Call for Papers

In advance of its third annual meeting, the Texas Advancement Analytics Symposium is extending a call for papers based on the theme of “The Democratization of Analytics”.

Submit a Proposal

The Democratization of Analytics: How the Confluence of Tools, Data Literacy, and Organizational Maturity are Changing the Analytics Landscape


The democratization of analytics is the process of making analytics available to an entire organization,  or more precisely, it is “the ongoing process of enabling everybody in an organization, irrespective of their technical know-how, to work with data comfortably, to feel confident talking about it, and as a result, make data-informed decisions and build customer experiences powered by data.”[1] In both the public and private sectors this is a concept that resonates as organizations look to implement self-service analytics.

This democratization is made possible by maturing organizations adopting easy-to-use tools to accomplish previously sophisticated operations on increasingly integrated data. The cycle-time of data to action is decreasing as automated real-time analytics are allowing institutions to engage with their constituents in more prescriptive ways, informing customized journeys, messaging, and engagement to connect people, passions, and priorities.

Likewise, the modernization efforts enabling this process are allowing new questions to be asked and answered across advancement programs. These questions challenge the way we perceive ourselves and the way we conduct the business of advancement. Asking questions of efficiency, capability, and diversity highlights gaps in processes, constructs, or information and spurs a broadening of our efforts to engage all our constituents in the way they wish to be engaged.

The agents are also changing. The expansion of data literacy as a core competency and the emerging culture of data driven decision making are seeing analytics set free of the core analytics team and made available to anyone with an interest in engaging with data. The information and analysis required to make decisions is extending beyond senior leadership circles. This expansion does not come without its challenges, and questions of authority, actionability, accuracy, and significance are important to understand and address. Instead of looking at every piece of data, what are the key pieces of data that best inform our actions to produce desired outcomes? And now that information is being set free, how is the decision-making authority being delegated?

The Texas Advancement Analytics Symposium invites you to consider this emerging landscape with both its benefits and potential challenges and to submit paper proposals in response to the above stated theme. Proposals are due no later than Friday, January 21st, 2022. Accepted submissions will be presented at the virtual symposium in June of 2022 with final papers published in the Journal of Advancement Analytics (JAA), Volume 3. We welcome proposals that address both theoretical and practical applications of analytics in advancement, including case studies.



Submission Criteria

Proposals should be submitted as a .DOCX file. They should contain:

  1. An abstract between 250-500 words in length
  2. A paper outline 1-2 pages in length

For questions on potential proposal topics and for submission guidance please reach out to Rachel Veron (


Evaluation Criteria

Abstracts will be evaluated based on the following criteria:

  • The originality of ideas/approach and level of innovation
  • Relevance for advancement analytics and the defined theme
  • Presentation: coherence of clarity of structure and thought
  • Contribution to advancement analytics theory/solution building



To submit, please email your proposal using the button above or to Submissions must be received no later than Friday, January 21st, 2022 to be considered.

Submissions must NOT have been previously presented, scheduled for presentation, published, accepted for publication, and if under review, must NOT appear in print before the symposium.