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Tech Decision Makers
Women in Nonprofit Tech
Nonprofits and Data
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This group is for those interested in learning and sharing about all things data-related for nonprofits.
The Nonprofits and Data group is for people using data to serve a mission, either directly or by improving nonprofits and the nonprofit sector. That includes everything from collecting data and managing databases to analytics, data visualization and data mining. Here are some examples of topics we discuss: using data to improve organizational effectiveness, measuring impact, using data for storytelling, tools for data management and analysis, figuring out the “right” data to collect, and learning skills to help us use data better.
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Notes from our January Community Call
Notes from our January Community Call
Posted Feb 03, 2019 12:03
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Below are the notes from our January call,
Preparing Data for Sharing and Visualization
. You can access the
entire collaborative notes document, which contains two examples of nonprofit data communication materials
. We had 28 participants.
Stay tuned for information on our next call.
What percentage of data collected are specific metrics you actively track vs. from platforms that passively collect data (and then you pull out the most pertinent information for reporting purposes)? Pros/cons/pitfalls to avoid?
Danger of second hand data that isn't gathered intentionally. Applying standards after the fact on already collected data.
Clarification on definition of "second hand data": data collected for another purpose.
The data we have to work with is the data that's feasible to collect, which often was done for some other purpose. That's common.
Is it possible to piggyback the existing data collection process? Adding another question to an existing form, for example. +1 (Janice)
Inherited data - staff turnover, no documentation on how data was collected → Crucial to document what is collected, where/how it is collected, and the formula for adding them up
Context for what the data actually means. Bad or missing descriptions of the data can be confusing. (Stephanie)
Examples of problems caused
Difference in counting from financial vs. development standpoint
Sometimes definitions of data are intentionally different based on the context and source of the data. Trying to cross-report between such systems causes problems.
Has anyone used data visualization as a way to have conversations about different perspectives? Building on above point where finance and development have different needs and definitions.
Building out CRM dashboards for each dept based on their own needs had the side effect of showing that KPIs were different for each department. EX "Lapsed member" classifications different across departments. Helped break down some silos.
Example where website needed each org to have 1 executive director; deviations were problems. Setup a report to alert staff whenever there was 0 or 2.
Do you collect data entered manually by volunteers? How do you ensure the quality of the data? How do you ensure the data is what you think it is?
Budget time for data discovery, validation before creating a dashboard - talk to people, find out what data governance exists
Often find that there isn't a good data governance structure unless there's a reporting culture in place
For example, report about opportunities to speak/engage with us - who counts as a speaker? → Need to talk it out with the data in front of us; will take more time than you think
Do you blend data from multiple sources? How do you make sure you're comparing apples to apples?
Data from vendors/external sources?
If no way to compare data from different sources (no control over/understanding of data governance and provenance), keep it separated and measure against itself for consistency controls.
Document where data is coming from (if multiple systems your own), how you got it, etc. so you can tell whether you can truly combine it or if it should be kept separate
If third party vendor but know what event they attended, and have them in the CRM → can get an updated e-mail address or identify a spouse; review before importing (can clean up, can select only records that you consider a match)
Director, Technology and Digital Strategy
Food and Drug Law Institute
Community Organizer, NTEN Nonprofits and Data group.
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