Data normalization is a step in data preprocessing that involves adjusting the values of data attributes to a common scale, without distorting differences in the ranges of values.
Enterprises have data in diverse formats and sources: databases, spreadsheets, documents, APIs, and more. These data sources may have different formats, units, and scales.
Normalized data is also compressed, meaning that your AI can ingest more substance and less fluff, which makes for improved performance.
For example, Wang, et. al. demonstrate that 2NF normalization can reduce a sales database by more than 79%.
Data normalization lays the foundation for balanced AI analysis, fair feature comparison, and overall improved model performance. Think of it like tuning instruments and adjusting microphones before a concert: skip it, and some elements will overpower others, distorting the overall output.
There are many different use cases for enterprise AI implementation, and most of them rely on the AI's ability to interpret data accurately and without bias. All features need to be standardized for maximum fairness in the AI's interpretation.
Data normalization is how we prepare your entire dataset, including numbers, text, and categorical data, to be interpreted equitably by the AI system. This includes scaling numerical features, standardizing text formats, and ensuring consistent terminology usage across your knowledge base.
At its core, data normalization is about creating a level playing field for all your data. It's like establishing a common language across your entire knowledge base.
Here are the "3 S's" of data normalization:
Here are our basic steps to effective data normalization:
If you’d like to explore how data normalization can drive efficiencies in your workplace, request a free consultation with Talbot West. We can discuss specific tools, implementations, and risk management strategies.
Here are ten examples of how data normalization ensures balanced and accurate AI analysis.
| Order ID | Product Name | Price ($) |
|---|---|---|
1 | Novelty keychain | 3.25 |
2 | Designer handbag | 2415.00 |
3 | Smartphone charger | 15.99 |
4 | Wireless earbuds | 129.99 |
5 | Smart watch | 299.00 |
6 | Laptop sleeve | 24.50 |
| Order ID | Product Name | Normalized Price |
|---|---|---|
1 | Novelty keychain | 0.0000 |
2 | Designer handbag | 1.0000 |
3 | Smartphone charger | 0.0053 |
4 | Wireless earbuds | 0.0525 |
5 | Smart watch | 0.1226 |
6 | Laptop sleeve | 0.0088 |
Need help with data normalization? Book a free consultation and let us show you how we can tackle your data preprocessing and document preparation challenges. Check out our services for a full rundown of what we offer.
Here’s an example of using min-max scaling and other methods to normalize data with a wide variance.
Original data:
| Order ID | Product name | Price ($) | Order date |
|---|---|---|---|
1 | Widget A | 2500.00 | 01/15/202 |
2 | Gadget B | 35.99 | 03/01/2023 |
3 | Widget A | 2400.50 | 2023-04-20 |
4 | Tool C | 150.75 | 05-10-202 |
Normalized data:
| Order ID | Product name | Price | Order date |
|---|---|---|---|
1 | Widget A | 1.00 | 2023-01-15 |
2 | Gadget B | 0.00 | 2023-03-01 |
3 | Widget A | 0.96 | 2023-04-20 |
4 | Tool C | 0.05 | 2023-05-10 |
Here are the normalization techniques we used to solve this:
Scaling numerical data:
Normalizing dates:
When preparing your data for AI uptake, normalization is a crucial step in the preprocessing stage. Anyone training an AI on their knowledge base or otherwise developing AI models should normalize data in the preparatory stages.
Not Only Structured Query Language (NoSQL) databases are designed to handle large volumes of unstructured or semi-structured data, offering flexibility, scalability, and high performance.
Unlike traditional relational databases, NoSQL databases do not rely on a fixed schema and can store data in varied formats such as documents, key-value pairs, wide-column stores, and graph structures. Because of this flexibility, NoSQL databases are not typically normalized. Instead of adhering to normalization rules to minimize redundancy, NoSQL databases use denormalization to optimize read and write performance.
Neither SQL nor NoSQL is universally faster; the performance depends on the specific requirements and characteristics of the application.
SQL databases excel in structured data environments with complex queries, while NoSQL databases offer superior performance and scalability for large, unstructured datasets and high-throughput scenarios.
The best normalization form depends on your specific needs:
For most practical purposes, achieving 3NF is sufficient and balances data integrity with efficiency. For highly complex systems, BCNF or higher normal forms might be necessary. In performance-critical applications, strategic denormalization might be employed to optimize read operations.
After data normalization, we may need to apply other methodologies to your knowledge base.
These include, but are not limited to, the following:
Once the entire data preprocessing task is completed, your knowledge base is ready for AI ingestion.
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