LO 40.2: Explain how a firm can set expectations for its data quality and describe

LO 40.2: Explain how a firm can set expectations for its data quality and describe some key dimensions of data quality used in this process.
A fundamental step in managing risks due to flawed data would be to set user expectations for data quality and then establish criteria to monitor compliance w ith such expectations. In order to define and measure these expectations, they can be categorized into key dimensions of data quality. The important (but not complete) set of dimensions that characterize acceptable data include accuracy, completeness, consistency, reasonableness, currency, and uniqueness.
Accuracy
The concept of accuracy can be described as the degree to which data correctly reflects the real world object. Measurement of accuracy can occur by m anually comparing the data to an authoritative source of correct information for example, the temperature recorded in a thermometer compared to the real temperature.
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2018 Kaplan, Inc.
Topic 40 Cross Reference to GARP Assigned Reading – Tarantino and Cernauskas, Chapter 3
Completeness
Completeness refers to the extent to which the expected attributes of data are provided. There may be mandatory and optional aspects of completeness. For example, it may be mandatory to have a customers primary phone number, but if the secondary phone number (optional) is not available, then the data requirement for the phone number is still considered complete.
Note that although data may be complete, it may not necessarily be accurate. For example, customers may have moved and their mailing addresses may not have been updated yet.
Consistency
Consistency refers to reasonable comparison of values between multiple data sets. The concept of consistency is broad and could require that data values from each data set do not conflict (e.g., a bank account is closed but the statement still shows account activity) or that they meet certain pre-defined constraints.
Note that consistency does not necessarily imply accuracy.
There are three types of consistency: 1. Record level: consistency between one set of data values and another set within the same
record.
2. Cross-record level: consistency between one set of data values and another set in different
records.
3. Temporal level: consistency between one set of data values and another set within the
same record at different points in time.
Reasonableness
Reasonableness refers to conformity with consistency expectations. For example, the income statement value for interest expense should be consistent or within an acceptable range when compared to the corresponding balance sheet value for long-term debt.
Currency
Currency of data refers to the lifespan of data. In other words, is the data still considered relevant and useful, given that the passage of time will gradually render it less current and less correct? Measurement of currency would consist of determining the frequency in which the data needs to be updated, and determining whether the existing data is still up-to-date.
Uniqueness
Uniqueness of data is tied into the data error involving duplicate records. Uniqueness suggests that there can only be one data item within the data set. For example, within a
2018 Kaplan, Inc.
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Topic 40 Cross Reference to GARP Assigned Reading – Tarantino and Cernauskas, Chapter 3
client list, there should only be one Mr. Jack Lee with a date of birth of January 1, 1970 living at 1234 Anywhere Street in New York City.
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