Nonprofit organizations often find themselves needing to perform in multiple markets to survive, and these markets aren’t all customer-facing. Especially prevalent within the arts, companies will sell tickets, memberships, books, coffee, and more, all while competing behind the scenes for grants and major gifts. This drives the need to adopt specific market orientations by allocating labor and resources to whichever markets in which a firm wants to be competitive and successful. An overview of market orientation and its benefits can be found in Part I.

With the rise of data-driven decision-making and greater technological adoption, competing in many markets may lead to more problems than benefits for an organization's data. Different divisions or departments may collect different data points on different (or even the same) audience members without having any of their datasets talk to each other. One of many results is a distorted understanding of patrons, donors, finances, and more. This fractured data is often a deeper issue than mere computer-literacy, and is addressed in the way an organization addresses and approaches building its own data culture. This article provides overview of what data cultures are and how to measure your own, along with invisible threats to both your data and your mission that can occur when operating with weak or undefined data cultures.

Disempowerment and Hegemony

Funders have long held power over grant recipients, with their data demands determining the practices and approaches of grantees. For example, sociological and linguistic analysis of government grant applications among Belgian artists from 1965 to 2012 reveal shifts in arguments away from reasoning rooted in the aesthetic and romantic to reasoning more centered on entrepreneurial economics, socio-politics, and other extrinsic factors. This shift reflected the growing standardization of the Belgian grantmaking bodies over this time and a shift in viewing artists as social and political agents.

These patterns persist in the 21st-century arena of data. An overemphasis on data-driven work within the nonprofit sector has been shown to result in cycles of disempowerment, and this culture is driven by third-party data demands by the same types of funding bodies.

The Cycle of Disempowerment

Constructed in three stages, the cycle of disempowerment illustrates the way by which funders drive organizations toward certain ends. This begins with an initial erosion of autonomy in the choice of the data collected by nonprofits based on the metrics required of grant reports, regardless of how relevant the metrics are for the organization’s operations.

This compounds into a larger data drift, whereby nonprofits’ entire data gathering focus shifts toward satisfying the demands of funders as opposed to intrinsically-motivated data gathering practices aligned with organizational sustainability and the impact of their mission.

Long-term, this data drift and loss of autonomy results in data fragmentation. By juggling the demands of multiple funders with varied reporting metrics, organizations begin to develop disparate, unsynchronized, homebrew databases. These often clutter an organization’s data culture and drain already-limited time, effort, and resources.

This cycle exposes entrenched power relations between funders and nonprofits, raising questions around the efficacy of broad metricization of the mission-driven social sector. Funders ultimately impose a form of coercive isomorphism upon nonprofits, whereby even the specific data cultures of organizations are shifted toward a framework desired by the funder in power.

Defining Data Cultures

Data culture is not necessarily digital culture; data can be stored and analyzed on post-its, whiteboards, and notebooks. Deeper than simple technological adoption, it is becoming increasingly important to establish cultures informing how organizations collect, manage, and share data. This burden has become particularly important in light of the modern demands on nonprofits to raise funds, communicate program outcomes, and service grant reports in an increasingly competitive landscape.

Deeply related to the concept of general organizational culture, data cultures can be understood in a variety of ways. Researchers Griffin and Holcomb present a dual-axis model, based on a more general competing-values model of organizational culture, through which data cultures can be understood. Called the Usage & Flow Data Culture Model (UFDCM), this framework employs two measurements for a data culture:

The use of data which ranges from use solely for measurement, monitoring, and efficiency-maximization, to a more positivistic use of data for understanding, differentiation, and knowledge-building.

And

The flow of data which his ranges from a restrictive, authoritarian flow whereby data is disseminated on a need-to-know basis, to a democratic free-flow of information.

Together, these axes reveal four distinct types of organizational data cultures: Preservationist, Progressive, Protectionist, and Traditional cultures.

*Source: Griffin & Holcomb, Page 13. 2023*

All data cultures serve a purpose, since not all business areas need to be inundated with every possible data point. It is imperative that an organization establish the proper uses and flows to align departments with the data they need and to drive the strongest data culture possible. Strong data cultures become evident in the consistent deployment of data-informed decision-making practices.

Arts organizations and nonprofits may lie in any of the four cultures, all of which deeply affect how an organization uses, analyzes, and responds to information of all kinds. As an industry, however, the arts have demonstrated slower adoption of data analysis methods when compared with other nonprofits, indicating a high likelihood of weak data cultures across the field.

