Data Quality Quiz

Test your knowledge with nine data quality questions from SearchCRM.com.

How to take the quiz:
- This is a low-tech pencil and paper quiz. After reading the question, note your answer choice. There is an answer key at the end of the quiz, where you'll also find additional resources related to the correct answer.

1. Some organizations appoint this individual to be responsible for data management and data quality from a business perspective.
a. data analyst
b. data steward
c. information architect
d. data keeper

2. This is a data management practice that characterizes the content, quality, and structure of your data.
a. data enrichment
b. data caving
c. data refinement
d. data profiling

3. This term is used to define the process of consolidating and managing customer information from all available sources to create a single customer profile:
a. Customer Data Integration
b. Customer Relationship Management
c. data profiling
d. data consolidation
This is the term for the process of matching customers belonging to a group, usually identified by the same address.
a. householding
b. data association
c. customer data relating
d. data grouping
See the rest of our quiz topics
Answer Key:

1.) b. data steward
Barney Beal's article explains why you should "Put your faith in CRM's data stewards".

2.) d. data profiling
Read up on data profiling in this white paper: "Advanced data profiling and analysis technology".

3.) a. Customer Data Integration
SearchCRM.com has more information in their Featured Topic, Customer Data Integration

4.) c. raw data
Get a detailed definition of raw data from Whatis.com.

5.) b. data cleansing
Read up on the best ways to tackle your data cleansing chores: "Two options for data cleansing"

6.) a. householding
Find out more about householding and other customer matching techniques.

7.) b.
data enrichment
Ask SearchCRM.com's business intelligence expert William McKnight your question on data enrichment and other data quality issues.

8.) d. bring it in
Browse through our collection of stories from around the Web on data quality

9.) c. name misspelling
Get information on how to avoid some of today's data quality violations

10.) b. data preprocessing
For more information, see our tip about data preprocessing.

This was first published in July 2004

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