The Good, The Bad, and The Ugly — Learn free

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Questions 1–10 of 10

1. In the Good, Bad and Ugly view of data products, what is described as the real challenge, rather than defining what a data product is?
Question 1A. Understanding a data product's qualityB. Agreeing an industry-wide taxonomy of data productsC. Choosing the right storage technology for data productsD. Counting how many data products an organisation ownsE. Deciding which team should build dashboards
2. According to the Good, Bad and Ugly definition, which three traits does an output need to count as a data product?
Question 2A. It is stored in the cloud, is real-time and is machine-learning basedB. It delivers business value, offsets the cost of inaction (COI) and can be reused by multiple stakeholdersC. It has a dashboard, an API and a documented schemaD. It is built by IT, approved by legal and certified by governanceE. It is large, structured and refreshed daily
3. In the juice analogy for data products, why is juice made at home for you NOT considered a product?
Question 3A. Because home-made juice has fewer ingredients than bottled juiceB. Because it is less nutritious than store-bought juiceC. Because it is made only once for you, is not designed for consistent reproduction or sale, and has no brand or owner accountable for its qualityD. Because it has not been tested by a laboratoryE. Because it is free of charge
4. In the juice analogy, an unlabelled local juice is still a product. What does the buyer's decision to purchase it depend on?
Question 4A. The colour of the bottle and the price aloneB. Whether the juice has been advertised on televisionC. Whether the juice is organicD. Trust in the producer, trust in the seller and the buyer's contextE. Whether a data steward has approved it
5. Which of the following is NOT one of the five dimensions used to judge whether a data product is Good, Bad or Ugly?
Question 5A. OwnershipB. StandardsC. Service LevelsD. GovernanceE. Programming language
6. How is an Ugly data product characterised?
Question 6A. A siloed, fragile, non-compliant solution that is still valuable but high-riskB. A product that delivers no business value at allC. A trusted, reliable, compliant and scalable productD. A product that follows all standards but is rarely usedE. A product that has been formally decommissioned
7. In the Good, Bad and Ugly case studies, which example is classified as Ugly?
Question 7A. A retailer's governed, versioned sales and inventory datasets feeding APIs and dashboardsB. A machine learning model built by a business-unit data scientist from local extracts, outside governance, with no lineage or testingC. A marketing dashboard that relies on manually refreshed spreadsheetsD. An executive dashboard certified by the data governance teamE. A finance report that meets all of its SLAs
8. The marketing campaign report in the Good, Bad and Ugly case studies drives decisions but relies on manually refreshed spreadsheets and lacks quality checks. How is it classified?
Question 8A. GoodB. UglyC. BadD. Not a data product at allE. Decommissioned
9. Using the seven-point Data Product Quality Checklist, what does a 'Yes' on three to five of the dimensions typically indicate?
Question 9A. A Good productB. A product that should be deletedC. A product that is not yet a data productD. A Bad productE. An Ugly product requiring urgent remediation
10. What does a data product's classification as Good, Bad or Ugly signal?
Question 10A. Whether the product is legally allowed to existB. How many users the product hasC. Whether the product should be rebuilt in a different technologyD. The size of the budget the product consumedE. How urgently the product needs improvement, not whether it should exist