Today’s technologies pull data from everywhere and it is the proper understanding and the use of insights from the data that can keep nonprofits on the right track when it comes to organizational efficacy and sustainability.
But where to start? Luckily, Carnegie Mellon’s AMT Lab has posted a helpful guide.
Bria Blackshear wrote this simple guide to data analytics for nonprofits published in Carnegie-Mellon University’s AMT Lab:
In general, data analytics refers to the collection of internal and external information about quantifiable metrics that relate to an organization’s performance strategies and tactics for future success. Analysis tools can be used by arts managers to find improved ways to reach and appeal to desired audience segments. The extent to which data analytics can inform an organization is vast and perhaps overwhelming at times; however, this means that arts managers can tailor their analysis to measure only the metrics that are relevant to their mission and programming. In fact, the majority of nonprofits use data analytics to track crucial financial and operations metrics in order to make budgetary decisions for future programming.
Indeed, the core reason for implementing data analytics practices should be for the expansion of the organization’s mission: “Tracking program and outcome-related data should be the bread-and-butter for nonprofits because it’s one of the best ways to articulate what they are delivering and the extent to which they are delivering on their mission.” It is of critical importance that arts organizations focus on measuring the data that is directly relevant to desired effects and impacts in their respective cultural communities—that is, focus on the information that will yield the highest level of optimization for the goals at hand.
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In terms of the types of analysis that can be done, organizations can tailor the output based on the overarching result they are seeking (i.e. mission advancement, programming expansion, donor acquisition, etc.). Statistical analysis incorporates regression and frequency models to make new predictions based on prior donor and patron behavior. Arts organizations can create a model with accurate numeric data and plug in various inputs to understand how different scenarios might play out.
Forecasting is a type of analysis that tells organizations about when resources will be needed and how much. For example, a financial director might use forecasting to understand how budgets should be allocated given the current monetary trends within the organization. A more simplistic, yet highly valuable analysis is known as segmentation or descriptive analysis, which divides data sets (audiences/donors) into groups so that organizations can understand the variations among their constituents.
Organizations that wish to hypothesize about future audience and patron behavior should implement predictive modeling, which uses a combination of historical and comparative data sets. In keeping mission-related goals in the forefront of operations, arts managers would benefit greatly from employing optimization analysis.
