Data Cleaning & Structuring

Collected or supplied datasets can arrive with duplicate records, inconsistent formatting, missing values, mismatched field structures, or other inconsistencies. Before that information is useful, it needs to be cleaned and organized.

This service focuses on that step: taking a dataset as it exists and processing it into the structure a project actually needs, according to the rules defined for that project.

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Depending on the dataset and the project’s requirements, cleaning can address issues such as:

• Duplicate records
• Inconsistent formatting
• Missing or incomplete values
• Inconsistent field values
• Unwanted characters or formatting
• Inconsistent naming or casing
• Other data-quality issues defined for the project

Which of these apply depends entirely on the condition of the dataset being processed.

Cleaning removes the noise; structuring gives the remaining data a shape the project can actually use. A clean dataset still needs a defined structure before it’s ready to use. Depending on the requirement, this can include:

• Consistent columns and fields
• Normalized values
• Defined record structures
• Organized datasets
• Database-ready structures, where supported by the project

The work generally moves through the same stages, adapted to the dataset and the project’s requirements:

1. Dataset Receipt
2. Field Definition
3. Cleaning
4. Normalization
5. Structuring
6. Validation
7. Data Delivery

The dataset going into this process doesn’t have to come from one place. It may originate from:

• Web data collection
• An existing dataset you already hold
• Files supplied directly by the client
• Other sources defined for the project

The cleaning and structuring rules are applied to the dataset regardless of where it originated.

Once processed, the result is a dataset defined by:

• Consistent fields
• Organized records
• Cleaned values
• A defined structure
• The output format required for the project

Common delivery formats include CSV, Excel, JSON, API, or database-ready output, depending on what the project calls for.

A single dataset is cleaned, normalized, and structured once. This fits an existing dataset that simply needs to be put right before use, such as:

• Cleaning an existing dataset
• Duplicate removal
• Field normalization
• Formatting cleanup
• Restructuring a supplied dataset

Where a dataset is refreshed on an ongoing basis, the same cleaning and structuring rules can be applied to each new batch, so the fields stay consistent from one round to the next.

The diagram below illustrates that transformation: information starts out scattered and inconsistent, and moves through defined cleaning and structuring steps until it reaches a structure ready for the format your business needs.

This service tends to fit situations where the data already exists but isn’t yet usable, such as:

• A dataset with duplicate records
• A dataset with inconsistent formats
• Data collected from multiple sources that don’t line up
• A supplied dataset that needs normalization
• A dataset that needs to be reshaped into a defined output structure

Where the data doesn’t exist yet and needs to be collected first, that falls under a collection project rather than this service.

Before scoping the work, it helps to define:

• The source dataset
• The fields it contains
• Required cleaning rules
• Normalization requirements
• The output structure needed
• The delivery format required

Once these are defined, we can review the dataset and confirm an approach for processing it.

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