Remarks from a LIT-SIS Grant Recipient for Students and Newer Librarians
Written and submitted by Amber Cain
I am the User Experience (formerly Technology and Research Services Librarian) at Seton Hall Law. In my role, I manage access to our databases, and I oversee Digital Commons, Springshare, and EDS. I also troubleshoot library technology issues, handle database registrations, and facilitate library events and programming. I teach Advanced Legal Research courses, and I guest lecture and provide trainings as requested.
I want to first express my sincere thanks to the LIT-SIS Grants and Awards Committee for awarding me the AALL LIT-SIS Grant for Students and Newer Librarians, which greatly facilitated my attendance at the AALL Annual Meeting & Conference in Portland this year. This was my second in-person attendance of the Meeting & Conference. My first actual attendance of the Annual Meeting & Conference was remote in 2020 while I was a law student. I then attended in person for the first time in Boston in 2023, where I presented at the Cool Tools Café on Power Automate.
Having extensively worked with Power Automate, I was thrilled to attend the panel titled, Leveraging Data and Technology in Law Libraries: From Operational Efficiency to Strategic Decision Making. This panel was presented by Qui Hang (Stanford Law School), Erin Vassalotti (Holland & Knight LLP), and Alex Zhang (Duke Law School). They gave a thorough overview of the lifecycle of data analytics: (1) defining the problem; (2) collecting and preparing data; (3) analyzing and modeling: uncovering insights to inform decision-making; (4) visualization and reporting: converting data onto digestible reports; (5) engaging in the ongoing process to monitor, evaluate, and iterate. At every step of the process, they gave tool suggestions at the beginner, intermediate, and advanced levels. The beginner recommendations were generally Excel/Google Sheets; the intermediate recommendations were generally Microsoft Power Platform and Tableau products; and the advanced recommendation was generally Python. For the sake of time, they briefly discussed three specific use cases and how they worked through it with their data, with screenshots of the way they facilitated this with specific technology.
While the presentation was illustrative, I would absolutely love to see a follow-up to this panel (perhaps even a workshop) where speakers go more in depth into the specific technology behind this topic. Specifically (and personally), I would be interested in an advanced demonstration of how Python is being used for data analytics, and I do not imagine that I am alone in this. I think that part of the workshop or deep dive could discuss when to use which level of technology: basic Excel functions vs using Power BI to accomplish the tasks, and Power BI vs Python. While I am familiar with the basics of Python, I would say that I have more experience with the Microsoft Power Platform. However, from what I understand, Python is better for larger datasets and for more advanced analytics, and it also allows for more customization. I think that everyone could benefit from understanding when a tool that might require a higher degree of expertise or is relatively more technologically complicated is useful, but just as (if not more) importantly, when a more advanced/complicated tool may not be necessary to accomplish the task at hand. While this distinction may be somewhat obvious based on the amount of data and the type of data (ex: unstructured data), I still believe there are those that would benefit from a discussion of that, and then a deep dive into how one might accomplish certain data analytics using technology from the more advanced level.
All in all, I had a wonderful time at AALL, and I really enjoyed this panel. Hopefully, someone somewhere who does this type of work with Python sees this and can show us what they do!
