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Demographics via Candid: A data-driven approach to encourage equitable funding practices

Candid

By better understanding the demographic profile of those organizations that are—and are not—receiving funding, we can more closely track if they are representative of the communities they serve and whether they are getting the support they need to deliver on their missions. Introducing Demographics via Candid.

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Reimagining grantmaking with data collaboration at scale 

Candid

PEAK Grantmaking’s “Reimagining Philanthropy” conference in March exploring these themes was a fitting setting for a session Candid organized about advancing systems change via data collaboration at scale. As a grantmaking public charity, United Way of Massachusetts Bay is both a fundraiser and a grantmaker with a place-based focus.

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Are you getting the most out of your retention data?

Association Analytics

Many factors are important to consider: tenure, type of membership, demographics, how the member was acquired, how the member is engaging with your association, and more. Analyzing this data is challenging as it typically resides in multiple systems (e.g. The post Are you getting the most out of your retention data?

Retention 169
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6 Tips to Transform Your Dirty Data in 2024

Association Analytics

We likely don’t need to convince you that in today’s digital age, data is the lifeblood of any successful association. And you’re likely to agree that ensuring the cleanliness and accuracy of that data is key. But what happens if your data isn’t clean? Data should be directional Your data may be disorganized.

Data 169
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Data to support the relentless pursuit of racial equity 

Candid

Based on Candid data processed as of October 2023, approximately 78,133 grants valued at $16.8 My many years of experience collecting and analyzing data as an evaluator naturally lead me to ask: What has been the measurable impact of this important shift? The best way to answer these questions is to measure and analyze consistent data.

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Are you getting the most out of your retention data?

Association Analytics

Many factors are important to consider: tenure, type of membership, demographics, how the member was acquired, how the member is engaging with your association, and more. Analyzing this data is challenging as it typically resides in multiple systems (e.g. Demographic/Career Info. Schedule a demo to learn more.

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Don’t Fear Your Dirty Data

Association Analytics

The concept of “dirty data” and how to approach it can be daunting. Simply put, dirty data is data that is inaccurate, incomplete, inconsistent, duplicative, or outdated. At Association Analytics, we sometimes hear concerns about data quality in the context of associations starting their journey into analytics.

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