Information Management And The State Of The Animals
Faunalytics executive director, Che Green, weighs in on animal advocacy in the information age and “The State of the Animals”
The following text is excerpted from my article entitled “Animal Advocacy in the Age of Information,” which is the first chapter in the latest State of the Animals volume from Humane Society Press (HSP). To download a copy of the full publication, which also includes nine other interesting chapters on important topics, please visit the HSP website.
Information management involves the collection, creation, storage, distribution, and utilization of data for a specific and defined purpose. It is not simply a database or an intranet and, in fact, does not necessarily involve technology at all, although technology can be instrumental in helping to facilitate the process. Information management systems are critically important both within individual organizations and between groups with similar purposes, such as those working for animal protection. In general, the scope of this chapter pertains to shared information, with some emphasis on data that are relevant to the entire animal protection movement rather than proprietary or relevant to a single organization.
To assist the information management process, I have developed an overall framework for categorizing and prioritizing information and research for animal-advocacy purposes. The framework includes “research categories” based on the different relationships between animals and humans and several “data types” for each category. I also provide more than fifty references to good sources of information that may be used as starting points for finding relevant data. I use these and other sources to provide an overall assessment of the availability of information by category and data type. Finally, this chapter also includes a set of recommendations for individual groups and the movement overall regarding how to choose research priorities as well as generate and share important information more effectively.
Why Do Animal Advocates Need Research?
Making a significant difference in the lives of animals is predicated on the ability to access and interpret reliable information about how society sees and uses them. Without access to accurate data to determine effective campaign messaging and measure their performance, for instance, animal advocates operate in a virtual vacuum. Perhaps even more important, in most cases animal advocates do not engage in the behavior they are trying to change in other people (the target audience). For this reason and due to other inherent biases, advocates simply cannot rely only on their own perception of why the target audience thinks or behaves the way it does. Similarly, they cannot evaluate their impact on attitudes and behavior using only their hunches and anecdotal evidence. For many it has just been too long since they have walked in the suede shoes of those they hope will switch to pleather.
Information is the basis of informed decision making. Indeed, no animal protection campaign or project should begin without first identifying and analyzing the available data on the topic or issue and, where the information is not available, collecting new data to support critical decisions. Detailed and reliable data, obtained through research, have played an important role in many successful animal-related projects and campaigns; below are a few examples.
- In New Hampshire P. Marsh, of Solutions to Overpopulation of Pets, collected and analyzed shelter intake and euthanasia data to determine the state’s primary sources of “surplus” animals: low-income residents. Using these data, the group was able to create a publicly funded and highly targeted spay/neuter program for these low-income individuals. Ongoing research and tracking of shelter data indicates that the program led to a 77 percent decline in the state’s euthanasia rate over an eight-year period (Marsh 2005).
- In New York City and Washington, D.C., The Fund for Animals conducted focus groups with fur garment owners and teenage females to test its anti-fur advertising. The qualitative research clearly showed that two of the Fund’s prototype ads—one featuring a rabbit and the other a chinchilla—did not elicit nearly as much sympathy as ads featuring a young bobcat and a fox cub. The results were used to create a more effective campaign with ads in Teen People and Seventeen magazines (Green 2004).
- Ohio-based Stop Animal Exploitation Now (SAEN) conducts detailed audits of the National Institutes of Health (NIH) database to estimate taxpayer funding of animal research. The group says that in 2005 the U.S. government gave $12 billion in funding for animal experimentation, an increase of nearly $7 billion over ten years earlier. SAEN uses the research data to help persuade policy makers that animal experiments are wasteful by combining them with details of duplicative research protocols from the NIH database (Budkie 2005).
These are just a few of instances where research-driven data have been instrumental in helping animals. Effective information management can also help animal advocates level the playing field with animal-related industries and corporations, for which “data mining” (involving a detailed quantitative analysis about consumer traits, attitudes, and purchase behaviors) is all the rage. Advocates may not have resources comparable to corporations’ to devote to information management, but in this area a small investment can reap significant rewards. In most cases it is inexpensive (although perhaps time-consuming) to collect and analyze all of the publicly available data on an issue. When animal advocates need to collect primary data because there is little or no existing research, a host of inexpensive and do-it-yourself research methods can often be used.
Knowing What Animal Advocates Need to Know
The breadth of information that is potentially useful to animal advocates is nearly overwhelming. It includes various types of animal demographic and “usage” data, “public opinion” data, consumer behavior research, economic data, and so on. Advocates need all of these data and more for the full range of animal protection issues, including primarily companion animals, farmed animals, research, and wild and exotic animals. Any system designed to manage the information must be comprehensive (or nearly so) regarding the types of data and animal issues covered and organized in mutually exclusive categories. In this chapter, I propose a framework that may be used to categorize and prioritize the types of data that advocates need to improve and evaluate their work.
Prioritization of the most necessary and practical information is essential. For some animal protection issues, there are very few data (e.g., the number of actual vegetarians and their motives), and it is necessary to carefully pick and choose the most strategic areas for conducting new research. For other animal issues, advocates have access to significant information (e.g., demographics of companion animal “ownership”), in which case the priority may be to figure out where to begin analyzing and interpreting the data. Once the initial framework is developed (see the next section), an information management system can help us understand and keep track of which data are known (and which aren’t). In all cases animal advocates’ knowledge is much improved by having a continuous historical perspective, so data collection must also be an ongoing effort.
A Proposed Framework for Animal-Related Data
Information is a source of learning. Unless it is organized, processed, and available to the right people in a format for decision making, however, it is a burden, not a benefit
- (Pollard, 2000).
A framework for organizing information of value to animal advocates must be comprehensive, as already mentioned, but it must also be as pragmatic and useful as possible. In this chapter, I recommend two general bases for data classification: 1) research categories and 2) data types; these are described in detail in the following sections. I also briefly discuss the most likely sources of information for each data type. The framework I suggest in this chapter is intentionally oversimplified to meet the goals of practicality and comprehensiveness, but it has the potential for significantly more detail. In the future the framework can be defined in much more granular terms, including multiple subcategories for each research category and subtypes for each data type.