Data-driven decision-making & evidence based practice

Gathering and using quality data to drive services is time consuming and difficult. Even after years in research and 4-5 years trying to apply the principles of data-driven decision making, this process still feels slow and fraught with uncertainty. This is one indication that my typical inclination to push forward really needs to be interrupted so I can slow down, reflect, and think methodically about things. I know people who trust their guts and their brains to retain and recall the crucial stuff when they need it. I wish I could be like that, but I know just enough about cognitive psychology (in part, thank you Kanhneman & Tversky) to doubt myself. My brain is lazy, takes lots of shortcuts, and often adjusts perceptions and memories to reflect what I want to see and remember. Recently, I’ve been doing several things that demand that I slow down to make sure I am asking the right questions, all so I can extract reasonable answers from the information I have. It feels like the process is taking forever when my time remaining in the tenure track gets shorter by the hour. I begin to understand why some, in misguided desperation, begin to consider shortcuts in their research…well, sort of, in my high anxiety moments. Writing about the process here encourages deeper processing, allowing me to extract more value and to more quickly articulate ideas as they emerge.

Data management lit review – an update
I am often surprised by the communication gap between academic and medical libraries, in particular around digital research data. For many reasons, medical libraries have been dealing with the challenge of digital research for about a decade. This is largely due to the more rapid adoption of emerging research technologies by medical/clinical researchers. The nature of their research and the money invested in biomedical research simply drives innovation more quickly than in other fields. Additionally, there is the major role that pharmaceutical and biotech industries play in driving innovation. One result of this is the widespread development and adoption of data management standards and processes, specifically in clinical data management. There is a wealth of knowledge about data management best practices in the Society for Clinical Data Management‘s (SCDM) Good Clinical Data Management Practices (GCDMP) guide, but few in academic seem to be aware of this knowledge base or interested in what we can learn from it. My perspective is that it fills a major knowledge gap within academic libraries – data management during the active project phase. Libraries do a great job of preserving information for long-term access, but our expertise is lacking in managing dynamic content. Largely, we have outsourced this to our vendors – databases, OPACs, etc. So, I’ve been gathering knowledge from sources like the SCDM GCDMP and the ICPSR Guide to Social Science Data Preparation and Archiving, systematically ranking them for application in the health and social sciences. It has been a laborious, but enlightening, process. My goal has been to select those strategies with the highest impact with minimal expertise and time required to implement them for use in the data management lab pilot.

By Heather L. Coates

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