Project MUSE®: Journal of Advancement Analytics - Latest Articles
https://muse.jhu.edu/journal/882
Project MUSE®: Latest articles in Journal of Advancement Analytics.daily12026-05-15T00:00:00-05:00text/htmlen-USVol. 1 (2021) through current issueLatest Articles: Journal of Advancement AnalyticsTWOProject MUSE®Journal of Advancement Analytics2693-44502693-4442Latest articles in Journal of Advancement Analytics. Feed provided by Project MUSE®Utilizing Artificial Intelligence and Machine Learning to Determine a Prediction Analytics Process for Determining Foundation Giving Gift Capacity
https://muse.jhu.edu/article/941468
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Artificial intelligence and machine learning have continued to reshape how fundraising gets done. The AI definition is credited to John McCarthy, an emeritus professor at Stanford University in 1955 and coined during his time at Dartmouth College (Fine & Kanter, 2019). AI was defined as “the science and engineering of making intelligent machines.” Christopher Manning (2020) explained in “Artificial Intelligence Definitions,” a piece from Stanford University Human-Centered Artificial Intelligence, “Much research has humans program machines to behave in a clever way, like playing chess, but, today, we emphasize machines that can learn, at least somewhat like human beings do.” Manning (2020) further defined machine
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Project MUSE®https://muse.jhu.edu/2026-05-15T00:00:00-05:00https://muse.jhu.edu/journal/882/image/coversmallUtilizing Artificial Intelligence and Machine Learning to Determine a Prediction Analytics Process for Determining Foundation Giving Gift Capacity2024-10-26text/htmlen-USUtilizing Artificial Intelligence and Machine Learning to Determine a Prediction Analytics Process for Determining Foundation Giving Gift Capacity2024-10-262024TWOProject MUSE®385092026-05-15T00:00:00-05:002024-10-26Uncovering Actionable Insights for Customer Service Improvement: A Text-Mining Approach to Identify Key Themes
https://muse.jhu.edu/article/941469
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In the rapidly evolving landscape of the digital era, organizations struggle with the daunting task of extracting actionable insights from the ever-expanding, vast amount of textual information (Chakraborty & Pagolu, 2014). In recent times, higher education institutions, in particular, have encountered a significant influx of unstructured text data emanating from many sources—alumni surveys, student/employee feedback, customer service tickets, and the ubiquitous realm of social media. These data, often in the form of open-ended responses, present an arduous task for organizations seeking to extract business intelligence for decision-making. As we at North Carolina State University (NCSU), as an institution
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Project MUSE®https://muse.jhu.edu/2026-05-15T00:00:00-05:00https://muse.jhu.edu/journal/882/image/coversmallUncovering Actionable Insights for Customer Service Improvement: A Text-Mining Approach to Identify Key Themes2024-10-26text/htmlen-USUncovering Actionable Insights for Customer Service Improvement: A Text-Mining Approach to Identify Key Themes2024-10-262024TWOProject MUSE®730432026-05-15T00:00:00-05:002024-10-26Editor’s Notes
https://muse.jhu.edu/article/941470
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When the selection committee and I met to plan for the 2023 symposium in late 2022, we could barely have imagined the revolution in analytics and technology that was about to come to fruition the following year. With the release of ChatGPT, suddenly the world had easy access to powerful AI technology that could be used to create programs, execute analyses, and undertake any number of creative and investigative tasks. Truth be told, AI has been in the vernacular since John McCarthy coined the term in the 1950s. However, 2023 is the year in which we saw the confluence of computational techniques and shear computing power brought about by the cloud computing revolution transform AI into a practical reality for the
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Project MUSE®https://muse.jhu.edu/2026-05-15T00:00:00-05:00https://muse.jhu.edu/journal/882/image/coversmallEditor’s Notes2024-10-26text/htmlen-USEditor’s Notes2024-10-262024TWOProject MUSE®51572026-05-15T00:00:00-05:002024-10-26Deployment and Integration of Machine Learning Methods with Continuous Integration/Continuous Delivery
https://muse.jhu.edu/article/941471
