This document discusses data cleaning, which is the process of preparing data for analysis by identifying and modifying incorrect, incomplete, or misleading information in datasets. It involves understanding the data and project goals, and ensuring the data meets quality criteria like validity, accuracy, completeness, consistency, and uniformity. The cleaning process may involve handling missing values, outliers, duplicates, harmonizing data formats, and more to get the data ready for its intended use or analysis.

1. Data Cleaning
What every Manager should learn in data analytics . . .
Lydia Gitonga
Project Management| Data for Social Good| Business Analytics

2. What is data cleaning?
Why data cleaning?
How?
When?
…………….

3. This is the process of preparing the
data for analysis. It involves identifying,
eliminating or modifying information
that is incorrect, incomplete or could
be misleading in your datasets.
What is Data Cleaning?

4. Data ready for use
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5. End Goal
• What is your goal of analyzing the
data? What is the project/ business
goal?
Understanding your data
• What does your data contain?
• How was the data collected?
• How was the data recorded?
Data Cleaning Guiding Principles

6. Validity
Accuracy
Completeness
Consistency
Uniformity
What is the data quality criteria that must be
attained?

7. The data must conform to some set rules and
constraints corresponding to the real world
Validity

8. The data must be correct
and accurate. Comparison
with third party information
or comparing with known
information can be used to
qualify your data.
Accuracy

9. Is the data complete?
Are all important measures
available?
Completeness

10. The data should not contradict itself.
There should be a reasonable degree of
uniformity across the set of measures
Consistency

11. The data measurements
should have the same
scale rates, conversion
rates etc.
Uniformity

12. Cleaning Process

13. Missing values
Outliers
Duplicates
Harmonization (or normalization) of data,
Formatting

14. Thank You!