20 Data Quality Interview Questions and Answers

Data quality is an important skill for any business professional. In an increasingly data-driven world, companies are looking for employees who can help them make sense of the vast amounts of information they collect. As a result, data quality has become an important job skill.

If you’re interviewing for a position that involves working with data, you can expect to be asked questions about your data quality experience and skills. In this article, we’ll share some of the most common data quality interview questions and provide tips on how to answer them.

Data Quality Interview Questions and Answers

Here are 20 commonly asked Data Quality interview questions and answers to prepare you for your interview:

1. What is data quality?

Data quality is a measure of how accurate and consistent data is. Data can be of poor quality for a number of reasons, including incorrect or missing values, incorrect data formats, and duplicate data. Data quality is important because it can impact the accuracy of results from data analysis.

2. Can you explain how to ensure data quality in a project?

There are a few key ways to ensure data quality in a project:

1. Make sure that data is complete – all required fields should be filled in, and no extra fields should be included.
2. Make sure that data is accurate – all values should be correct, and there should be no invalid entries.
3. Make sure that data is consistent – all data should be formatted in the same way, and no conflicting information should be included.
4. Make sure that data is timely – all data should be up-to-date, and no outdated information should be included.

3. What’s the difference between data validation and data cleansing?

Data validation is the process of ensuring that data is accurate, complete, and consistent. Data cleansing is the process of identifying and correcting errors and inconsistencies in data.

4. What are some of the different types of data errors that can occur during analysis?

Data errors can occur during analysis for a variety of reasons. One common reason is incorrect data entry. This can happen if data is entered into a system incorrectly, or if it is transferred from one system to another incorrectly. Another common reason for data errors is incorrect data interpretation. This can happen if data is not properly understood, or if it is not properly formatted for analysis.

5. Can you give me an example of what you understand about dirty data?

Dirty data is data that is incorrect, incomplete, or otherwise not up to standard. This can happen for a number of reasons, such as human error, system error, or simply because the data is old and no longer accurate. Whatever the reason, dirty data can cause problems downstream if it is not cleaned up.

6. How do you go about identifying dirty data?

There are a few different ways to identify dirty data. One way is to look for data that is incomplete, inaccurate, or duplicated. Another way is to look for data that is formatted incorrectly or is in the wrong data type. Finally, you can also look for data that is outliers or that doesn’t make sense in the context of the rest of the data.

7. Why is it important to consider data duplication when working with data?

Data duplication can introduce errors into your data set, and can also make it more difficult to work with the data. For example, if you have two copies of the same data, and one copy is updated, you then need to update the other copy as well. This can be time-consuming and error-prone. Additionally, data duplication can take up extra storage space, which can be costly.

8. What do you understand about missing values, nulls, and blanks?

There are three main types of data quality issues: missing values, nulls, and blanks. Missing values are when data is simply not available, nulls are when data is available but not applicable, and blanks are when data is available but not filled in. All three of these can cause problems when trying to analyze data, so it is important to be aware of them and how to deal with them.

9. How would you handle duplicate records while working with data sets?

One way to handle duplicate records is to simply ignore them when they are encountered. Another way to handle duplicates is to keep track of them and report them to the user.

10. What do you understand by outliers and anomalies?

Outliers are data points that are far from the rest of the data, while anomalies are data points that do not conform to the expected pattern. Outliers can be caused by errors in data entry, while anomalies may indicate a problem with the underlying process that generated the data.

11. Can you give me examples of anomalous data?

Anomalous data is data that does not fit the expected pattern. This can be due to errors in data entry, incorrect data, or simply data that is out of the ordinary. Examples of anomalous data include values that are far outside the normal range, values that are duplicates of other values, and values that are missing entirely.

12. What steps can be taken to improve the accuracy of data?

There are a few steps that can be taken to improve the accuracy of data:

-Ensure that data is entered correctly in the first place by implementing data entry guidelines and procedures
-Regularly check and clean data to remove any errors or inaccuracies
-Use data validation techniques to verify the accuracy of data
-Store data in a central location where it can be easily accessed and updated

13. What is sampling? How does it help in data analysis?

Sampling is the process of selecting a representative subset of a larger population. This can be done in a number of ways, but the most common is simple random sampling, where each unit in the population has an equal chance of being selected. Sampling can be used in data analysis to help reduce the amount of data that needs to be processed, making it more manageable. It can also help to improve the accuracy of results by providing a more representative sample of the population.

14. What kinds of data should not be included in your final results?

There are a few different types of data that should not be included in your final results. This includes data that is inaccurate, incomplete, or irrelevant. Inaccurate data can lead to incorrect conclusions, while incomplete data can make it difficult to draw any conclusions at all. Irrelevant data is simply data that is not related to the question you are trying to answer and can therefore be safely ignored.

15. Is there any way to tell if data is trustworthy or not? If yes, then how?

There are a few ways to tell if data is trustworthy. One way is to check the source of the data to see if it is reliable. Another way is to look at the data itself to see if it is consistent and accurate.

16. In which situations is it recommended to discard data from a dataset?

There are a few situations where it might be recommended to discard data from a dataset. One is if the data is corrupted or otherwise unusable. Another is if the data is not relevant to the task at hand. Finally, if the data is not representative of the population as a whole, it might be discarded in favor of a more representative dataset.

17. What is the difference between a primary key and a foreign key?

A primary key is a unique identifier for a given record in a database table, while a foreign key is a field in a database table that matches the primary key of another table. A foreign key can be used to create a relationship between two database tables, allowing data from both tables to be linked together.

18. When should you use a surrogate key instead of a natural key?

A surrogate key is a key that is used to uniquely identify a record in a database, but which has no inherent meaning. A natural key, on the other hand, is a key that is used to uniquely identify a record in a database and which has some inherent meaning.

There are a few reasons why you might choose to use a surrogate key instead of a natural key. First, surrogate keys are often easier to generate and manage than natural keys. Second, surrogate keys can be changed without affecting the meaning of the data, while natural keys cannot. Finally, surrogate keys can be used to link data from multiple tables together, while natural keys can only be used to link data within a single table.

19. What are some common problems associated with using surrogate keys?

Surrogate keys can cause problems if they are not managed properly. For example, if a surrogate key is used to identify a customer and that customer changes their name, the surrogate key will not be updated to reflect the change. This can lead to confusion and data quality issues. Additionally, if a surrogate key is used to identify a record in a database and that record is deleted, the surrogate key can be reused for a new record, which can also lead to data quality issues.

20. Can you list out some practices that make data more reliable?

There are a few practices that can make data more reliable:

-Using data from multiple sources can help to verify accuracy
-Cleaning and standardizing data can help to reduce errors
-Tracking changes to data over time can help to identify errors
-Using data validation techniques can help to identify errors
-Testing data against known values can help to identify errors