While this scenario describes a system integration challenge that can be solved with ETL or ELT, Angela is facing a Record linkage issue.

A Data mart is too narrow, because Taylor needs data from across multiple divisions.
OLAP is a broad term for analytical processing, and OLTP systems are transactional and not ideal for the task. Since Taylor is working with data across multiple different divisions, she will work with a Data warehouse.

This is because duplicate data refers to data that is repeated or copied in a data set, which can affect the quality and validity of the analysis. A DISTINCT function is a type of function that removes duplicate values from a column or a table, leaving only unique values. For example, a DISTINCT function in SQL that can achieve this is:

The other data cleansing issues will not be fixed by applying a DISTINCT function. Here is why:
Missing data refers to data that is absent or incomplete in a data set, which can affect the accuracy and reliability of the analysis. A DISTINCT function does not help with missing data, because it does not fill in or impute the missing values.
Redundant data refers to data that is unnecessary or irrelevant for the analysis, which can affect the efficiency and performance of the analysis. A DISTINCT function does not help with redundant data, because it does not remove or filter out the redundant values.
Invalid data refers to data that is incorrect or inaccurate in a data set, which can affect the validity and reliability of the analysis. A DISTINCT function does not help with invalid data, because it does not validate or correct the invalid values.

The measure of dispersion is used to describe the spread of data around a central value. In the context of a data set containing actual and projected sales, the measure of dispersion will help to understand the variability or consistency of sales figures. The variance is themost appropriate statistical method for finding the measure of dispersion because it calculates the average of the squared differences from the Mean, providing a clear picture of data spread. It is especially useful in comparing the spread between different data sets and understanding the distribution of data points.
* Mean is a measure of central tendency, not dispersion.
* Correlation measures the relationship between two variables, not the spread of a single variable.
* Confidence intervals are used to estimate the range within which a population parameter will fall, but they do not measure dispersion within the data set itself.
References:
* Measures of Dispersion in Statistics1
* Measures of Dispersion - Definition, Formulas, Examples2
* Statistical dispersion - Wikipedia3