It's no exaggeration to say that everything is in some way an information management problem. Managing small amounts of data is relatively easy and can be (and often is) done in an ad hoc manner. But large-scale data management is a different thing altogether. At scale, our information management procedures can empower us or imprison us. The deluge of data that health agencies had to deal with during the COVID-19 pandemic is just the most recent example. At scale, information management processes and procedures must be effective, efficient, consistent, predictable, and documented. Designing and maintaining such procedures comprised the bulk of my work during the pandemic.

Statisticians have a saying: "Get there before anyone collects any data." Time invested up front discussing what you want to learn from your data is the key to a successful and useful analysis.

"What are you counting and how are you counting it?" This is the fundamental question in all data analysis. Sensible interpretation of any analytical output depends entirely on understanding this "data-generating mechanism."

Of course! This will include, as appropriate to the problem:

• a thorough description of data sources and analytical methods
• good graphical displays and descriptive statistics to summarize your data
• hypothesis testing and linear or nonlinear modeling
• quantitative spatial analysis
• a thorough yet comprehensible explanation of the findings
• a discussion of potential limitations of the analysis, and where you should be careful in its application.

Grant applications often require a plan for quantitative assessment of the program's results. I can help craft the analysis plan as part of a strong application and then conduct the analysis during and after your initiative.