarity with technology and data, new definitions of success, and new attitudes about supporting “causes” over organizations. Millennials, along with new “digital native” organizations that fundamentally integrate technology into their business models, are changing approaches and expectations about how data is collected, analyzed, and integrated into decision-making.2
Blurring of the sectors
The social sector, private sector, and government are increasingly intertwined. Innovative new models that blend elements from multiple sectors—including impact investing, social impact bonds, social enterprises, and B Corps—are increasingly prevalent. The number of B Corps, for example, has grown exponentially since 2007. In the social sector, greater numbers of staff have private sector experience and bring expectations, tools, and approaches rooted in their business experience. As organizations and individuals work across sectors, it’s also becoming more difficult to distinguish between funding for investing, philanthropy, and political activity.3
Accessibility and sharing of information
The spread of technology and near-universal internet access has changed the way we access and share information. There is now more data than ever at our fingertips. Technology now allows us to find and broadcast information both simultaneously (in real time) and asynchronously (for information posted online that can be found and retrieved indefinitely)—making it easier than ever before to share and collect data, and for people to find the information they need when they need it. As important, the rise of environmental sensors, “smart cities,” and the “internet of things” means the digital data collection process envelopes us everywhere. However, access is not universal or equitably distributed, and the more information that goes online, the more isolated those without access become.4
Growing connectedness and aggregation
Technology is not only increasing our access to data, but also making it cheaper, faster, and easier for people to collaborate, connect data sources, and create entirely new knowledge by mashing up and building on information they find. Organizations can now pool individual information into much larger, more powerful, collective datasets, and protocols for interoperability are making it possible to stitch together disparate data sources to aggregate information like never before. At the same time, individuals are becoming more aware of a lack of control over their data, and interest in privacy-protecting, data-destroying, and encrypted messaging tools is on the rise.5
The power of data analytics
The growing accessibility of information and exponential improvements in data processing technologies have resulted in a rapid increase in the speed and scale at which data can be processed. For example, in 2003 it took eight years and $1 billion to sequence a genome. Today it can be done in a few days for a few thousand dollars. Advances in our ability to analyze, visualize, and make sense of data are increasingly enabling society to ask and answer new—and often more challenging—questions. However, the skills and capacities to do this credibly and ethically – and to understand the findings – are not well distributed. Further, algorithmic analysis of large data sets includes racial, ethnic, and other biases. The datasets used to train artificial intelligence, machine learning approaches, and analytic methods rely on data sets also contain biases and other limitations.6
Growing recognition of the power of collaboration and ecosystems
Given the interconnectedness of today’s world and the scale of the challenges we now face, no independent business, agency, or organization, no matter how large, can succeed on its own. For-profit companies are increasingly recognizing the importance of thinking about value chains and ecosystems—Google, Uber, and Ford, for instance, are working together with lawmakers and regulators to advance clear rules of the road for self-driving cars. Meanwhile, foundations and nonprofits are embracing collective impact approaches and figuring out ways to align independent action to advance progress towards shared social goals.7
Increasing expectations for “say”
New technologies and methodologies are raising public expectations for greater participation and voice. The social, private, and government sectors are, with varying degrees of success, engaging users and constituents earlier and more frequently in decision-making. For example, product and program development is increasingly incorporating a user-centered design that places the customer or citizen at the center of the process (e.g., patient-centered or student-centered design). And this shift has been complemented by growing knowledge of human behavior supplied by disciplines such as behavioral science and social marketing. At the same time, individuals are becoming more aware of a lack of control over their data, and interest in privacy-protecting, data-destroying, and encrypted messaging tools is on the rise.8
Demand for greater transparency, accountability, and measurement
Efforts to make government data more open and available have succeeded in multiple countries. Civil society organizations have been both proponents and beneficiaries of this movement, and robust subsectors of “civic tech” organizations have emerged, alongside new programs at nonprofits that depend on access to digital government data. The US government has in turn increasingly emphasized an "evidence-based approach" to government social policy, tying more funding to data on effectiveness. There are still not clear guidelines on data sharing across sectors, and many nonprofits are challenged to meet often conflicting demands for privacy, accountability to funders, and non-discrimination or bias laws. Governments are also finding that controlling access to data can be as powerful as controlling funding when it comes to shaping civil society.9
1 See Bill Chappell. “For U.S. Children, Minorities Will Be The Majority By 2020, Census Says.” NPR. March 4, 2015; National Center for Education Statistics. “Digest of Educational Statistics.” 2012; US Census Bureau. “America’s Diverse Entrepreneurs.” 2012.; Joint Center for Housing Studies of Harvard University. “Homeownership.” No Date; Pew Research Center. “Multiracial in America.” 2015.
2 Pew Research Center. “Millennials surpass Gen Xers as the largest generation in U.S. labor force.” May 11, 2015.
3 See Lucy Bernholz. “Philanthropy and the Social Economy: Blueprint 2017.” Grantcraft. 2017; Suntae Kim et al. “Why Companies Are Becoming B Corporations.” Harvard Business Review. June 17, 2016; USSIF. “Report on US Sustainable, Responsible, and Impact Investing Trends 2016.” 2016.
4 See Larry Keeley. “Beyond Design Thinking.” Deloitte Insights. April 15, 2015; Hal Hodson. “Uber and Google race against car firms to map the world's cities.” New Scientist. September 28, 2016; Louis Columbus. “Roundup Of Internet Of Things Forecasts And Market Estimates, 2016.” Forbes. November 27, 2016.
5 See Shannon Greenwood, Andrew Perrin, and Maeve Duggan. “Social Media Update 2016.” Pew Research Center. November 11, 2016.
6 See Gina Kolata. “Human Genome, Then and Now.” New York Times. April 15, 2013; Craig Timberg. “Racial profiling, by a computer? Police facial-ID tech raises civil rights concerns.” The Washington Post. October 18, 2016.
7 See David Shepardson. “Google, Ford, Uber launch coalition to further self-driving cars.” Reuters. April 26, 2016.
8 See Deloitte. “Made-to-Order: The Rise of Mass Personalization.” 2015; Susan Sorenson and Keri Garman. “Getting the Most Out of the Employee-Customer Encounter.” Gallup. June 25, 2013; Ranking Digital Rights. “2017 Ranking Digital Rights Corporate Accountability Index.” 2017.
9 See Alan Abramson, Benjamin Soskis, and Stefan Toepler. “Public-Philanthropic Partnerships: Trends, Innovations, and Challenges.” Council on Foundations. 2012; Anne Kingston. “Vanishing Canada: Why we’re all losers in Ottawa’s war on data.” Macleans. September 18, 2015.