Management Information repository at the University of Pennsylvania

 
 
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Three years ago, motivated by the need to improve the measurement of electronic resource use, the Penn library began working with various kinds of transaction data, and in particular Web server logs, as sources of management information. Though narrowly defined in its early stage, this effort at log analysis is beginning to resemble the form and function of an MIS. The following is a brief survey of the embryonic MIS at the Penn library, an aggregation of data files and databases, form interfaces, and Web pages called the library data farm.

Initial Challenges

By 1998, the Penn library offered a large collection of online article indexes, full-text databases, and electronic journals through its Web site. As this quickly evolving, costly set of services expanded, frustrations mounted over the difficulty of measuring its use and impact. The lack of good management information, if only in the form of frequencies or other descriptive statistics, was viewed as a serious shortcoming, one that could hamper the library's accountability to the university's schools and impede planning and budgeting.

For several years, the library staff has worked diligently to compile the few statistics that vendors provide and trace the spiral of Web activity based on Web-site visits and page counts. But these attempts at measurement have had obvious drawbacks. Vendor statistics have been meager, erratic, poorly defined, and incompatible across products. Web measures have provided information about machine load, but have revealed nothing about resource use. Together, the external and internal sources have contributed little clarity to the picture of digital information use. In addition, the compilation of these crudest of measures has proved too time consuming and labor intensive to carry out with any regularity.

Given the absence of third-party solutions or working models, any approach to the management information problem would require a lot of experimentation with local data sources and systems. The approach would have to be independent of information providers and sufficiently robust to generate useful statistics with a modicum of labor. In short, it would have to provide a means of:

1. increasing the resolution of library statistics, especially with regard to demographics and cost analysis;

2. counting with reasonable accuracy and sustainable methods;

3. reducing the level of effort involved in harvesting and converting data into information; and

4. improving the consistency and reliability of data collection and administration.

The key to meeting these objectives exists in the self-monitoring capabilities of the library management system (the Penn LMS is the Voyager product from Endeavor Information Systems) and other components of the information technology (IT) infrastructure, specifically standardized and configurable logging processes.

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What will feed the uptake in data-rich mobile applications? They have to be sort of free, nearly free, or simply free.

What's going to be the business model to make that happen? Although just getting off the ground, expect to see an uptick in mobile advertising.
 

Our expectation of free applications stems from our personal computer experience. We don't pay a dime when we go to the Weather Channel, MapQuest, or traffic.com. And we don't bat an eye when we see the ads that finance these services.

Advertising will eventually drive mass adoption of mobile apps by bringing down or removing app fees paid by the consumer. Talk at the Mobile Marketing Forum and Navigation & Location '08 focused on the intersection of mobile applications, advertising, and location relevance.

I've heard all sorts of numbers; Generator Research found that more than 60 percent of mobile users would be interested in a deal that offered substantial discounts for voice and text, provided that users agreed to received a limited number of ads that were relevant based on a user-defined ad profile. Other studies reveal that location-specific ads or ads that are personalized to a person's interest are the most acceptable and often viewed as useful. Q Research Advertising and Mobiles Report indicates that 76 percent are interested in ads in exchange for discounts or special offers, 82 percent for top-up credit, and 71 percent for "only things I'm interested in."

Location information enables more targeted, less intrusive, and genuinely useful information to be sent to a user.

There are right ways and wrong ways to determine what interests a user. Privacy remains a sensitive issue that will bite those that cross the line. The industry is experimenting with how to best deliver advertising in the United States. Advertisers don't yet know the available inventory of advertising opportunities, so you can expect advertising pricing to go through a number of adjustments as the learning curve develops.

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