Action AnalyticsPosted: March 5, 2012 | Author: Gwen Evans | Filed under: administration, change, data, technology, what-if | Tags: analytics, data, students, what-if? | Comments Off
What is Action Analytics?
If you say “analytics” to most technology-savvy librarians, they think of Google Analytics or similar web analytics services. Many libraries are using such sophisticated data collection and analyses to improve the user experience on library-controlled sites. But the standard library analytics are retrospective: what have users done in the past? Have we designed our web platforms and pages successfully, and where do we need to change them?
Technology is enabling a different kind of future-oriented analytics. Action Analytics is evidence-based, combines data sets from different silos, and uses actions, performance, and data from the past to provide recommendations and actionable intelligence meant to influence future actions at both the institutional and the individual level. We’re familiar with these services in library-like contexts such as Amazon’s “customers who bought this item also bought” book recommendations and Netflix’s “other movies you might enjoy”.
Action Analytics in the Academic Library Landscape
It was a presentation by Mark David Milliron at Educause 2011 on “Analytics Today: Getting Smarter About Emerging Technology, Diverse Students, and the Completion Challenge” that made me think about the possibilities of the interventionist aspect of analytics for libraries. He described the complex dependencies between inter-generational poverty transmission, education as a disrupter, drop-out rates for first generation college students, and other factors such international competition and the job market. Then he moved on to the role of sophisticated analytics and data platforms and spoke about how it can help individual students succeed by using technology to deliver the right resource at the right time to the right student. Where do these sorts of analytics fit into the academic library landscape?
If your library is like my library, the pressure to prove your value to strategic campus initiatives such student success and retention is increasing. But assessing services with most analytics is past-oriented; how do we add the kind of library analytics that provide a useful intervention or recommendation? These analytics could be designed to help an individual student choose a database, or trigger a recommendation to dive deeper into reference services like chat reference or individual appointments. We need to design platforms and technology that can integrate data from various campus sources, do some predictive modeling, and deliver a timely text message to an English 101 student that recommends using these databases for the first writing assignment, or suggests an individual research appointment with the appropriate subject specialist (and a link to the appointment scheduler) to every honors students a month into their thesis year.
But should we? Are these sorts of interventions creepy and stalker-ish?* Would this be seen as an invasion of privacy? Does the use of data in this way collide with the profession’s ethical obligation and historical commitment to keep individual patron’s reading, browsing, or viewing habits private?
Every librarian I’ve discussed this with felt the same unease. I’m left with a series of questions: Have technology and online data gathering changed the context and meaning of privacy in such fundamental ways that we need to take a long hard look at our assumptions, especially in the academic environment? (Short answer — yes.) Are there ways to manage opt-in and opt-out preferences for these sorts of services so these services are only offered to those who want them? And does that miss the point? Aren’t we trying to influence the students who are unaware of library services and how the library could help them succeed?
Furthermore, are we modeling our ideas of “creepiness” and our adamant rejection of any “intervention” on the face-to-face model of the past that involved a feeling of personal surveillance and possible social judgment by live flesh persons? The phone app Mobilyze helps those with clinical depression avoid known triggers by suggesting preventative measures. The software is highly personalized and combines all kinds of data collected by the phone with self-reported mood diaries. Researcher Colin Depp observes that participants felt that the impersonal advice delivered via technology was easier to act on than “say, getting advice from their mother.”**
While I am not suggesting in any way that libraries move away from face-to-face, personalized encounters at public service desks, is there room for another model for delivering assistance? A model that some students might find less intrusive, less invasive, and more effective — precisely because it is technological and impersonal? And given the struggle that some students have to succeed in school, and the staggering debt that most of them incur, where exactly are our moral imperatives in delivering academic services in an increasingly personalized, technology-infused, data-dependent environment?
Increasingly, health services, commercial entities, and technologies such as browsers and social networking environments that are deeply embedded in most people’s lives, use these sorts of action analytics to allow the remote monitoring of our aging parents, sell us things, and match us with potential dates. Some of these uses are for the benefit of the user; some are for the benefit of the data gatherer. The moment from the Milliron presentation that really stayed with me was the poignant question that a student in a focus group asked him: “Can you use information about me…to help me?”
Can we? What do you think?
* For a recent article on academic libraries and Facebook that addresses some of these issues, see Nancy Kim Phillips, Academic Library Use of Facebook: Building Relationships with Students, The Journal of Academic Librarianship, Volume 37, Issue 6, December 2011, Pages 512-522, ISSN 0099-1333, 10.1016/j.acalib.2011.07.008. See also a very recent New York Times article on use of analytics by companies which discusses the creepiness factor.