The document discusses data quality management, emphasizing its importance for maintaining high-quality information crucial for making reliable business decisions. It identifies major causes of poor data quality, such as manual entry and system upgrades, and outlines essential factors for ensuring clean data, including completeness and accuracy. Additionally, it highlights the need for tools and processes to clean and manage data effectively.

1. DATA QUALITY
MANAGEMENT
Presented by,
Chandana Maya S
2147014

2. AGENDA
 Data quality management.
 Why do you need data quality management?
 Major causes of poor data quality.
 Essential factors for clean data.
 How to maintain clean data?
 Best data quality tools.

3. Data Quality Management
 Data quality management consists of the processes and practices of constantly
maintaining a high quality of information.
 Data quality management includes the process of identifying poor-quality
data, cleaning it, and making it usable with your business intelligence
platforms.

4. Why do you need data quality management?
 Data enters an organization in various ways, so not all the data is accurate and
perfect. It may be outdated, duplicated, or inconsistent.
If it is not accurate and consistent, you cannot use it to make important
decisions.
Making business decisions based on incorrect and unreliable data could cost
you a fortune.
 Data quality management helps you find the poor-quality
data and detect how it is coming into your database.
 Then you can clean that data and prevent more from entering
your database.

5. Major causes of poor data quality
Manual entry
Acquisition and mergers
Real-time updates
Indiscriminate data collection
System upgrades

6. Essential factors for clean data
Completeness

7. Accuracy

8. Consistency

9. Timeliness

10. Integrity