Equitable and Sustainable Models in Statistical and Data Science Consulting and Collaboration

Measuring success plays a central role in justifying and advocating for a statistical or data science consulting or collaboration program (SDSP) within an academic institution. We present several specific metrics to report to targeted audiences to tell the story for success of a robust and sustainable program. While gathering such metrics includes challenges, we discuss potential data sources and possible practices for SDSPs to inform their own approaches. Emphasizing essential metrics for reporting, we also share the metric gathering and reporting practices of two programs in greater detail. New or existing SDSPs should evaluate their local environments and tailor their practice to gathering, analyzing and reporting success metrics accordingly. This approach provides a strong foundation to use success metrics to tell compelling stories about the SDSP and enhance program sustainability. The area of success metrics provides ample opportunity for future research projects that leverage qualitative methods and consider mechanisms for adapting to the changing landscape of data science. 

Clinical and academic research continues to become more complex as our knowledge and technology advance. A substantial and growing number of specialists in biostatistics, data science and library sciences are needed to support these research systems and promote high-calibre research. However, that support is often marginalized as optional rather than a fundamental component of research infrastructure. By building research infrastructure, an institution harnesses access to tools and support/service centres that host skilled experts who approach research with best practices in mind and domain-specific knowledge at hand. We outline the potential roles of data scientists and statisticians in research infrastructure and recommend guidelines for advocating for the institutional resources needed to support these roles in a sustainable and efficient manner for the long-term success of the institution. We provide these guidelines in terms of resource efficiency, monetary efficiency and long-term sustainability. We hope this work contributes to—and provides shared language for—a conversation on a broader framework beyond metrics that can be used to advocate for needed resources. 

In operating an academic statistical consulting center, it is essential to develop a strategy for covering the anticipated costs incurred, such as personnel, facilities, third-party data, professional development, and marketing, and for handling the revenues generated from sources such as university commitments, extramural grants, fees for service, internal memorandums of understanding, and consulting courses. As such, this presentation will describe each of these costs and revenue sources in turn, discuss how they vary over phases of a project and life cycles of a center, provide a review of both historical and modern perspectives in the literature, and give illustrative examples of financial models from three different institutions. These points of consideration are meant to inform consulting groups who are interested in becoming either more or less centrally structured. 

Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Partnerships are needed for a thriving DSU as an active part of the larger university network. This presentation will provide guidance with examples, summarized as six rules for identifying, developing and managing successful partnerships for DSUs: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, and can be adapted to different organizational models for DSUs.