During our recent internal Programs and Research summit meeting, we re-visited various portions of the PAR work agenda, looking for opportunities to add, revise, or perhaps even delete. I participated in discussions having to do with the Management Intelligence section of the agenda, which covers work aimed at gathering, mining, and analyzing data sources in support of library decision-making and context-setting needs. Management Intelligence extends over a wide range of current and prospective projects in PAR, but the scope and rationale of this work can be summarized with a couple of simple themes: Aggregate – Analyze – Generalize, which summarizes the sort of work we do in Management Intelligence; and Context – Evidence – Patterns, which summarizes why we do it.
1. What we do: Aggregate – Analyze – Generalize
Aggregate: The more institutions, collections, and individuals over which we aggregate data, the richer the context against which decision-making can be placed; issues can be characterized and understood; and patterns can be discerned and extrapolated. Much of PAR’s data-mining work flows around the aggregated bibliographic and holdings data in WorldCat. But our WorldCat-based work will be complemented with a new emphasis on aggregating other forms of data, such as circulation data, ILL transactions, virtual reference queries, click-through patterns, and e-usage data.
Analyze: Value is released from data by analyzing and leveraging it in innovative ways that support a variety of needs. Management Intelligence will prioritize work aimed at providing our Partners with the information and evidence required to support the directions in which they are moving, in areas such as digitization, shared print storage, and deeper forms of collaborative collection management.
Generalize: An important aspect of the work in Management Intelligence will be to identify and pursue opportunities to change library practice and improve existing services and processes. In pursuing these goals, we will prioritize forms of analysis that can be “generalized” into standard methodologies applicable across a variety of contexts – for example, by converging on sets of standard questions to be asked of the data in particular decision-making scenarios.
2. Why we do it: Context – Evidence – Patterns
Context: Cultural heritage institutions must endeavor to understand, and where appropriate, seize, opportunities created by trends, technologies, and other factors shaping the information environment. Therefore, an area of priority will be to pursue work that supplies empirical context for a range of general issues impacting libraries, archives, museums, and the wider information landscape. Such work will inform community-wide dialog on these issues, and help participants channel discussions in productive directions.
Evidence: Decision-making is increasingly data-driven. As more and more library services and usage migrate to online environments, the ease with which data can be captured, aggregated, and leveraged to support decision-making and planning will only increase. Looking ahead, we will prioritize work aimed at cultivating an “evidence-based” approach to library decision-making. In doing so, we will address issues like characterizing what an “evidence base” looks like in various library decision-making contexts, and developing clusters of questions that draw on well-defined evidence bases to inform key decision-making processes.
Patterns: As we collect, aggregate, and analyze data about library collections and user behavior, patterns begin to emerge, illuminating the shape of aggregated collections, the research and learning habits of library users, as well as other features of the overall library landscape. As these patterns emerge, we gain a better understanding of the system-wide characteristics of library collecting and usage activity. This intelligence can inform libraries’ thinking on ways to optimize the system-wide supply and demand for library materials, and in particular, how to reduce supply-side cost while improving demand-side accessibility.
Since our Programs colleagues joined us last year, the opportunities for work in the area of Management Intelligence have expanded dramatically. It is sometimes difficult to draw together all the strands of current and future work being undertaken in this area. However, the dual themes of Aggregate – Analyze – Generalize, and Context – Evidence – Patterns are a useful way to think of this work as a cohesive whole, as well as a roadmap for the kinds of work we will be prioritizing in the future.
Brian Lavoie is a Research Scientist in OCLC Research. He has worked on projects in many areas, such as digital preservation, cooperative print management, and data-mining of bibliographic resources. He was a co-founder of the working group that developed the PREMIS Data Dictionary for preservation metadata, and served as co-chair of a US National Science Foundation blue-ribbon task force on economically sustainable digital preservation. Brian’s academic background is in economics; he has a Ph.D. in agricultural economics. Brian’s current research interests include stewardship of the evolving scholarly record, analysis of collective collections, and the system-wide organization of library resources.