data-informed 
Association Analytics
APRIL 1, 2024
You’ll come away with specific examples from the team at the International Association of Exhibitions and Events (IAEE) that you can use to inform your own strategy. Once the team aggregated the data and they were able to form a hypothesis, test it and confirm a need.
NonProfit PRO
OCTOBER 1, 2022
Nonprofits that fail to protect sensitive information risk exposing their donors’ personal information, and losing their trust and financial support along the way. But proactive steps — including data devaluation — keep valuable information out of bad actors’ hands and prevent data compromise.
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Association Analytics
JANUARY 11, 2021
As part of their mission to serve their members, NCACPA has been focused on analyzing their member data to influence decisions that impact the member experience. They’re dedicated to monitoring changes within the profession and ensuring members receive necessary resources and updates.
Candid
JANUARY 24, 2024
I’ll share what I’ve learned from the big-picture analytics, insights from my deep dive into content, and how they inform my approach to our social media plan (which you’re welcome to borrow for your nonprofit). We get it, who doesn’t love data presented in a nice visual format? But our data doesn’t lie—you love them!
Beth's Blog: How Nonprofits Can Use Social Media
FEBRUARY 21, 2013
One of the questions I was asked, ”Why Data Informed? Why Not Data-Driven?” ” Being data-informed is something very different from a data-driven culture. The term “data-driven” has been used to describe organizations that rely solely on cold hard data to make decisions.
VolunteerMark
MAY 5, 2021
In the era of “hacking” and information being easily accessible on the internet, it’s no surprise that people are ever more concerned about privacy and information security, especially at nonprofits. The post Keeping Volunteer Information Secure from Cyber Theft & Data Leaks appeared first on VolunteerMark Blog.
Nonprofit Tech for Good
APRIL 21, 2024
Machine Learning (ML): A subset of AI that enables systems to learn from data and improve their performance without being explicitly programmed. Supervised Learning: ML technique where algorithms learn from labeled data to make predictions or decisions. Data Bias: Prejudice or skewed results in AI systems due to biased training data.
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