In your responses to peers, consider how the data your peers presented would be helpful in your discipline. Make suggestions as to how the data presented in the original post might be used in another way.

In the MSNBC documentary One Nation, Overdosed: Documentary On The Deadliest Drug Crisis In American History, Jacob Soboroff (2017) presents a range of descriptive statistics that illuminate the scope and severity of the opioid epidemic in Dayton, Ohio. Two clear examples of data levels in the film are "percentage of overdose fatalities by drug type" (ratio level) and "county classification as urban, suburban, or rural" (nominal level). Examining these statistics not only clarifies the crisis in Dayton but also suggests how professionals in data analytics and technology might leverage similar measures to inform policy, allocate resources, and optimize interventions.

First, Soboroff reports that in 2017, opioids accounted for approximately 68 percent of all overdose deaths in the United States, up 9.8 percent from the prior year (MSNBC 2017). These figures, derived from counts of fatalities and expressed as percentages, are ratiolevel data because they have a meaningful zero point (zero deaths equals no fatalities) and support arithmetic operations such as addition, subtraction, and ratio comparisons. By noting that one county's opioid mortality rate was 30 deaths per 100,000 residents compared to a neighboring county's 15 per 100,000, analysts gain clear, actionable insight into geographic disparities. In a business analytics context, ratio data like overdose rates can guide where to focus investments in treatment centers, outreach programs, or emergencyresponse capacity. For instance, a data analyst might build a dashboard that ranks counties by overdose ratio and automatically triggers alerts when rates exceed predetermined thresholds.

Second, the documentary distinguishes counties according to their landuse classification, urban, suburban, or rural, when discussing access to naloxone kits and behavioral health services (MSNBC 2017). These categories are nominallevel data: they label distinct groups without any inherent ranking or measurable distance between them. Nevertheless, classifying areas this way uncovers patterns, such as the disproportionate death toll in rural counties where treatment resources are scarce. From a technology perspective, linking nominals like "rural" or "urban" to service availability can power geospatial analyses and mobile health applications. A business technologist might integrate opensource mapping APIs with overdose statistics to visualize "opioid deserts," prompting mobile clinics to prioritize regions identified as rural and underresourced.

Together, ratio and nominal data shape a multifaceted portrait of Dayton's opioid emergency. Ratio figures quantify the magnitude and growth of overdose fatalities, while nominal categories contextualize those figures against community characteristics. In my discipline of data analytics and technology, these descriptive statistics underscore the importance of selecting the appropriate level of measurement, designing dashboards that juxtapose quantitative ratios with categorical labels, and ultimately translating raw numbers into evidencedriven strategies. By applying similar descriptive analyses, organizationsfrom public health agencies to nonprofit coalitions- can better anticipate needs, optimize resource allocations, and measure the impact of interventions over time.