1. Inconsistent data formats: If the same data is recorded in different formats or styles, it can lead to data quality

issues.

2. Duplicate records: Duplicate records can cause confusion and lead to inaccurate insights and decisions.

3. Missing data: Missing data can lead to incomplete information and can impact the accuracy and reliability of

data-driven decisions.

4. Outdated data: Old data can be irrelevant and negatively impact data analysis and decision-making.

5. Invalid data: Data that doesn’t meet specific criteria or rules, such as incorrect phone numbers or email

addresses, can lead to data quality issues.

6. Inaccurate data: Incorrect or inaccurate data can negatively impact data analysis and decision-making.

7. Incomplete data: Data that is missing important information, such as dates or values, can lead to data quality

issues.

8. Inconsistent data definitions: Different departments or teams may use different definitions for the same data,

leading to confusion and data quality issues.

9. Unstructured data: Data that is not properly organized or structured can make it difficult to analyze and make

decisions based on it.

10. Lack of data governance: Without proper data governance, there may be a lack of control over data quality,

leading to data quality issues.

‍

Want to discover how AI can solve your data issues? Contact Cody Clements at cody@cognistx.com.

‍