Data Scrubbing,Cleansing andCleaning — Learn free

Self-check

Questions 1–10 of 10

1. Who can typically perform Data Cleaning, and why?
Question 1A. Only database administrators, because it needs root accessB. Only external vendors, because it needs special toolsC. Only data scientists, because it needs machine learningD. Business users themselves, because it requires little effort such as exporting data to Excel, cleaning it and sending it downstreamE. Nobody, because cleaning happens automatically
2. Who is ideally placed to perform Data Cleansing?
Question 2A. Business users using ExcelB. IT subject-matter experts writing SQL queries at the database levelC. Customers correcting their own recordsD. The marketing team using a surveyE. A regulator conducting an audit
3. Which activity is described as the best level for achieving the highest Data Quality?
Question 3A. Data CleaningB. Data CleansingC. Data EnrichmentD. Data SplittingE. Data Scrubbing
4. In the face-care analogy used to explain the three levels of data cleaning, which situation corresponds to Data Scrubbing?
Question 4A. Going to a proper parlour with the best worker and creams before a wedding or valuable eventB. Wiping paint splashes off your face while paintingC. Washing dust off your face after a walkD. Applying face cream for ten to fifteen minutes after a football matchE. Cleaning your face after eating
5. Which of the following is NOT given as a reason to conduct Data Scrubbing?
Question 5A. Wrong data entryB. Two or more databases being mergedC. The data having just been validated by an automated toolD. Few or no standards defined in the systemE. Very old data that is not yet obsolete
6. What happens to corrupt and bad data during a Data Scrubbing exercise?
Question 6A. It is permanently deleted without recordB. It is sent to customers for correctionC. It is left in place but flagged in a reportD. It is removed from the mainstream of data and parked separately for investigationE. It is merged with good data to fill gaps
7. According to the statistics cited in the discussion of Data Scrubbing, businesses lose up to what share of their revenue because of bad data quality?
Question 7A. 5%B. 20%C. 50%D. 75%E. 90%
8. How much of their productive time do employees reportedly waste dealing with routine data quality tasks?
Question 8A. Up to halfB. Up to one tenthC. Up to one quarterD. Almost all of itE. None, because tools handle it
9. According to the survey cited in the discussion of Data Scrubbing, what happens in any given hour of the day?
Question 9A. Every company changes its addressB. About 500 businesses closeC. No business names changeD. Fewer than five new businesses openE. Almost five dozen companies change address, nearly a dozen change name, and over 40 new businesses open
10. In Data Scrubbing, across which range of systems should the findings be implemented?
Question 10A. Only in the reporting layerB. Only in the spreadsheet where problems were foundC. From source systems through to downstream systemsD. Only in the staging areaE. Only in third-party systems