Consider the following aspects of Data Quality. Indicate which is the dominant issue in each case below.

A few years ago your company acquired another company and merged summary financial data into a key database. The data looks complete but there are some peculiarities we can't explain.

1. Completeness / Uniqueness
2. Accuracy / Consistency
3. Conformance / Validity
4. Timeliness
5. Provenance

1 Completeness / Uniqueness : "Completeness" refers to how comprehensive the information is. When looking at data completeness, think about whether all of the data you need is available; you might need a customer's first and last name, but the middle initial may be optional. If information is incomplete, it might be unusable. Let's say you're sending a mailing out. You need a customer's last name to ensure the mail goes to the right address - without it, the data is incomplete.

2 Accuracy / Consistency : Accuracy is a crucial data quality characteristic because inaccurate information can cause significant problems with severe consequences. As the name implies, this data quality characteristic means that information is correct. To determine whether data is accurate or not, ask yourself if the information reflects a real-world situation

3 Conformance / Validity : Requirements governing data set the boundaries of this characteristic. For example, on surveys, items such as gender, ethnicity, and nationality are typically limited to a set of options and open answers are not permitted. Any answers other than these would not be considered valid or legitimate based on the survey's requirement. This is the case for most data and must be carefully considered when determining its quality. The people in each department in an organization understand what data is valid or not to them, so the requirements must be leveraged when evaluating data quality.

4 Timeliness : Timeliness, as the name implies, refers to how up to date information is. If it was gathered in the past hour, then it's timely - unless new information has come in that renders previous information useless. The timeliness of information is an important data quality characteristic, because information that isn't timely can lead to people making the wrong decisions. In turn, that costs organizations time, money, and reputational damage.

5 Provenance : Data provenance refers to records of the inputs, entities, systems, and processes that influence data of interest, providing a historical record of the data and its origins. As the data points or hops increases, the complexity of such representation becomes incomprehensible