Data Cleansing

Question 1

What are the types of data sources mentioned?

Answer 1

• Technical
• Data structure - Databases - onPrem/Cloud/Legacy
• Records
• Reputational
• Limited
• Special
• Social data
• Public datasets
• Sourced media
• Transactional

These categories represent various origins and types of data used in analysis.

Question 2

What is data collection?

Answer 2

The process of gathering raw data from various sources for analysis and decision-making.

Data collection is essential for obtaining accurate information.

Question 3

Why is data collection important?

Answer 3

It provides accurate information needed for analysis, planning, and decision-making.

Accurate data is crucial for effective decision-making.

Question 4

What are common methods of data collection?

Answer 4

• Interviews
• Questionnaires
• Observations
• Surveys
• System records

These methods help gather data from various sources.

Question 5

What are primary data?

Answer 5

Data collected directly from the source for a specific purpose.

Primary data is often more reliable for specific research needs.

Question 6

What are secondary data?

Answer 6

Data already collected by others and reused for analysis.

Secondary data can save time and resources in research.

Question 7

What tools are used for data collection?

Answer 7

• Forms
• Surveys
• Mobile apps
• Sensors
• Databases
• Spreadsheets

These tools facilitate the gathering of data efficiently.

Question 8

What problems can occur during data collection?

Answer 8

• Incomplete data
• Inaccurate responses
• Bias
• Data duplication

These issues can compromise the quality of the collected data.

Question 9

What is data accuracy?

Answer 9

The degree to which data correctly represents real-world values.

High data accuracy is essential for reliable analysis.

Question 10

What is data validity?

Answer 10

It ensures data measures what it is intended to measure.

Valid data is crucial for drawing correct conclusions.

Question 11

What is data consistency?

Answer 11

Data is uniform and does not contradict across systems.

Consistent data is vital for maintaining integrity in datasets.

Question 12

What is data cleansing (data cleaning)?

Answer 12

The process of identifying and correcting or removing errors and inconsistencies in data.

Data cleansing is essential for improving data quality.

Question 13

Why is data cleansing important?

Answer 13

It improves data quality, accuracy, and reliability for analysis.

Clean data leads to better decision-making.

Question 14

What types of errors require data cleansing?

Answer 14

• Missing values
• Duplicate records
• Incorrect formats
• Outliers

Addressing these errors is crucial for data integrity.

Question 15

What is missing data?

Answer 15

Data fields with no recorded values.

Missing data can skew analysis results.

Question 16

How can missing data be handled?

Answer 16

• Deleting records
• Filling values manually
• Using averages or estimates

Proper handling of missing data is necessary for accurate analysis.

Question 17

What is duplicate data?

Answer 17

Repeated records representing the same data item.

Duplicate data can lead to inflated results and inaccuracies.

Question 18

How do you remove duplicate data?

Answer 18

By using unique identifiers and data-matching techniques.

Effective removal of duplicates is essential for data integrity.

Question 19

What is data standardization?

Answer 19

Converting data into a consistent format (e.g., date or text format).

Standardized data is easier to analyze and compare.

Question 20

What is data validation?

Answer 20

Checking data against rules to ensure correctness.

Validation helps maintain data quality.

Question 21

What is data normalization in cleansing?

Answer 21

Organizing data to reduce redundancy and improve consistency.

Normalization is key for efficient data management.

Question 22

How are data collection and data cleansing related?

Answer 22

Collected data must be cleansed to ensure it is accurate and usable.

The cleansing process is vital after data collection.

Question 23

What happens if data is not cleansed?

Answer 23

It can lead to incorrect analysis and poor decisions.

Unclean data can severely impact outcomes.

Question 24

When should data cleansing be done?

Answer 24

After data collection and regularly during data use.

Ongoing cleansing ensures data remains reliable.

Question 25

What tools are used for **data cleansing**?

Answer 25

* Excel * SQL * Python * R * Data management software ## Footnote These tools facilitate effective data cleansing.

Question 26

Give an example of **data cleansing**.

Answer 26

Removing duplicate customer records and correcting wrong phone number formats. ## Footnote This example illustrates practical data cleansing techniques.