| Course | MGMT 8525M Big Data Decision Making and Management |
|---|---|
| Week | Week 2 |
| Paper type | Data profile brief |
| Length | About 600 words, 5 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | PhD in Management |
| Updated | October 2026 |
Free sample paper for MGMT 8525M Week 2
A 311 File Records Who Called, Not What Broke: A Data Profile Brief Reconstructing How a Mid-Sized City's Service Requests Were Created, Renamed and Filtered, and Which Residents and Problems the File Could Never Contain
Student Name
Walden University
MGMT 8525M: Big Data Decision Making and Management
Instructor Name
Month Day, Year
A 311 File Records Who Called, Not What Broke: A Data Profile Brief Reconstructing How a Mid-Sized City's Service Requests Were Created, Renamed and Filtered, and Which Residents and Problems the File Could Never Contain
Purpose of the Profile
The public works department of Calder City, a composite city of about 310,000 people, wants to use its 311 service-request file from 2019 through 2025 to allocate next year's pothole repair crews by neighborhood. Before any analysis, this brief profiles the file: where its records came from, why they were created and what the file cannot contain.
The Profile Table
[after]
The rows follow the questions Gebru et al. (2021) proposed for datasheets: motivation, composition, collection process, preprocessing and recommended uses. Each answer comes from the vendor's documentation, the department's change log or interviews with two call center supervisors.
Table 1
Datasheet for the Calder City 311 Service Request File, 2019 to 2025
| Datasheet question | What the file shows | |
|---|---|
| Why was it created? | To route residents' requests to departments and track response times; not to measure city conditions | |
| What does a record represent? | One request by one resident or staff member, with category, location, channel and timestamps | |
| How were records collected? | Phone calls to an agent, a mobile app with photo upload and a web form; staff can also file requests | |
| What changed over time? | A vendor switch in March 2022 renamed 14 categories and merged two pothole codes into one | |
| What preprocessing exists? | Duplicate requests within 50 meters and 72 hours are merged into one record | |
| What uses does it suit? | Response-time management and workload planning for reported problems | |
Records, Not Conditions
A pothole appears in the file only when someone reports it. The file therefore records the intersection of road damage and residents' willingness and ability to report it. Kontokosta and Hong (2021), studying 311 data in a large American city, found that complaint behavior varied with neighborhoods' socioeconomic characteristics, so that allocating services by complaint counts could reinforce existing disparities. The Calder file shows the same pattern. Request density per road mile in the three highest-income neighborhoods is about 2.6 times that of the three lowest-income neighborhoods, while the city's 2024 pavement condition survey, conducted by contractors driving every street, rates the lower-income neighborhoods' roads as worse on average.
Coverage Against a Benchmark
Comparing the two sources neighborhood by neighborhood, the 311 file captures most defects on arterial roads, which many people drive and report, and far fewer on residential streets in neighborhoods with lower app adoption. The coverage gap is evidence about reporting habits, not about road conditions.
Missingness by Mechanism
Rubin (1976) separated three situations: gaps unrelated to anything, gaps explained by things the analyst can observe, and gaps that depend on the very value that is missing. Unreported potholes are not missing at random: the chance a defect is reported depends on traffic volume, neighborhood characteristics and app use, some observed and some not, and possibly on how severe the defect is. Analyses that treat the file as a census of potholes will be biased in a predictable direction.
Fitness Statement
The file is fit for managing response times and planning crews for reported requests. It is not fit, on its own, for allocating repair crews by need, because it measures reporting. The category merge in 2022 also prevents comparison of pothole counts before and after that date without recoding.
Questions the File Cannot Answer
Where are the worst roads? Which neighborhoods have the most unreported damage? Did conditions improve after 2022? Each needs the pavement survey or another independent measure.
References
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., III, & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92. https://doi.org/10.1145/3458723
Kontokosta, C. E., & Hong, B. (2021). Bias in smart city governance: How socio-spatial disparities in 311 complaint behavior impact the fairness of data-driven decisions. Sustainable Cities and Society, 64, Article 102503. https://doi.org/10.1016/j.scs.2020.102503
Rubin, D. B. (1976). Inference and missing data. Biometrika, 63(3), 581-592. https://doi.org/10.1093/biomet/63.3.581
MGMT 8525M Week 2 instructions, in plain terms
Week 2 asks you to profile one dataset you might use for a management decision, reconstructing where it came from before you analyze it. State the decision first, then build a datasheet-style table covering why the data were collected, what a record represents, how records were collected, what changed over time, what preprocessing was applied and what uses the data suit. Compare the file's coverage with an independent benchmark where one exists. Classify missing data by mechanism. Close with a statement of what the file is fit for and the questions it cannot answer. Four to six pages is typical, with the datasheet table carrying much of the weight and sources noted for each answer in it.
How this MGMT 8525M Week 2 example is built
The sample opens with the pothole allocation decision. A six-row datasheet records the file's purpose, record unit, intake channels, a category change during a vendor switch and a duplicate-merging rule, with sources noted beneath. A section argues that the file records reporting rather than road damage and cites research showing 311 complaint behavior varies with neighborhood characteristics. The coverage section compares requests with a contractor-run pavement survey. The missingness section explains why unreported potholes are not missing at random. A fitness statement says the file suits response-time management but not need-based allocation, and a final section lists three questions only the pavement survey can answer.
MGMT 8525M Week 2 rubric: what earns full marks
The datasheet and the coverage comparison carry the grade. Profiles that describe columns and data types without asking why the data exist score as documentation. Graders look for answers to the datasheet questions drawn from real sources, attention to changes over time, a benchmark comparison and a correct use of missing-data mechanisms. The strongest briefs state clearly what the file measures, here reporting rather than conditions, and draw the decision consequence. A fitness statement that names suitable and unsuitable uses shows judgment. The remaining points reward sourced datasheet answers, a readable table and correct citations. A profile that never states the decision it serves cannot judge fitness and loses marks.
MGMT 8525M Week 2 help: mistakes that cost marks
The common shortfall is profiling the columns rather than the history. Ask who created each record and why; the answer often reveals what the file cannot contain. Look for changes in definitions, vendors or forms over the period, since they break comparisons. Find an independent measure of the thing you care about, even a partial one, and compare. Think about why records might be missing, and whether the reason relates to the value you want to measure. Close by saying, in a sentence, which jobs the file can do. A tutor can help you find documentation for a public dataset, and the 311 brief above shows a complete profile. Vendor documentation and change logs are often the best sources for the datasheet.
Get a MGMT 8525M Week 2 example written to your instructions
What the desk needs: the Week 2 instructions and rubric, plus the dataset or its documentation if one was assigned. Within 24-48h a profiled brief is back, and nothing is owed for a first request. When the choice is open, a public dataset with a published data dictionary keeps every row of the profile checkable, which is why the sample uses one.
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- MGMT 8525M Week 11: Research Paper
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MGMT 8525M Week 2 questions, answered
Where can I find a free MGMT 8525M Week 2 sample paper?
Read the 311 data profile brief above, which is the complete Week 2 sample.
What is a datasheet for a dataset?
A structured record, proposed by Gebru and colleagues, of why data were collected, how, what they contain and what uses they suit.
Why can't 311 requests measure road conditions?
A pothole enters the file only when someone reports it, so the file reflects reporting habits as much as damage.
How does the MGMT 8525M Week 2 sample check coverage?
It compares request density by neighborhood with an independent pavement condition survey.
What is the file fit for, according to the brief?
Managing response times for reported problems, but not allocating repair crews by need on its own.