NONPROFIT DATA
Everything you need to know about data. In plain language.
DATA
Definition: Data is the facts, figures or information allow us to draw conclusions. Data is often numerical but they can also be things like words, sounds, symbols, and images.
Examples:
program participant first/last names
donor information
donation amounts, etc.
Importance: like in any industry, data is useful in helping us better understand our work to make sure we get the highest quality results. This is never truer than when our missions are to help marginalized communities with, often, scarce resources like staff, money and time.
INPUTS
Definition: resources you provide to particular programs or processes in your nonprofit.
Examples:
Money
Staff
Volunteers
Space
Partnerships
Consultants, etc.
Importance: collecting and understanding the resources you put into a program helps you effectively plan and evaluate that work in order to help maximize it.
OUTPUTS
Definition: an output is what your programs or processes of your nonprofit produce, typically represented as a number and/or percent. They are easy to collect and produce but don’t mean that a program or process is successful.
Examples:
Numbers of meals
Number of people served
Hours of programming
Locations of programming
Jobs created, etc.
Importance: like inputs, outputs are often the first level of data that funders request in their reports. They are important for showing a funder(s) that the money provided was used to execute on a commitment that was made during the request for funding.
DATA COLLECTION
Definition: the process of gathering/collecting specific facts, figures and/or information within a system. The system can be online (digital) or offline (paper-based) and the goal is to collect accurate data so that it can be analyzed to answer questions and evaluate/assess results.
Examples:
Program registration
Completing surveys
Filling out forms
Video recordings
Importance: data is becoming increasingly more important in society and that’s no exception for nonprofits. More funders are looking to data to help define whether or not the funding provided was successful in what it was intended to do. If we’re not successfully collecting and storing this information, it will become more and more difficult to attract and retain funding for our work.