Now that we've discussed the why and what of data transformation, cleaning, structuring, enriching, and standardizing, let's put these ideas into practice. This section provides hands-on exercises using a sample dataset to solidify your understanding of common transformation techniques. We won't be writing code here; the focus is on applying the logic you've learned.

Scenario: Preparing Customer Data

Imagine you've extracted the following customer data from a legacy system. The goal is to clean and standardize this data before loading it into a new marketing database.

Here's our raw data:

CustID	FullName	RegistrationDate	Email	State	Orders
101	Alice Smith	2022-05-15	alice.s@mail.com	CA	5
102	Bob Johnson	11/20/21	bob	ny	3
103		2023-01-10	charlie@mail.com	Texas	-2
104	Diana Lee	March 3, 2022	diana.lee@mail	ca	10
105	Evan Green	2022-08-01		FL	7
106	Fiona Adams	07/10/23	fiona.a@mail.com	Nevada	0
107	George Miller	2021-12-25	george@mail	CA	none

This data has several issues typical of raw extracts: missing values, inconsistent date formats, inconsistent state representations, invalid email formats, and potentially incorrect order counts. Our task is to apply transformations to fix these problems.

Activity 1: Data Cleaning

First, let's address the most obvious errors and missing information.

Missing Values: Identify rows with missing FullName and Email. How would you handle these? A common approach is to replace them with a default value like 'Unknown' or NULL, depending on the target system's requirements. Let's decide to use 'Unknown' for FullName and leave Email as potentially empty if it's truly missing (but we'll validate formats later).
Incorrect Values: Look at the Orders column. CustID 103 has -2 orders, which is likely an error. CustID 107 has 'none'. How should these be handled? We might decide that negative orders are invalid and should be set to 0 or flagged for investigation. Text values like 'none' should definitely be converted to a numerical representation, likely 0.

Applying the Cleaning:

CustID 103: Set FullName to 'Unknown'. Set Orders to 0.
CustID 105: Set Email to an empty string or NULL (let's represent as empty for now).
CustID 107: Set Orders to 0.

Our data now looks like this:

CustID	FullName	RegistrationDate	Email	State	Orders
101	Alice Smith	2022-05-15	alice.s@mail.com	CA	5
102	Bob Johnson	11/20/21	bob	ny	3
103	Unknown	2023-01-10	charlie@mail.com	Texas	0
104	Diana Lee	March 3, 2022	diana.lee@mail	ca	10
105	Evan Green	2022-08-01		FL	7
106	Fiona Adams	07/10/23	fiona.a@mail.com	Nevada	0
107	George Miller	2021-12-25	george@mail	CA	0

Activity 2: Data Validation and Standardization

Next, let's enforce consistency in formats and validate certain fields.

Email Validation: Examine the Email column. CustID 102 ('bob') and CustID 104/107 ('diana.lee@mail', 'george@mail') seem incomplete or invalid. A simple validation rule might check for the presence of an '@' symbol and a period '.' after the '@'. Emails failing this check could be flagged or set to a default invalid marker. Let's assume we flag them for review or set them to empty/NULL if they fail basic validation.
Date Standardization: The RegistrationDate column uses multiple formats ('YYYY-MM-DD', 'MM/DD/YY', 'Month Day, YYYY'). We need to standardize this, perhaps to the 'YYYY-MM-DD' format.
- '11/20/21' becomes '2021-11-20'
- 'March 3, 2022' becomes '2022-03-03'
- '07/10/23' becomes '2023-07-10'
State Standardization: The State column has 'CA', 'ny', 'Texas', 'ca', 'FL', 'Nevada'. We need to standardize this to a consistent format, like the 2-letter uppercase abbreviation. We'll need a mapping (lookup) for full names or lowercase versions.
- 'ny' becomes 'NY'
- 'Texas' becomes 'TX'
- 'ca' becomes 'CA'
- 'Nevada' becomes 'NV'

Applying Validation and Standardization:

CustID 102: Email 'bob' fails validation. Set to empty. Date becomes '2021-11-20'. State 'ny' becomes 'NY'.
CustID 104: Email 'diana.lee@mail' fails validation (no period after @). Set to empty. Date becomes '2022-03-03'. State 'ca' becomes 'CA'.
CustID 103: State 'Texas' becomes 'TX'.
CustID 106: Date '07/10/23' becomes '2023-07-10'. State 'Nevada' becomes 'NV'.
CustID 107: Email 'george@mail' fails validation. Set to empty.

The data after this stage:

CustID	FullName	RegistrationDate	Email	State	Orders
101	Alice Smith	2022-05-15	alice.s@mail.com	CA	5
102	Bob Johnson	2021-11-20		NY	3
103	Unknown	2023-01-10	charlie@mail.com	TX	0
104	Diana Lee	2022-03-03		CA	10
105	Evan Green	2022-08-01		FL	7
106	Fiona Adams	2023-07-10	fiona.a@mail.com	NV	0
107	George Miller	2021-12-25		CA	0

Activity 3: Data Structuring and Enrichment

Finally, let's restructure the data slightly to better fit our target system's schema. Assume the target database requires separate FirstName and LastName fields instead of FullName.

Splitting Fields: We need to split the FullName column into FirstName and LastName. We can split the string at the first space. For 'Alice Smith', FirstName is 'Alice' and LastName is 'Smith'. What about 'Unknown'? We can set both FirstName and LastName to 'Unknown'.
Adding Fields (Enrichment): Let's add a simple derived field. Suppose we want to categorize customers based on their registration year. We can extract the year from the standardized RegistrationDate and create a new RegistrationYear column.

Applying Structuring and Enrichment:

Split FullName into FirstName and LastName.
Create RegistrationYear from RegistrationDate.

The final transformed data, ready for loading:

CustID	FirstName	LastName	RegistrationDate	RegistrationYear	Email	State	Orders
101	Alice	Smith	2022-05-15	2022	alice.s@mail.com	CA	5
102	Bob	Johnson	2021-11-20	2021		NY	3
103	Unknown	Unknown	2023-01-10	2023	charlie@mail.com	TX	0
104	Diana	Lee	2022-03-03	2022		CA	10
105	Evan	Green	2022-08-01	2022		FL	7
106	Fiona	Adams	2023-07-10	2023	fiona.a@mail.com	NV	0
107	George	Miller	2021-12-25	2021		CA	0

Summary

Through these activities, we simulated several fundamental transformation steps:

Cleaning: Handled missing values and corrected obviously incorrect data.
Validation: Checked email formats against a simple rule.
Standardization: Converted dates and state names to consistent formats.
Structuring: Split a single field into multiple fields.
Enrichment: Derived a new field (RegistrationYear) from existing data.

This transformed dataset is now much more consistent, reliable, and structured appropriately for loading into the target marketing database. While this example used simple rules and a small dataset, the principles apply to larger, more complex transformation tasks encountered in real-world ETL processes. The key is to understand the target requirements and apply the necessary cleaning, validation, standardization, structuring, and enrichment steps systematically.