## 2 Methods
### Case Study: Information Gap Analysis  
For the interviews, we standardized questions (Supplementary Materials S2) and used a standardized online data collection form to take notes as a precaution against subjective interviewer bias. For each interview, there was a lead interviewer and two note takers, one taking verbatim notes and the other synthesizing key points emphasized by the interviewee. The questions (Supplementary Materials S2) paralleled the structure to populate a Change Traceability Matrix (Supplemental Material S1). The online data collection form generated a shared spreadsheet with 36 columns: A through AJ. These columns were then cross-walked to the CTM to show where key information from explicit questions likely populate into the CTM (Figure 4). To populate the CTM, we then pulled key information from responses into synthesized statements relevant to each column of the CTM. We created a separate CTM per User Archetype. Each interview response was parsed into the appropriate User Archetype by creating a new row for each KPI starting left to right. Sometimes, one decision would have multiple KPIs as goals or objectives; in these cases, the decision descriptor was a merged cell spanning all associated KPIs. Similarly, any one KPI may rely on multiple types of information. A new row was added for each piece of needed information, and the KPI cell related to that decision was merged to span all needed information. While every effort was made to ask all questions, sometimes, interviewees answered questions not yet asked in  
Figure 3: Number of interviewees by user persona. Note that here, we show five personas when the analysis determined 8 personas because Emergency Responders, Land Management/Preparedness/Resilience Planning, and Public Information Officers were further partitioned into local and state/regional personas. response to earlier questions, and sometimes, interviewees simply did not understand or could not answer a question. In these cases, any questions for which participants did not provide details were left blank in the final CTM and may represent gaps in information from the interviews or and gaps in the system. Bolded text in the CTM represents key quotes from interviews that capture a recurring sentiment by interviewees within that persona. Both the verbatim and synthesis notes (each taken by a different note taker) were treated independently (as different data points) and analyzed through a double-blind process whereby the interviewee and the analyzer were not identified to either party. As information from the interview overlapped with existing synthesized information in the CTM, no new rows were created, but existing rows were edited to provide more clarity. All details specific to a company, agency, or organization were removed for anonymity. The CTM provides a synthesis capturing general methods, mechanisms, and patterns for a user persona and each key decision they make. Prior to interview synthesis, the CU Boulder researchers presented nine data analytics capabilities in development, which were used as potential solutions for further refinement. As part of the HCD process, the joint Deloitte-CU Boulder team spent several days qualitatively synthesizing the interviews, documenting individual pain-points and solution areas with sticky notes, and aggregating information into cross-cutting insights and themes. These insights were later validated through the CTM analysis. We then used HCD to gauge human desirability, business viability, and technical feasibility. We applied a desirability-viability-feasibility, or \"DVF\" framework [49], to the nine CU Boulder researcher data analytic capabilities, and we down-selected three that scored the highest. To assign scores, the integrated CU Boulder-Deloitte team discussed research insights to collaboratively arrive at desirability scores. The Deloitte team led the scoring for business viability, using market research and subject matter expertise from project advisors working in the field.