This week, we look at how technology is shaping the nonprofit sector and how to prepare for the biggest charitable giving months of the year. We highlight the need for high-quality, always-available, curated data for artificial intelligence (AI) and machine learning (ML), and how to avoid garbage data. And finally, we examine a case study of how one grocery chain has successfully executed a digital transformation.
by Mike Liddell and Amanda Coulumbe, featured on EveryAction.com
This informative white paper recaps some of the biggest nonprofit technology trends that emerged in 2018. It also explores how they will continue to impact the market in 2019 and predicts other developments on the horizon. Find out the steps that nonprofits should be taking this year to ensure that they are positioned for success in 2019 and beyond.
by Jaspreet Singh, contributing writer for InformationWeek.com
There is a symbiotic relationship between data and artificial intelligence. Data creates the foundation of a successful implementation. AI and ML require both high-quality data and an infrastructure that ensures data is always available. Additionally, in order for ML to have an impact, the data needs to be curated. And as you might guess, this is no small task.
by Tracy Vanderneck, contributing writer for NonProfitPro.com
The majority of charitable donations are made within the last three months of the year. And Giving Tuesday is just around the corner, on December 3rd. Now is the time to plan for the fundraising and communications activities for the remainder of the year. This helpful article gives you things to think about and ways to immediately make an impact.
by Shashin Shah for MarTechAdvisor.com
Improving business with digital growth is an area that is proving impactful to almost all industries. And the grocery industry is no exception, however, this transformation is often slower to take shape. But many legacy grocers are working to bring their online offerings up to speed. Read about how Northgate Market has successfully navigated digital transformation.
featured on SPGLobal.com
The quality of data goes a long way to determining the quality of your machine learning output. Garbage in = garbage out. “Garbage” includes poorly labeled or inaccurate data, data that reflects underlying human prejudices, and/or incomplete data. Identifying “garbage” in your output requires both a general skepticism when evaluating results and knowledge of best practices in data science. Here are some considerations to take into account in order to avoid and handle garbage data.
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