Challenges In Establishing Data Cultures

This is not to say that arts and cultural organizations have not tried to become more data-driven. The nature of the industry, however, presents some challenges. Common constraints are both budgetary limitations and a lack of time for education and training in data-based software and technology, stymying effective management and analysis. As of 2019, a mere “20% of the surveyed cultural organizations in the UK have adopted a strategic perspective for data collection and analysis”

An additional factor influencing data cultures comes from outsized-influence of executive leadership on the implementation, or cessation, of data and technology policies. The Dallas Museum of Art (DMA), under the leadership of Maxwell Anderson, implemented the DMA Friends program, which offered free membership to expand its audience. Participants received a card that tracked engagement with the museum, including earnable badges, smartphone integration, and social media-engagement incentives. Under this program, a single visits to the museum could generate over 1000 data points, leading to a deluge of data requiring 25-30 staff just to manage the program.

While successful at generating significant data, the program was discontinued after a change in directors in 2017. This dramatic end demonstrates the fragility of data-driven programs on their own and the sheer amount of labor required to manage internal data. Both obstacles require broader institutional changes in strategy and culture to overcome and necessitate that data cultures be recognized and established.

Importantly, the story of the DMA illustrates a larger fear of data-driven decisions across the sector, with many often labeling such efforts as “off-putting to many leaders and managers in ACOs, as they could believe that the use of such language and techniques is the top of a slippery slope leading to a purely commercial, transactional relationship with audiences and the cultural sector becoming just another part of consumer society.”

Therefore, the search for strong data cultures among arts and culture organizations persists, reliant on supportive leadership paired with broader organizational investments in data management and capacity-building. Beth Kantor, a widely published nonprofit consultant, has been a proponent of nonprofits shifting to smart technology and capturing what she calls a “dividend of time” through the time-saving capabilities of such data and tech integrations. This approach to technology adoption as a time-saver, rather than an output-multiplier, frees up staff time to focus on organizational mission and strategy.

Building a Data Culture

Building a data culture from the ground up does not need to be hard, and it can fit within any nonprofit’s budget. Beginning with small behavioral changes such as more openly talking about your data in designated meetings, sharing files and databases, and charting a formal data strategy are all effective ways to plant the seeds for a data culture to grow. Outlining a data strategy detailing how you will collect, manage, and access your data is a preliminary step that sets an organization up for success in establishing itself within one of the four quadrants.

Further efforts may include aiming for a specific quadrant on the UFDCM that aligns with your sector or company and building out roles and responsibilities internally aimed at achieving that type of culture. Establishing an individual or group responsible for data oversight, such as a Chief Data Officer or a data governance office in larger and more mature firms, can be fundamental for reorienting your firm’s data use and flow.

Staff training is another investment an organization can make to improve its data culture and fulfill its data strategy. Investments in data analytics training for staff can improve data literacy and aid in creating a shared data culture throughout an organization. However, this can be costly or beyond the budget of individual nonprofits.

Alternative and collaborative forms of capacity building have been found in civic data hackathons - events where nonprofit organizations may volunteer data to outside analysts (often graduate students or consultants) to help generate novel insights to answer the nonprofit’s data-related questions and goals. Collaborative workshops coordinated with analysts and other nonprofits can also help to establish common literacy among a sector and improve the return on investment.

Conclusions

The need to establish solid organizational data cultures to survive in increasingly data-driven markets has pushed nonprofits to the edge of their capacity. Contending with disparate funder requirements and tight budgets, mission-driven organizations are constantly enmeshed in cycles of disempowerment and data fragmentation in their feverish attempts to swim above the flood.

This tension underpins the rise of the modern data economy among nonprofits and the growing consulting and research industry servicing the field. Research out of the RAND Journal of Economics posits that modern consulting firms can be understood as participating in two markets: one in which they sell their consulting services and another where they sell their data insights gathered from clients. The data economy multiplies across verticals as more forms of isomorphic tension emerge. Present among sector-wide indices and the rise of consulting services and professionalization within the field, forces drive both mimetic and normative isomorphism in addition to and resulting from the initial coercions of funders.

These tensions have placed significant pressure on nonprofits in the grant economy, and risk creating unhealthy dependency of grantees on their funders, particularly among nonprofits which have not invested in creating an internal data culture. When governments cut their budgets or foundations begin sunsetting, dependent nonprofits that have slowly and unwillingly aligned themselves toward success in this market are left facing fiscal cliffs, potentially risking significant programmatic cuts or dissolution entirely.

In conclusion, nonprofits dependent on grant funding are constantly at risk of data drift resulting from the hidden cycle of disempowerment. This compounds due to the many market orientations nonprofits need to adopt, particularly prevalent among the arts. A key to navigating these markets and funder requirements lies in identifying a specific data culture best fit for your organization and investing in resources and training in order to achieve that framework sustainably.

Deeper insights into funder-evaluation metrics, navigating broader data environments throughout the nonprofit arts sector, and the role of the consulting industry on shaping nonprofit data-collection and storytelling are explored in parts to come.

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