An inspection report on a single supplied file, sorting its problems into completeness, accuracy and timeliness, with a count for each and a note on how each would bend the analysis. Searches like "busi 3014 week 4 assignment example", "busi3014 week 4 sample" and "busi 3014 week 4 example" land here.
What a finished BUSI 3014 Week 4 data audit looks like
The audit fills three to four pages, and one table carries its findings. Rows are the problems found; columns give the quality dimension, the field affected, an illustrative count, and the likely effect on the first-visit wait. Typical entries include about 90 requests with a blank logged date out of roughly 2,400 rows, around 40 rebooked jobs entered twice, a handful of visits dated before their own requests, and a stretch of weeks where technicians keyed every visit on Friday afternoon. Prose above the table introduces the three dimensions, crediting Richard Wang and Diane Strong with the view that data quality means fitness for the person using the data. Prose below states the cleaning rule applied to each problem and flags which rows were set aside rather than repaired. All counts carry an illustrative label.
How a BUSI 3014 Week 4 example is structured
The audit is organized by dimension rather than by column, because the same field can fail in two different ways. Completeness is checked first, counting blanks in the fields the measure depends on. Accuracy follows, covering duplicates and impossible values such as a visit that precedes its request. Timeliness comes third, and for this file it is the subtle one: records keyed days after the fact carry dates that are technically present and practically wrong. Each finding is then traced to its effect on the wait measure, which is the step that turns an inventory of flaws into analysis. Cleaning rules are stated after the effects are known, so each rule can be justified by what it protects. A confidence statement closes the audit: which conclusions the file can bear and which it cannot.
Three dimensions, named up front
Completeness, accuracy and timeliness are defined in a sentence each before any finding appears. Framing quality as fitness for the person using the data, rather than as tidiness, is what lets the audit judge a flaw by its effect on the measure instead of by how untidy it looks.
Every problem counted
Blank logged dates, duplicated rebookings, visits dated before their requests: each comes with an illustrative count and the share of the file it represents. A count lets a reader decide how worried to be, which a general remark about messy data never does.
Late entry treated as a flaw
Visits keyed in a Friday batch have dates, so they pass a completeness check, yet the dates are wrong by up to four days. Catching that pattern, often visible as a spike of Friday entries, is where a careful audit separates itself from a count of blank cells.
Each flaw traced to the measure
Duplicates shorten average wait because the second entry often records the rebooked date. Friday keying lengthens it. Stating the direction of each bias tells the owner whether the uncleaned figure flatters the company or condemns it, and by roughly how much.
Rules stated, removals disclosed
Duplicates are collapsed to the earliest request; impossible dates are set aside rather than guessed. The report states how many rows each rule touched, so a reader can see that the cleaned file is smaller and knows exactly why.
Where marks go in BUSI 3014 Week 4
A fixed share of the credit goes to coverage, and an audit that reports only blank cells has checked one dimension of three. Quantification is checked next, since a remark that some rows look duplicated gives the reader nothing to weigh. The heaviest band sits on consequence, meaning the link from each flaw to the measure it threatens, and papers listing problems without saying which way they push the wait figure forfeit much of it. Cleaning rules draw scrutiny too: deleting every incomplete row without comment is marked down, because the deleted rows may not be random. The confidence statement at the end is usually scored as its own element, and papers that close by declaring the file clean invite a hard question.
Get a BUSI 3014 Week 4 example written to your instructions
Upload the Week 4 file, the prompt and the rubric; an audit written against that exact file returns in 24 to 48 hours, with the opening sample free. Should your data belong to an employer, send its column list and row count instead, and a stand-in file with the same structure carries the analysis.
BUSI 3014 Week 4 questions, answered
What if the supplied file looks clean?
Look at timeliness and at values that are possible but unlikely, since those rarely show as blanks. A wait of zero days on hundreds of rows, or every visit logged at the same hour, suggests how the data was entered. If the file genuinely holds up, your audit says so with evidence of what was checked, which still earns the coverage marks.
Should problems be fixed or only reported?
Most prompts want both: the finding, then the rule applied to it. What instructors dislike is silent repair, where the analysis runs on altered data without saying what changed. State each rule and how many rows it affected, and set aside anything you cannot repair defensibly rather than inventing a replacement value.
Does the audit need a named framework?
It helps. Completeness, accuracy and timeliness are widely used quality dimensions, and citing a source for the idea that quality depends on the user's purpose gives the audit a principle for judging flaws. Some sections add consistency or validity. Use whatever set your course reading names, and apply each dimension to specific fields rather than defining them in the abstract.