Data integrity is the accuracy, completeness, consistency, and trustworthiness of data throughout its life cycle.
Data replication caused the compromise. Data replication is the process of storing data in multiple locations. If not done properly, replication can compromise integrity and cause inconsistencies.
Based on the available data, the analyst would need more data to determine the reasons behind the population increase.
Row 9 is a duplicate of row 8. Duplicate data is a limitation because it will lead to faulty analysis.
This example describes insufficient data that keeps updating. If a dataset keeps updating, that means the data is still incoming and might be incomplete.
A sample relates to a population by representing a population at a smaller scale.
Sampling bias in data collection happens when a sample isn’t representative of the population as a whole.
If a data analyst believes the business objective should be adjusted, it’s important to first have a discussion with stakeholders.
The analyst would need more data to identify the reason for a population increase.
This example describes data that is insufficient because it’s geographically limited. If the analytics project has a global focus, the dataset should also be global.
The restaurant should give samples to all diners.
Accuracy and completeness are necessary to ensure data integrity.
The analyst could use the dataset to find the average population of a certain country from 2015 through 2020 and the difference in population between two specific countries in 2018.
This example describes outdated data, which is insufficient. If a dataset is outdated, that means the data is old and probably no longer relevant.
If a data analyst is using data that has been compromised, the data will lack integrity and the analysis will be faulty.
Data transfer caused the compromise. When a data transfer is interrupted, it can result in an incomplete dataset.
Rows 10 and 11 do not contain duplicate data.
This scenario describes sampling bias because parties of six or more are not representative of the population as a whole.
Maintaining data integrity helps ensure a close alignment of data and business objectives because the data is likely to be accurate, complete, consistent, and trustworthy.
Using 100% of a population is ideal, but it can be very expensive to gather data from an entire population.
Insufficient data and sampling bias can prevent alignment.
Fill in the blank: Data _____ refers to the accuracy, completeness, consistency, and trustworthiness of data throughout its life cycle.
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