Data is meticulously cleaned prior to consumption to ensure the highest value from your dataset.

At mytiki, we focus on making your data clean, clear, and ready for use. Our data cleaning systems are about delivering quality, ensuring your datasets are free from duplicates, inconsistencies, and format mismatches.

The major processes involved in data cleaning are listed below, though other minor processes also take place prior to data being eligible for use.

Deduplication

Keepin’ it unique.
This is all about nixing the extra copies in your dataset.


Tech Speak: If you've got multiple records with the { "user_id": "12345", "email": "[email protected]" }, we'll ensure there's only one instance in the final dataset.

Sanitization

Data's personal stylist. We make sure your data’s dressed appropriately by correcting formats and fixing inconsistencies.


Tech Speak: When you've got strings like 'CA', 'Ca', 'cali', 'California' in a state field, we normalize to a single preferred format, such as 'CA', across all records.

Standardization

Harmony in data form.
Here, we're not just ironing shirts; we're tailoring them. We convert various data expressions into a standardized format based on predefined schemas.


Tech Speak: Let's say demographic data uses a 1-5 scale for income brackets, and transaction data uses a 1-10 scale. We'll normalize these to a common scale so that when you query across datasets, you're actually comparing apples to apples.

Normalization

Linking data dots.
This is the step where we ensure that all data plays well together by scaling and encoding to ensure compatibility across systems.


Tech Speak: Consider you have receipt data with receipt_id, demographic data with customer_number, and transaction records with transaction_id.

All these are different identifiers from various datasets that can be connected to one another. We establish unified foreign keys across all datasets.

So, if receipt_id: A123 in one dataset, customer_number: 001B in another, and transaction_id: X456 in a third all refer to the same individual, we add to the receipt data the corresponding transaction_id. We add to the transaction record the corresponding customer_number. This is for seamless cross-reference.

Always Improving, Always Adapting

Data needs keep evolving, and so do our cleaning methods. We're continually adding new techniques to match these changes. If you have specific cleaning needs or want to discuss a particular method, just reach out.