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Across many industries, a paradigm shift from traditional analytics to integrated data science systems is occurring to better inform high-level decision-making. For many years, the primary goal of analytics has been to provide insight on specific reports and dashboards that only required periodic updates. Increasingly, fundraisers are seeking to leverage insights from historical action outcomes to inform day-to-day business processes in near real time, requiring an integrated data science (IDS) approach.At the heart of the IDS approach is crafting a model specifically tailored to optimize fine-scale decision outcomes. In advancement, examples of these fine-scale decisions include which prospects to reach out to or
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Project MUSE®https://muse.jhu.edu/2026-05-15T00:00:00-05:00https://muse.jhu.edu/journal/882/image/coversmallDeployment and Integration of Machine Learning Methods with Continuous Integration/Continuous Delivery2024-10-26text/htmlen-USDeployment and Integration of Machine Learning Methods with Continuous Integration/Continuous Delivery2024-10-262024TWOProject MUSE®312512026-05-15T00:00:00-05:002024-10-26Prospect Management and Data Science: Partnering to Build an Efficient Horizon for Fundraising
https://muse.jhu.edu/article/941473
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Prospect research as a profession evolved from the competitive intelligence field. It began in fundraising offices in an organic manner. In the early days, fundraisers would ask their assistants if they could look up some information on a prospect to prepare for a visit. The assistants would then scour through files, newspapers, and business journals; they would call tax assessor’s offices and research librarians at well-resourced libraries and other organizations to find or verify information about the prospects.When internet resources came into existence, some were free and many were pay-per-search. The assistants, many of whom were evolving into the first prospect researchers, had to know their spending limit so
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Project MUSE®https://muse.jhu.edu/2026-05-15T00:00:00-05:00https://muse.jhu.edu/journal/882/image/coversmallProspect Management and Data Science: Partnering to Build an Efficient Horizon for Fundraising2024-10-26text/htmlen-USProspect Management and Data Science: Partnering to Build an Efficient Horizon for Fundraising2024-10-262024TWOProject MUSE®452382026-05-15T00:00:00-05:002024-10-26Adopt DataOps to Support Development of Sophisticated Analytics Programs
https://muse.jhu.edu/article/941474
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Machine learning has unlocked value for companies across industries. The hype of machine learning has driven many to attempt to replicate that success in their own fields. But often the fundamentals that enable successful machine learning are overlooked. The companies that have found success in developing sophisticated analytics programs owe their success in part to adopting best practices such as code quality control and automation. This paper examines what those best practices are and how a typical fundraising office can recreate them.The value of applying machine learning in a fundraising context is clear: empower gift officers to have more meaningful relationships with more prospects. But realizing that value
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Project MUSE®https://muse.jhu.edu/2026-05-15T00:00:00-05:00https://muse.jhu.edu/journal/882/image/coversmallAdopt DataOps to Support Development of Sophisticated Analytics Programs2024-10-26text/htmlen-USAdopt DataOps to Support Development of Sophisticated Analytics Programs2024-10-262024TWOProject MUSE®337552026-05-15T00:00:00-05:002024-10-26The Power of Insights: Leveraging Data Analytics to Drive Business Value and Performance
https://muse.jhu.edu/article/941475
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The motivation behind this exploration stems from the growing need for data analytics specialists and understanding the significance of data as a strategic asset for organizations across industries. Understanding and effectively managing this asset has become paramount for staying relevant and achieving success in a rapidly evolving business environment.This paper aims to provide guidelines for organizations seeking to optimize their data management processes, foster a robust data governance framework, leverage analytics effectively, and ultimately embed a data-centric culture within their operations. By adopting the principles outlined herein, organizations can harness the full potential of their data to drive
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Project MUSE®https://muse.jhu.edu/2026-05-15T00:00:00-05:00https://muse.jhu.edu/journal/882/image/coversmallThe Power of Insights: Leveraging Data Analytics to Drive Business Value and Performance2024-10-26text/htmlen-USThe Power of Insights: Leveraging Data Analytics to Drive Business Value and Performance2024-10-262024TWOProject MUSE®386752026-05-15T00:00:00-05:002024-10-26